The identified risks.
\r\n\tThis book intends to cover major mineral deficiency problems such as calcium, iron, magnesium, sodium, potassium and zinc. These minerals have very important task either on intracellular or extracellular level as well as regulatory functions in maintaining body homeostasis.
\r\n\r\n\t
\r\n\tBoth macrominerals and trace minerals (microminerals) are equally important, but trace minerals are needed in smaller amounts than major minerals. The measurements of these minerals quite differ. Mineral levels depend on their uptake, metabolism, consumption, absorption, lifestyle, medical drug therapies, physical activities etc.
\r\n\tAs a self-contained collection of scholarly papers, the book will target an audience of practicing researchers, academics, PhD students and other scientists. Since it will be published as an Open Access publication, it will allow unrestricted online access to chapters with no reading or subscription fees.
",isbn:"978-1-83881-085-6",printIsbn:"978-1-83881-081-8",pdfIsbn:"978-1-83881-086-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8bc7bd085801296d26c5ea58a7154de3",bookSignature:"Dr. Gyula Mozsik and Dr. Gonzalo Díaz-Soto",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8935.jpg",keywords:"Calcium, Iron, Magnesium, Potassium, Sodium, Zinc, Diagnostic tools, Treatments, Food Fortification, Malnutrition, Metabolic Disorders, Lifestyle",numberOfDownloads:741,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 26th 2020",dateEndSecondStepPublish:"June 16th 2020",dateEndThirdStepPublish:"August 15th 2020",dateEndFourthStepPublish:"November 3rd 2020",dateEndFifthStepPublish:"January 2nd 2021",remainingDaysToSecondStep:"9 months",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus of Medicine at Univesity of Pecs, Hungary, and recipient of Andre Roberts award from the International Union of Pharmacology in 2014. He published 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, and edited 32 books.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"58390",title:"Dr.",name:"Gyula",middleName:null,surname:"Mozsik",slug:"gyula-mozsik",fullName:"Gyula Mozsik",profilePictureURL:"https://mts.intechopen.com/storage/users/58390/images/system/58390.jpg",biography:"Gyula Mózsik, MD,PhD, ScD(med) is a professor emeritus of medicine at First Department of Medicine, Univesity of Pécs, Hungary. He was head of the Department from 1993 to 2003. His specializations are medicine, gastroenterology, clinical pharmacology, clinical nutrition. His research fields are biochemical and molecular pharmacological studies in gastrointestinal tract, clinical pharmacological and clinical nutritional studies, clinical genetic studies, and innovative pharmacological and nutritional (dietetical) research in new drug production and food production. He published around 360 peer-reviewed papers, 196 book chapters, 692 abstracts, 19 monographs, 32 edited books. He organized 38 national and international (in Croatia ,France, Romania, Italy, U.S.A., Japan) congresses /Symposia. He received the Andre Robert’s award from the International Union of Pharmacology, Gastrointestinal Section (2014). 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\nRisk management is defined as the process that is able to find the risks and analyze these risks using a suitable method and then put the appropriate response to eliminate those risks or reduce them, thereby increasing the success of the project and the achievement of its goals [2].
\nRisk management is also defined as the process that enables the analysis and management of risks related to the project and its aim is to reduce the risk that threatens the goal of the project and hence it takes the responsibility of increasing the opportunity for the competition of the project in time, cost, and quality [3]. Risk management techniques are considered to be very important and there are a lot of techniques, especially artificial intelligent techniques.
\nArtificial intelligence is defined as the process of studying systems which behave in an intelligent manner as an observer to another. AI includes the use of tools depending on the intelligent behavior of human beings and other animals too in order for the complex problems [4].
\nAI is interested in artifacts intelligent behavior, which includes understanding, thinking, learning, communicating, and working in environments which are complex. In general, the utmost objective of AI is the process of perceiving the development of tools and mechanisms that can behave as humans behave or even better. Another objective of AI can be known which can as comprehension behavior, whether it appears in machines or in humans that mean simulate the human’s behaviors. Thus, AI contains both scientific and engineering objectives [5]. Various references discover in the scientific literature that artificial intelligence integrates with project management areas are based on the artificial intelligence, project success estimation, critical success factors identification, project budget Relatedness, project schedule connection planning of the project, and risk identification relatedness [6].
\nArtificial intelligence include the classification techniques.
\nClassification, as described in statistics and machine learning, is the identification of a group of categories (subpopulations) to identify a new category, depending on the o training set of data that have the same instance whose classes are already known. For instance, if an email is to be assigned as spam or nonspam or diagnosis of a specific patient by certain disease based on known features of the patient like gender, the symptoms that he has and blood pressure, in another word, can be said that classification is a symbol of pattern recognition [7].
\nClassification is the process of training the objective function f in which each attribute x is a map to class label y that is already known. Resulting in a group of records which is the training set (training set), I every and each record includes a collection of attributes, in which the class is among one of them [8].
\nThe model classification can be used for the following.
Descriptive modeling: the model of classification can work as a caption tool to show the difference between different classes with the same objects.
Predictive modeling: in this type of classification model, a label of the class that belongs to unknown data can be predicted [8], as shown in Figure 1.
Process of training the objective function f in which each attribute x is mapped.
The methods used in classification can be split into two categories as parametric and nonparametric problems. As a matter of fact, the basis of the parametric method is on the assumptions that the population normally distributed and the parameters are assessed to solve the problem [9]. On the other hand, there are no assumptions made about the distributions in the nonparametric methods and hence the distribution is free [10].
\nThe classification techniques are as follows.
\nThe decision is supported by using a tool like a decision tree by using the graph as a tree or modeling a variety of decisions and their potential effects, which include several examples of the outcomes of a chance event, costs of the resource, and utility [11].
\nDecision trees are usually used in the research of operations, more specific in analysis of the decision, to assist in the identification of the most of the strategies used to reach a goal, but they are a very popular tool to use in machine learning [11].
\nThe induction of the decision tree is to learn the decision trees from the row of training in the class that is already labeled. A decision tree as can be considered as a flowchart, for instance, tree structure, in which the internal node stands for test on an attribute, and the outcome of this test is represented by the node of the leaf containing a class label [12].
\nAn instance-based classifier called K-star or K*, is a class of the instance in the test step depending on the similar instance in the training step, as found by the function of the similarity. The difference between this algorithm and other instance-based learners is that this one uses a distance based on the entropy function. Classification based on the instance-based learners is made by comparing the instance to database examples that are previously classified. The basic assumption is that instances with similar classifications will be similar too. “Similar instance” and “similar classification” can be defined as follows: the instance-based components are the distance function that identifies the similarity between the two instances, and the classification function assigns how the instance similarities yield a final classification to classify the new instance. The entropic measure is used in K-star algorithm, depending on the likelihood of an instance transforming randomly into another by selecting among all potential transformations. The distance of the instance by using entropy as a meter is very helpful and the distance between the instances is measured with the help of the information theory. The distance between the instances is actually represented by the transformation complexity of one instance into another. It is being accomplished into two parts: first, a limited group of transformations is identified, which will assign one instance into another. Later, one instance (a) is tranformed to another instance (b) with the help of programs in a limited transformations sequence, beginning at (a) and ending at (b). A collection of points that is unbounded is given and a group of transformations that previously defined T is defined; group T has the value of. This t will be assigned as t: I → I. To assign instances with it, σ is used in the T (σ(a) = a). σ ending P, the group of all codes of the prefix from T*. Transformation on I is identified by members of T* and of P uniquely.
\nFor the employment of the classifier for the instance-based of a that employs the distance measure of entropy, there is the necessity of a method to select values for the x0 parameters for attributes that are real and s for the attributes that are symbolic, as method employment the values returned by the measure of distance to give a prediction.
\nFor every dimension, the selection of values must be made to the x0 parameters (for attributes that are real) and s (for attributes that are symbolic). The distance measure attitude as changes in these parameters is interesting. Function P* that considers the efficient number instances can be calculated using the expression[13]:
\nwhere N is considered to be the whole number of instances in the training and the instance number is in the training with the distance that considers the smallest from an (in this attribute).
\nValue for x0 (or so) is being selected by the K* algorithm, n0, and N with a number in between is selected and overturn the above expression. The nearest neighbor algorithm will be obtained by choosing n0 and weighted instances by choosing N. For convenience, the blending parameter “b” is used to specify the number in which the blending is different for n0, b = 0% and for N, b = 100%, with values of intermediate and linearly interpolated [13].
\nThe neural network is an analogous, information that considers distributed processing structure being composed of a processing element (which can have a local memory and implement operations that are considered as localized information processing) interrelated together as connections by using unidirectional signal channels. Each output is rlated to one element that connects with branches (“fans out”) into many collaterals as like (everyone has the same signal which is the output signal of the processing element) [14].
\nIn COLT-92 by Boser, Guyon and Vapnik introduced support vector machine (SVM). Since that time, it has become popular. This algorithm was theoretically developed from the theory of the statistical learning, and it considers the well-motivated algorithm since the 1960s [15].
\nPattern classification consider the main problem that deals with, that means different types of patterns is classified using this algorithm. Now, different sort of pattern exists, i.e., linear and nonlinear. Patterns that are linear can be distinguished easily or are able to be separated in low dimension easily, and on the other hand, the patterns that are nonlinear cannot be distinguish easily or are not able to be separated easily, and thus these patterns sorts require manipulation in order to be easily separated [15].
\nThe SVM main idea is the formation of a hyperplane that is considered an optimal, that is, able to be used for classification, in order to split the linear patterns. The selection of the optimal hyperplane is based on the selection of a hyperplane among the group of hyperplanes for the classification of the patterns in which the hyperplane margin is maximized like the distance between the nearest point of each pattern and the hyperplane. The main goal of SVM is that the margin is maximized in order and the process of the classification is preformed correctly of the given patterns, i.e., when the margin size is larger, the classification of the patterns is more accurate [15, 18]. The hyperplane equation:
\nThe pattern that is given by using kernel functions is able to be assigned to higher space of dimension; the function of the kernel is Φ(x). I.e. x Φ (x), the various functions of kernel election are very necessary for the classification using SVM; usually, the functions of the kernel that are used contain RBF, linear sigmoid, and Poly. For example [14].
\nThe Poly Kernel function equation is given as [14]:
\nThe basic concept of support vector machine is that a group of training sample is given (a) that contains a distributed sample which is considered identical and independent; the sample has xi, in which xi belongs to the Rd, and yi belongs to the {−1,1} and they both as {(xi,yi)}N i = 1, and they both refer to the classification input and output. The object is to determine wT.x + b = 0 that consider a hyperplane equation, in which two various samples are being split accurately. Hence, problem-solving with the classification that considers optimal is translated into quadratic programming for problems-solving. The search for a partition hyperplane is to maximize the area of bilateral blank (2/||w||), which means the weight of the margin has to be maximized. It is expressed as [14]:
\nThe main problem with the construction industry that contains a number of risks, and in order to minimize these risks, a model should be used to analyze these risks. A scientific research methodology is adopted which includes three stages:
\n\n
A review of the scientific literature and sources (books, magazines, engineering, research), which dealt with all of them.
Risk responses concept and strategies in construction projects.
Studying cost elements, types, and factors affecting it with the study of the causes of their appearance.
Studying the artificial intelligence techniques and the steps of its procedures and its uses in the construction projects.
Studying the simulation methods and the steps of its procedures and its uses in the construction projects.
The field study includes the following:
\nThis stage includes conducting many interviews with experts. The interviews include managers and university professors, and other parts of the projects in the following ministries: the Ministry of Higher Education and Scientific Research, the Ministry of Construction and Housing, and the Ministry of Education. These interviews have a very important role in helping the researcher in the later stage, also discussion about the questionnaire which is initially prepared from the literature and previous studies as well as doing some modifications on the form and adding another questions with the help of the experts to make sure of the success of the method and questions presented.
\nAfter the interviews with many experts have been finished. The problems of the research were divided into several groups which including the risks that cause to cost overruns, the top risk and their impacts on the projects, the strategies that are used for each risk, the reasons for risk response failure and finally the risks generated from risk response.
\nIn the light of the responses received from the questionnaire, the practical study is as follows:
Planning of risk.
Identification of risk.
Analysis of risk using decision tree and K-star machine.
Risk response evaluation using neural network and support vector.
In this model, two types of classification were used, descriptive classification by using decision tree and predictive classification by using K-star and as follow:
Identify the dependent variable.
Identify the independent variable.
Implement descriptive classification using a decision tree.
Implement predictive classification using K-star.
The decision tree application is an example of a descriptive classification. This type of classification considers a number of attributes (variable) which affect the variable to be described. This type of classification is important to the variables that have an effect on the target.
\nThis research describes the method of classification by using a decision tree to describe the qualitative analysis of the risks of project cost based on historical data. The data used to develop the classification model were the past data from various engineering works in different ministries. The method that is used to collect data is the direct data gathering from the engineering and the direct interview with the engineers and managers.
\nResults gained were collected from two parts: first one is the literature survey and the second one is the field of investigation (interview and questionnaire analysis) as mentioned before; 23 variables were considered as the independent variables; these variables are risks and their probabilities are considered too high, high, medium, low, and too low, and the impacts as too high, high, medium, low, too low, which are shown in Table 1.
\nRISKS | \n
1-Price fluctuation | \n
2-Inflation | \n
3-Unavailability of information | \n
4-Increase in cost due environment constrain | \n
5-Financial difficulty by the contractor | \n
6-financial difficulty by owner | \n
7-Absence of measurement before advance | \n
8-Design team performance | \n
9-Inadequate owner requirement | \n
10-Delay in agreement of design | \n
11-Mismanagement of contract | \n
12-Ambiguity of contract | \n
13-Selection of team management | \n
14-Miss selection of sit | \n
15-Labor production | \n
16-Luck of labor | \n
17-Delay in deliver in equipment | \n
18-Quality control on material | \n
19-Exceptional circumstances and risks | \n
20-Weather | \n
21-Wrong estimation | \n
22-Delay in the completing of the project | \n
23-Increase in the cost of material and equipment | \n
The identified risks.
Qualitative analysis is considered to be the dependent variable which is too high, high, medium, low, too low, and each individual engineer or manager is used as the basic unit of the observation. Therefore this model is considered to be an attempt to make a model consisting of the independent variables which could describe the qualitative analysis classification.
\nWaikato Environment for Knowledge Analysis (Weka) is a famous software in machine learning suite; the language used is Java; and University of Waikato, New Zealand developed this program. It is a software that considered as free, and the license of this program is a GNU General Public License. The Weka (said to rhyme similar to Mecca) is considered to be a workbench [16] which includes a group of tools for visualization and algorithms that are used to analyze the data and molding for the predictions; it is easy to access this function by using graphical user interface [15] and the version of Weka 3 that was developed in the early 1997 was used for many different implementation areas, especially for the purpose of education and research.
\nSeveral standard data mining tasks are supported by Weka, to be more specific, preprocessing of the data, regression, classification, clustering, visualization, and selection of the feature [17, 19], as shown in Figure 2.
\nGraphing component of WEKA 3.7.10 program.
The Explorer is in the GUI which is opened and the explorer is pressed on to insert the file that needs to be classified and by pressing the bottom open file to insert the file in the preprocess window which is used to choose and modify the data being acted on. As shown in Figure 3.
\nDisplays the preprocess window in the explorer.
At this stage, the model was uploaded and full information such as relation means the name of the file, the total number of instances, attributes, type, the missing value, and others is shown in the figure above.
\nAfter this stage, a classifier was selected to perform the descriptive analysis as shown in Figure 4.
\nDisplays the process of selecting the classifier.
In the section of Classify at the top, there is a Classifier box. This box has a number of text area that provides the name of the classifier that the research work with, and by clicking on the tree bottom, there are several algorithms available under this option, the researcher selects j48 algorithm which one the application and implementations of C4.5 as shown in Figure 5.
\nDisplays the process of selecting j48 properties.
In this step, the properties of the algorithm are selected. In this model after the trial and error, the confidence factor is 0.25, the debug is false, min NUM obj is 2, NUM Folds are 3, and the unpruned option is true, which give the researcher the best results achieved as shown in Tables 2 and 3.
\nTp rate | \nFp rate | \nPrecision | \nRecall | \nF-measure | \nMCC | \nROC area | \nPrc area | \nClass | \nDescription of the results | \n
---|---|---|---|---|---|---|---|---|---|
1.000 | \n0.031 | \n0.714 | \n1.000 | \n0.833 | \n0.832 | \n0.970 | \n0.638 | \nHigh | \nThe results show that this class is classified correctly as the Tp is 1 and Fp low and precision acceptable and the recall high, the MCC takes the classification between each two class and Roc area is high which indicates good performance for the high class and Prc is the medium indicator of the model performance | \n
0.867 | \n0.000 | \n1.000 | \n0.867 . | \n0.929 | \n0.983 | \n0.983 | \n0.957 | \nMedium | \nThe results show that this class is classified kind off correctly as the Tp is 0.867 and Fp is zero and precision acceptable and the recall high, the MCC takes the classification between each two class and Roc area is high which indicate good performance for the medium class and Prc is the good indicator of the model performance consider very good | \n
1.000 | \n0.130 | \n0.939 | \n1.000 | \n0.968 | \n0.904 | \n0.978 | \n0.979 | \nLow | \nThe results show that this class is classified correctly as the Tp is 1 and Fp is low and precision high and the recall high, the MCC takes the classification between each two class and Roc area is high which indicates good performance for the low class and Prc is the good indicator of the model performance which considers very good | \n
0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.739 | \n0.264 | \nToo low | \nThe results show that this class is classified incorrectly as the Tp is zero and Fp is zero and precision, the recall, the MCC are zero while Roc area is very low which indicates bad performance for the too low class and Prc is the good indicator of the model performance which considers very bad | \n
0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.000 | \n0.493 | \n0.014 | \nToo high | \nThe results show that this class is classified incorrectly as the Tp is zero and Fp is zero and precision, the recall, the MCC are zero while Roc area is very low which indicates bad performance for the too high class and Prc is the good indicator of the model performance which considers very bad | \n
0.928 | \n0.089 | \n0.895 | \n0.928 | \n0.908 | \n0.861 | \n0.965 | \n0.915 | \n\n | As a total cumulative result, its consider being good classification | \n
The decision tree results in the WEKA program.
Risks | \nActual | \nClassified | \n
---|---|---|
Price fluctuation | \nHigh | \nHigh | \n
Inflation | \nMedium | \nMedium | \n
Increase the cost of skilled labor | \nMedium | \nMedium | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nLow | \nLow | \n
The increase in cost due environment | \nToo low | \nLow | \n
Financial difficulty by the contractor | \nLow | \nLow | \n
Financial difficulty by owner | \nLow | \nLow | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nHigh | \nHigh | \n
Design team performance | \nLow | \nLow | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nLow | \nLow | \n
The ambiguity of contract | \nLow | \nLow | \n
Miss selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nMedium | \nMedium | \n
The decrease in labor | \nMedium | \nMedium | \n
Delay in delivery in equipment | \nMedium | \n\n |
Quality control on material | \nLow | \nLow | \n
Unexpected condition | \nHigh | \nHigh | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nLow | \nLow | \n
Price fluctuation | \nLow | \nLow | \n
Inflation | \nLow | \nLow | \n
Increase the cost of skilled labor | \nLow | \nLow | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nLow | \nLow | \n
The increase in cost due environment | \nToo low | \nLow | \n
Financial difficulty by the contractor | \nMedium | \nMedium | \n
Financial difficulty by owner | \nLow | \nLow | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nMedium | \nMedium | \n
Design team performance | \nMedium | \nMedium | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nLow | \nLow | \n
The ambiguity of contract | \nLow | \nLow | \n
Selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nLow | \nLow | \n
The decrease in labor | \nLow | \nLow | \n
Delay in delivery in equipment | \nLow | \nLow | \n
Quality control on material | \nMedium | \nHigh | \n
Unexpected condition | \nMedium | \nMedium | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nMedium | \nMedium | \n
Price fluctuation | \nLow | \nLow | \n
Inflation | \nLow | \nLow | \n
Increase the cost of skilled labor | \nLow | \nLow | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nMedium | \nMedium | \n
The increase in cost due environment | \nLow | \nLow | \n
Financial difficulty by the contractor | \nHigh | \nHigh | \n
Financial difficulty by owner | \nToo high | \nLow | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nMedium | \nMedium | \n
Design team performance | \nLow | \nLow | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nMedium | \nMedium | \n
The ambiguity of contract | \nLow | \nLow | \n
Selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nLow | \nLow | \n
The decrease in labor | \nLow | \nLow | \n
Delay in delivery in equipment | \nMedium | \nHigh | \n
Quality control on material | \nLow | \nLow | \n
Unexpected condition | \nHigh | \nHigh | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nLow | \nLow | \n
The classified and the actual data of decision tree the WEKA program.
According to the tree, the risk with medium impact has the following probability: medium—13 risks, low—3 risks, and too low and high—there is no medium classification; while the risk with low impact has the following probability: medium there are 7 class low, too low has 1 class, low has 36 class, and high does not have any class; on the other hand, the risk that has the impact high has class high with two of them are wrongly classified and the risks with impact too low and too high have the class of one too low and one too high, respectively, as shown in Figure 6.
\nDisplays the tree of j48.
As mentioned, Weka is a popular software for machine learning, and this type of algorithm will be used for predictive classification to predict the qualitative analysis of the risks for the periods 2014–2016 depending on the qualitative analysis for the previous periods from 2006 to 2014. The result is shown in Table 4.
\nRisks | \nActual | \nClassified | \n
---|---|---|
Price fluctuation | \nHigh | \nMedium | \n
Inflation | \nMedium | \nMedium | \n
Increase the cost of skilled labor | \nMedium | \nMedium | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nLow | \nLow | \n
The increase in cost due environment | \nToo low | \nLow | \n
Financial difficulty by the contractor | \nLow | \nLow | \n
Financial difficulty by owner | \nLow | \nLow | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nHigh | \nHigh | \n
Design team performance | \nLow | \nLow | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nLow | \nLow | \n
The ambiguity of contract | \nLow | \nLow | \n
Miss selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nMedium | \nMedium | \n
The decrease in labor | \nMedium | \nMedium | \n
Delay in delivery in equipment | \nMedium | \n\n |
Quality control on material | \nLow | \nLow | \n
Unexpected condition | \nHigh | \nHigh | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nLow | \nLow | \n
Price fluctuation | \nLow | \nLow | \n
Inflation | \nLow | \nLow | \n
Increase the cost of skilled labor | \nLow | \nLow | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nLow | \nLow | \n
The increase in cost due environment | \nToo low | \nLow | \n
Financial difficulty by the contractor | \nMedium | \nMedium | \n
Financial difficulty by owner | \nLow | \nLow | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nMedium | \nMedium | \n
Design team performance | \nMedium | \nMedium | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nLow | \nLow | \n
The ambiguity of contract | \nLow | \nLow | \n
Selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nLow | \nLow | \n
The decrease in labor | \nLow | \nLow | \n
Delay in delivery in equipment | \nLow | \nLow | \n
Quality control on material | \nMedium | \nHigh | \n
Unexpected condition | \nMedium | \nMedium | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nMedium | \nMedium | \n
Price fluctuation | \nLow | \nLow | \n
Inflation | \nLow | \nLow | \n
Increase the cost of skilled labor | \nLow | \nLow | \n
Unavailability of information | \nLow | \nLow | \n
Inaccurate estimation | \nMedium | \nMedium | \n
The increase in cost due environment | \nLow | \nLow | \n
Financial difficulty by the contractor | \nHigh | \nMedium | \n
Financial difficulty by owner | \nToo high | \nToo low | \n
The absence of measurement before | \nLow | \nLow | \n
Delay in time of the project | \nMedium | \nMedium | \n
Design team performance | \nLow | \nLow | \n
Inadequate owner requirement | \nLow | \nLow | \n
Delay in agreements of design | \nMedium | \nHigh | \n
The ambiguity of contract | \nLow | \nLow | \n
Selection of team management | \nLow | \nLow | \n
Miss election of sit | \nLow | \nLow | \n
Labor production | \nLow | \nLow | \n
The decrease in labor | \nLow | \nLow | \n
Delay in delivery in equipment | \nMedium | \nMedium | \n
Quality control on material | \nLow | \nLow | \n
Unexpected condition | \nHigh | \nMedium | \n
Weather | \nLow | \nLow | \n
Mismanagement of the contract | \nLow | \nLow | \n
The classified and the actual data of K-star in the WEKA program.
Thus the probability for each instance is calculated in the category of the qualitative analysis, and the highest probability is taken for the classification of the new instance.
\nThe process of opening the program and loading the file is mentioned earlier; the next step is choosing the properties of the algorithm as shown in Figure 7.
\nDisplays the process of selecting K-star properties.
The global blending was taken as 20 after several trial and error; it was found to be the best result, the entropy autoblending which means entropy base blending in this case, it does not use for better accuracy.
\nThe training data set has good accuracy, this high accuracy because the algorithm uses an entropy distance and use the whole data as training data. As shown in Figure 8.
\nDisplays the process of uploading the test file.
After this step, the testing data set is uploading to perform the prediction classification.
\nIn order to make a comparison between the two techniques, the whole data are used in cross-validation to make a comparison. As shown in Table 5.
\nCorrectly classified instances | \n91.304% | \nThis is considered being good classification accuracy | \n
Incorrectly classified instances | \n8.6975 | \nThere is no classification error | \n
Kappa statistic | \n81.85% | \nConsider being good value as compared to the realistic | \n
The correctly and incorrectly classified instance using cross validation in K-star.
Using the program, risk response failure in construction project was analyzed using the following techniques.
\nThis technique was used as part of the model to describe the risk response failure.
\nThis workflow represents the neural network model right-click it and select “Configure” from the menu, as shown in Figure 9.
\nDisplay neural network model.
The max number of iterations was selected 100 and by trial and error the number of the hidden layers was 1 and hidden neuron were 10, as shown in Figure 10.
\nDisplay configure menu.
The results are shown in Tables 6 and 7.
\nProject and year | \nRisks | \nQualitative analyses | \nRisk response | \nEffectiveness | \nEffectiveness linguistic | \n
---|---|---|---|---|---|
1, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n3.33 | \nMedium | \n
1, 2006 | \nInflation | \nMedium | \nAvoidance | \n2.60 | \nLow | \n
1, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.07 | \nMedium | \n
1, 2006 | \nLabor productivity | \nMedium | \nAcceptance | \n2.87 | \nMedium | \n
1, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n1.85 | \nLow | \n
1, 2006 | \nWrong estimation | \nHigh | \nAvoidance | \n3.33 | \nMedium | \n
\n | \n | \n | Acceptance | \n2.51 | \nLow | \n
2, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n3.40 | \nMedium | \n
2, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n1.80 | \nLow | \n
2, 2006 | \nChange in cost of equipment | \nMedium | \nAvoidance | \n2.87 | \nMedium | \n
2, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
2, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3.10 | \nMedium | \n
3, 2006 | \nDelay in completing of the project | \nHigh | \nAvoidance | \n3.33 | \nMedium | \n
3, 2006 | \nLabor productivity | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
3, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n1.95 | \nLow | \n
3, 2006 | \nWrong estimation | \nHigh | \nAvoidance | \n2.67 | \nMedium | \n
4, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAvoidance | \n3.07 | \nMedium | \n
4, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.60 | \nLow | \n
4, 2006 | \nChange in cost of equipment | \nMedium | \nAvoidance | \n3.20 | \nMedium | \n
4, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n3 | \nMedium | \n
4, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.80 | \nMedium | \n
5, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n3.20 | \nMedium | \n
5, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAvoidance | \n2.60 | \nLow | \n
5, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.20 | \nMedium | \n
5, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n3.13 | \nMedium | \n
5, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n1.85 | \nLow | \n
6, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.07 | \nMedium | \n
6, 2006 | \nChange in cost of equipment | \nMedium | \nAvoidance | \n3.40 | \nMedium | \n
6, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
6, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3.40 | \nMedium | \n
7, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n2.80 | \nMedium | \n
7, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAcceptance | \n2.90 | \nMedium | \n
7, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.87 | \nMedium | \n
7, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
7, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.60 | \nLow | \n
8, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n3.33 | \nMedium | \n
8, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAvoidance | \n3.17 | \nMedium | \n
8, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.80 | \nMedium | \n
8, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
8, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n1.90 | \nLow | \n
9, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAvoidance | \n2.87 | \nMedium | \n
9, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.13 | \nMedium | \n
9, 2006 | \nChange in cost of equipment | \nMedium | \nAvoidance | \n3.33 | \nMedium | \n
9, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n3.04 | \nMedium | \n
9, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3.40 | \nMedium | \n
10, 2006 | \nPrice fluctuation | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
10, 2006 | \nIncrease in the cost of skilled labor | \nMedium | \nAvoidance | \n2.77 | \nMedium | \n
10, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.27 | \nMedium | \n
10, 2006 | \nLuck of the labor | \nMedium | \nAvoidance | \n3 | \nMedium | \n
10, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n1.81 | \nLow | \n
11, 2006 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.3 | \nMedium | \n
11, 2006 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.90 | \nMedium | \n
12, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
12, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
12, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.17 | \nMedium | \n
12, 2008 | \nMismanagement of the contract | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
12, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.85 | \nMedium | \n
12, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.33 | \nMedium | \n
13, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.60 | \nMedium | \n
13, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.04 | \nLow | \n
13, 2008 | \nQuality control of the material | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
13, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.90 | \nMedium | \n
14, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
14, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.10 | \nMedium | \n
14, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.33 | \nMedium | \n
15, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.13 | \nMedium | \n
15, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.95 | \nMedium | \n
15, 2008 | \nQuality control of the material | \nMedium | \nAcceptance | \n1.81 | \nLow | \n
15, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.07 | \nMedium | \n
16, 2008 | \nFinical difficulty by the contractor | \nMedium | \nMedium | \n2.21 | \nLow | \n
16, 2008 | \nDelay in completing of the project | \nMedium | \nAvoidance | \n3.20 | \nMedium | \n
16, 2008 | \nDesign team performance | \nMedium | \nAcceptance | \n3 | \nMedium | \n
16, 2008 | \nQuality control of the material | \nMedium | \nMitigate | \n2.90 | \nMedium | \n
16, 2008 | \nMismanagement of the contract | \nMedium | \nAcceptance | \n3.22 | \nMedium | \n
16, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
16, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.23 | \nMedium | \n
17, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.17 | \nMedium | \n
17, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.85 | \nMedium | \n
17, 2008 | \nDesign team performance | \nMedium | \nMitigate | \n3.17 | \nMedium | \n
17, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n2.22 | \nLow | \n
17, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
17, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n1.81 | \nLow | \n
18, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
18, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
18, 2008 | \nQuality control of the material | \nMedium | \nAcceptance | \n2.87 | \nMedium | \n
18, 2008 | \nMismanagement of the contract | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
18, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.60 | \nMedium | \n
19, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.33 | \nMedium | \n
19, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
19, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.07 | \nMedium | \n
20, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.33 | \nLow | \n
20, 2008 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.85 | \nMedium | \n
20, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.03 | \nMedium | \n
20, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
21, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.40 | \nMedium | \n
21, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
21, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
21, 2008 | \nMismanagement of the contract | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
21, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
22, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.07 | \nMedium | \n
22, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
22, 2008 | \nDesign team performance | \nMedium | \nMitigate | \n2.95 | \nMedium | \n
22, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
22, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.07 | \nMedium | \n
22, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.40 | \nMedium | \n
23, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.21 | \nMedium | \n
23, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.07 | \nLow | \n
23, 2008 | \nDesign team performance | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
23, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.21 | \nMedium | \n
23, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
23, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.20 | \nMedium | \n
24, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.13 | \nMedium | \n
24, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.85 | \nMedium | \n
24, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.13 | \nMedium | \n
24, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.40 | \nMedium | \n
24, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
25, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n3.07 | \nMedium | \n
25, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.90 | \nMedium | \n
25, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.80 | \nMedium | \n
26, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.67 | \nMedium | \n
26, 2008 | \nDesign team performance | \nMedium | \nAcceptance | \n2.70 | \nLow | \n
26, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n2.55 | \nMedium | \n
26, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.33 | \nMedium | \n
26, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.17 | \nMedium | \n
27, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.80 | \nMedium | \n
27, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n3.40 | \nMedium | \n
27, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.17 | \nMedium | \n
27, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n2.87 | \nMedium | \n
27, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
28, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.33 | \nLow | \n
28, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.04 | \nMedium | \n
28, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.21 | \nMedium | \n
28, 2008 | \nWrong estimation | \nHigh | \nAcceptance | \n2.73 | \nMedium | \n
29, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.77 | \nMedium | \n
29, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n3.27 | \nMedium | \n
29, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.03 | \nMedium | \n
29, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.81 | \nMedium | \n
30, 2008 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n3.03 | \nMedium | \n
30, 2008 | \nQuality control of the material | \nMedium | \nAvoidance | \n2.90 | \nMedium | \n
30, 2008 | \nExceptional circumstance and risks | \nMedium | \nAcceptance | \n3.33 | \nMedium | \n
30, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.33 | \nLow | \n
31, 2008 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.07 | \nLow | \n
31, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.87 | \nMedium | \n
32, 2008 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n1.81 | \nLow | \n
32, 2008 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.33 | \nMedium | \n
32, 2008 | \nWrong estimation | \nMedium | \nAcceptance | \n2.60 | \nMedium | \n
33, 2014 | \nFinical difficulty by the contractor | \nHigh | \nAvoidance | \n3.33 | \nMedium | \n
33, 2014 | \nFinical difficulty by the owner | \nHigh | \nAvoidance | \n2.80 | \nMedium | \n
33, 2014 | \nDelay in completing of the project | \nHigh | \nAvoidance | \n2.87 | \nMedium | \n
33, 2014 | \nChange in cost of equipment | \nMedium | \nAcceptance | \n2.90 | \nMedium | \n
33, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.10 | \nLow | \n
34, 2014 | \nWrong estimation | \nMedium | \nAvoidance | \n3.33 | \nMedium | \n
34, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.13 | \nLow | \n
34, 2014 | \nFinical difficulty by the owner | \nHigh | \nAcceptance | \n2.95 | \nMedium | \n
34, 2014 | \nDelay in completing of the project | \nHigh | \nAvoidance | \n3.07 | \nMedium | \n
34, 2014 | \nChange in cost of equipment | \nMedium | \nAcceptance | \n3.13 | \nMedium | \n
34, 2014 | \nExceptional circumstance and risks | \nHigh | \nAvoidance | \n2.60 | \nMedium | \n
35, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.20 | \nLow | \n
35, 2014 | \nFinical difficulty by the owner | \nHigh | \nAcceptance | \n3 | \nMedium | \n
35, 2014 | \nDelay in completing of the project | \nHigh | \nAvoidance | \n2.90 | \nMedium | \n
35, 2014 | \nChange in cost of equipment | \nMedium | \nAcceptance | \n2.20 | \nLow | \n
35, 2014 | \nExceptional circumstance and risks | \nHigh | \nAvoidance | \n2.33 | \nLow | \n
36, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n3.20 | \nMedium | \n
36, 2014 | \nFinical difficulty by the owner | \nHigh | \nAcceptance | \n2.13 | \nLow | \n
36, 2014 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.85 | \nMedium | \n
36, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.07 | \nLow | \n
37, 2014 | \nWrong estimation | \nMedium | \nAvoidance | \n2.61 | \nMedium | \n
37, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
37, 2014 | \nFinical difficulty by the owner | \nHigh | \nAcceptance | \n2.60 | \nMedium | \n
37, 2014 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.77 | \nMedium | \n
37, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.80 | \nMedium | \n
38, 2014 | \nWrong estimation | \nMedium | \nAcceptance | \n1.87 | \nLow | \n
38, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.67 | \nMedium | \n
38, 2014 | \nFinical difficulty by the owner | \nHigh | \nAvoidance | \n2.60 | \nMedium | \n
38, 2014 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.13 | \nMedium | \n
38, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3.17 | \nMedium | \n
39, 2014 | \nFinical difficulty by the contractor | \nHigh | \nAvoidance | \n2.90 | \nMedium | \n
39, 2014 | \nFinical difficulty by the owner | \nHigh | \nAvoidance | \n2.80 | \nMedium | \n
39, 2014 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n2.90 | \nMedium | \n
39, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n2.87 | \nMedium | \n
40, 2014 | \nFinical difficulty by the owner | \nHigh | \nAvoidance | \n3.07 | \nMedium | \n
40, 2014 | \nDelay in completing of the project | \nHigh | \nAcceptance | \n3.33 | \nMedium | \n
40, 2014 | \nChange in cost of equipment | \nMedium | \nAvoidance | \n2.04 | \nLow | \n
40, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3.40 | \nMedium | \n
41, 2014 | \nFinical difficulty by the contractor | \nMedium | \nAvoidance | \n2.07 | \nLow | \n
41, 2014 | \nFinical difficulty by the owner | \nHigh | \nAvoidance | \n2.77 | \nMedium | \n
41, 2014 | \nDelay in completing of the project | \nMedium | \nAcceptance | \n2.27 | \nLow | \n
41, 2014 | \nExceptional circumstance and risks | \nHigh | \nAcceptance | \n3 | \nMedium | \n
The results of the risk response effectiveness in the 41 projects.
Risk response | \nActual effectiveness | \nPredication effectiveness | \n
---|---|---|
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nLow | \n
Avoidance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nLow | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nLow | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nLow | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nLow | \n
Acceptance | \nMedium | \nLow | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nLow | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nLow | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nLow | \n
Acceptance | \nMedium | \nLow | \n
Avoidance | \nMedium | \nLow | \n
Avoidance | \nLow | \nLow | \n
Acceptance | \nMedium | \nMedium | \n
Show the actual effectiveness and the predication effectiveness of risk response using neural network results.
This technique was used as part of the model to predict risk response failure. As shown in Figures 11 and 12.
\nDisplay support vector machine model.
Display support vector machine learner menu.
When clicking on the SVM learner node the following window appears. The results are shown in Tables 8 and 9.
\nClass | \nTrue post | \nFalse post | \nTrue neg | \nFalse neg | \nRecall | \nPrecision | \nsensitivity | \nSpecify | \nf-mean | \nAccuracy | \nDescription | \n
---|---|---|---|---|---|---|---|---|---|---|---|
Medium | \n\n | \n23 | \n0 | \n0 | \n1 | \n0.842 | \n1 | \n0.0 | \n0.941 | \n\n | The results show data classified correctly and the result perform well | \n
Low | \n0 | \n0 | \n123 | \n23 | \n0.0 | \n0.0 | \n0 | \n1 | \n0 | \n\n | The results show that this class is not classified correctly as there is a lot of error in the low class and the Precision, and also the f measure is less that indicate the reason to lower the classification accuracy | \n
Overall | \n\n | \n | \n | \n | \n | \n | \n | \n | \n | 84.2 | \n\n |
Support vector machine results.
Risk response | \nActual effectiveness | \nPredication effectiveness | \n
---|---|---|
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Mitigate | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Avoidance | \nMedium | \nMedium | \n
Avoidance | \nLow | \nMedium | \n
Acceptance | \nMedium | \nMedium | \n
Show the actual effectiveness and the predication effectiveness of risk response using support vector machine results.
\n
The result from the statistical analysis results in a period of (2006–2007) show that the risks that have the highest qualitative analysis are the same that resulting from the classification result by using j48 algorithm
The result from the statistical analysis results in the period of (2014–2016) show that the risks that have the highest qualitative analysis are the same that resulting from the classification result by using j48 algorithm except for one risk which is the quality control on the material and expertise in execution and that leads to an error.
The result from the statistical analysis results in the period of (2014–2016) show that the risks that have the highest qualitative analysis are same that resulting from the classification result by using j48 algorithm except for two risks which are financial difficulty by owner and Changes in the purchase costs or delay in the delivery of equipment and machinery and that leads to an error.
Different risks were found due to the different condition of these periods, however, some risks were the same as the delay in completing the project, exceptional circumstances, and the wrong estimation existed in every period
The decision tree is a successful quality technique in risk analysis
The result from the statistical analysis results in a period of 2014–2016 showed that the risks that have the highest qualitative analysis are same that resulting from the classification result by using K-star algorithm except for one risk which is financial difficulty by owner.
\nSecond: Risk response identification
\nIn the periods of (2006–2007) the method that used for risk response selection was historical information for similar previous projects has the mean of 4.03 that means often this method used for selection.
In the periods of (2008–2013) the method that used for risk response selection was historical information for similar previous projects has the mean of 3.73 that means often this method used for selection.
In the periods of (2014–2016) the method that used for risk response selection was historical information for similar previous projects has the mean of 3.80 that mean often this method used for selection.
The existing methods and tools for selecting a risk response are based on historical information for similar previous projects, m.aking them easily affected by anxiety, uncertainty.
Three techniques to identify risk response failure.
The decision tree shows the high accuracy and that because it considers the best algorithm in prediction of nominal class
The neural network shows the lower accuracy as the nature of the algorithm tends more to the numerical class.
The support vector machine show good results close to the decision tree
The most important reasons that risk response fails in the period of (2006–2007) were The difficulty of implementing a risk-response plan correctly for internal factors (terrorism and sabotage), Multiple decision sources for selecting a response strategy, Inadequate strategy with high risk and The inability to introduce sophisticated management methods to respond to risks.
The most important reasons that risk response fails in the period of (2008–2013) were Changes in the cost criteria that have been estimated at the planning stage of the project to the implementation stage, Inadequate strategy with high risk and The inability to introduce sophisticated management methods to respond to risks.
The most important reasons that risk response fails in the period of (2014–2016) are negligence of supervisors in the follow-up to the risk response plan, lack of funds for training and continuous development of the risk response team, inadequate strategy with high risk, delay in the disbursement of financial dues by the responsible party, the difficulty of implementing a risk-response plan correctly for internal factors (terrorism and sabotage) and The inability to introduce sophisticated management methods to respond to risks.
The period of (2014) shows many reasons that led to risk response failure in construction projects due to the condition of the country.
We live in a time in which claims proliferate about a multitude of issues regarding social reality and people’s lives. Because some of these issues are understood as adversely affecting a significant part of the population, they create a collective discourse and demands for action. When important societal groups (e.g. politicians, social change groups, the news media and numerous citizens) recognize these claims as legitimate and valid, they become social problems. As such, from a social constructionist perspective, the emergence and recognition of social problems are based on both the empirical evidence of their existence and impact as well as on the perceptions of their implications and need to be solved [1].
As Best [2] mentioned, social progress paradoxically creates social problems for different reasons. On the one hand, it generates a general expectation of perfectibility, which fosters a growing intolerance towards social difficulties and shortcomings. On the other hand, as the biggest challenges of humanity are within our reach (e.g. increased life expectancy, control of diseases), those that once were considered small now seem bigger and more serious (e.g. quality of life, lifelong learning). Moreover, the growing exigence of societies, together with the multiplication of relationships and communication networks, lowers the tolerance threshold of the population. Finally, because social progress improves life expectancy and standards of living, it fosters fears of loss as well as inflates perceived risks and defensive postures [2].
At given times and in relation to certain phenomena, a discrepancy exists not only between social perceptions and the available data on the issues but also among the perceptions of different social groups. For instance, policymakers might be attentive to a certain subject based on their knowledge, and this view might not be shared or valued in the same way by society as a whole. We believe that child trafficking meets this standard worldwide and, specifically, in Portugal.
Regarding Portugal, the following reasons (some general, others country-specific) are put forward to contend that child trafficking is not perceived as a social problem as previously defined: (i) it is relatively unknown among most of the population, (ii) it involves children as victims, (iii) it involves children who are often from disadvantaged backgrounds and/or foreign origins, (iv) it is a police matter and (v) it involves a small group of the population.
Let us discuss each aspect briefly.
Research on the public awareness of trafficking in human beings (THB) is scarce. However, as Sharapov [3] asserts, it is a distant subject for most of the population. Various European countries (e.g. Scotland, Belgium, Czech Republic, Finland and Romania) generally view it as having little relevance to their daily lives. This sense of detachment is not so much due to the lack of information on the subject, as to how this information is framed (i.e. primarily as a legal and criminal issue on the margins of normal everyday life) [4]. Portuguese reports on people’s awareness of human trafficking are in line with this general tendency. In a study conducted by Sani, Nunes and Caridade [5], the authors used a convenience sample to find that most respondents showed a poor understanding of THB and recognized the lack of information concerning this phenomenon in Portugal. Most participants viewed THB as the exploitation of immigrants and socially disadvantaged people in search of work. More than half (58.5%) had not heard any information about it over the last 2 years. Among those who had heard information, social media stood out as the privileged method of communication regarding this phenomenon.
Although the relationships among public opinion, the media and public policies are complex and controversial, the available evidence shows that political and media discourses on human trafficking significantly influence public opinion, and (conversely) public opinion affects media and policymaking [3, 4].
With regard to Portugal, the role the media plays in framing the public discourse on human trafficking has received specific attention. Research examining the written press between 2001 and 2004 [6] substantiated the media representation of human trafficking as infrequent, superficial and stereotyped. News on this topic was poor, barely visible and associated with criminal activity and deviant people from other countries and minorities. In a second study focusing on the news published in a tabloid newspaper in 2008, despite the increased visibility of the phenomenon, Couto, Machado, Martins, and Gonçalves [7] identified similar trends in the coverage: it was framed as a criminal problem, essentially involving deviant groups, and is generally related to illegal immigration. This representation facilitates the adoption of a passive and moralizing attitude of devaluation regarding the phenomena and depreciates the people involved. As has been indicated with regard to child trafficking in other European countries [8], a cross-border problem that affects almost only asylum seekers and immigrants or particular cases is unlikely to affect the general community. This assumption leads to the underestimation of the risks of child trafficking and to the weakening of the social relevance of prevention and protection strategies.
Research on human trafficking and, in particular, child trafficking, is critical. Producing and disseminating knowledge helps sensitize people in general. Moreover, it substantiates more effective processes of identification, prevention, and protection, as well as helps support victims and prosecute criminals [9].
As Clemente [10] stated, the Portuguese investigation concerning human trafficking has developed more slowly and inconsistently than that in other countries (the first publications date back to 2000) [11]. Driven by the increasing attention of the national authorities to the phenomenon, within the framework of international agreements, the academic research made its greatest development beginning in 2007 onwards. As in other European countries, where the empirical literature on child trafficking is scarce [12], the scientific approach to this problem in Portugal since the beginning consisted primarily of secondary research articles on the sexual exploitation of women based on official statistics. For various reasons, this research appears to report only one-third of the referrals per year [11]. Reports on the prevalence of the phenomenon to monitor and evaluate prevention, protection and rehabilitation programmes have been more systematic and frequent than that regarding trafficked victims, the circumstances of their victimization or the trafficking process, its rationale, dynamics and other people involved. Nevertheless, the study of the social representations of human trafficking, in particular those of specific groups including practitioners, has been a significant part of the investigation undertaken in Portugal in this field, yet with sampling limitations. In these types of scholarly papers, child trafficking is often referred to in connection with the characterization of the age of the victims. However, research specifically focusing on child trafficking is scarcer. In this regard, the RCAAP1 portal is a privileged source of access to Portuguese academic production. From 2006 to 2016, 12 papers on human trafficking were registered from Portuguese repositories [13]. However, only eight titles include the keywords “child trafficking” as of 2019: a working paper and seven master’s theses.
In short, the still incipient scientific production and media coverage focusing on criminal cases reinforce the lack of information associated with public opinion and, to that extent, create a distance with regard to this phenomenon.
As has been elaborated elsewhere [14], a widespread social consensus exists regarding child welfare matters. Although these matters are frequently used by politicians and the media as rhetorical devices and emotional assets [15], this discursive intensity is not always consistently translated into policies or practices. On the other hand, this unanimity, even if it arouses ethical shock and emotional indignation, does not induce social pressure or collectively persistent action in relation to children’s issues.
If children whose parents fail to protect their best interest are socially vulnerable, relatively invisible and voiceless, child victims are viewed even as more helpless. This issue is especially relevant because, as Gearon [16] argued, child trafficking narratives are pervaded by the notion of victimhood, conveying representations of helplessness, vulnerability and lack of agency. This convergent negativity (those of children and victims) [14] easily evokes empathic social responses but does not necessarily make the victim a priority.
Furthermore, the formal requirements for eligibility to the protection system, which are understood in light of the social representations of victimhood, create a paradox. On the one hand, the confirmation of the status of a victim is a mandatory requirement for accessing assistance. On the other hand, when victims do not comply with the social expectations of powerlessness and passivity, the question of their responsibility often arises regarding the situation in which they find themselves. Although they are children, if their participation in trafficking is understood as active and voluntary, then their status as child victims gives way to that of the criminal children: those who should be blamed, convicted and punished, rather than protected [16]. Although the victim is viewed as an object of behaviors and situations beyond their control or ability to self-determine, the circumstances of children allegedly involved in criminal activities are dimmed, and their behaviors and condition tend to be abstracted and decontextualized [14]. In short, whether they match the stereotype of a victim or not, children who are victims of trafficking have no voice or do not deserve to be heard. Either case applies to what Clemente [10] called “the deafening silence of trafficked people” (p. 663).
Public opinion is an indication of societal attitudes towards certain phenomena. With regard to human trafficking, if public opinion expresses the culture of society, then it might reflect the social discrimination of women and children as well as a social tolerance towards violence and exploitation [8], which would enable an understanding of the symbolic construction of this phenomenon. On the other hand, because public opinion influences policymaking, if well-formed and informed, it can also help reduce the structural factors that underlie trafficking [3].
Victims of child trafficking and exploitation often come from abroad and the margins of society. According to the final report of the study of groups at high risk for human trafficking in the European Union [8], the risk of child trafficking is significantly higher for children with disabilities or who engage in risky behaviors, from dysfunctional, disadvantaged and/or social excluded families, communities or neighborhoods in areas of conflict or crisis. Generally, they might be viewed as problem children. This label reinforces their inherent negativity [14], social invisibility and exclusion [17]; they are the children of others. As such, instead of a positive approach to the phenomenon based on a human rights perspective, they might evoke attitudes of socially organized denial [18], rejection, disinterest or criticism.
Thus, the genesis of trafficking is essentially sociopolitical, culminating in a legal phenomenon. Experts [16] have criticized the criminal perspective that has dominated child trafficking “as immigration-led and prosecution-focused” (p. 497) and as a threat to human and state security [19]. This simplistic view overshadows the broader dimension of the phenomenon, which as Gearon [16] asserted is not limited to international movements (legal or illegal) or migrant children. In fact, as Palmer [20] claimed, child trafficking is a complex, multidimensional and dynamic process; it is a shape-shifting phenomenon characterized by different patterns across countries. It involves national citizens, migrants (i.e. other EU citizens), immigrants from third world countries, children with their families and unaccompanied asylum-seeking children. It might begin long before it is detected, with or without family involvement, for a variety of purposes, and as part of more or less sophisticated networks. However, according to Crawley’s research in the UK [21], even the intervention of health and welfare professionals is permeated by the logic of immigration control [22], where the concern with the protection of the territory overrides that of children. In the same vein, Clemente [10] characterized the Portuguese support system for victims of trafficking as victim-oriented in theory but as focused on internal security objectives in practice. This feature corresponds to the current trend of protection systems, which are increasingly focused on control rather than care [23].
This partial viewpoint hinders interventions focused on the rights and needs of children and their protection [16]. As experts have argued, the majority of children who eventually become victims of exploitation and/or trafficking have previously been deprived of their rights [24]. In this sense, they contend that the criminal matrix of the definition of child trafficking frequently overshadows its true nature and origin: child trafficking is primarily a matter of rights and protection, and, as such, it reveals the shortcomings of protection and welfare systems [24, 25]. Therefore, the priority or almost exclusive emphasis on legal and procedural aspects and the subsequent production of penal legislation, national action plans and support measures for victims at the expense of action towards the structural conditions that underlie trafficking are criticized. Enforcing the United Nations Convention on the Rights of the Child is the most effective way to prevent the exploitation and trafficking of children and youth as well as safeguard their rights and respond to their needs.
Data available on human trafficking and, specifically, child trafficking provide only an approximation of the reality. In addition to being a hidden phenomenon [19], illegal and clandestine [26], concepts of trafficking differ across countries as various reports have highlighted. Because trafficking is usually closely connected to crimes such as illegal immigration, aid to illegal immigration, forgery and criminal association, the distinction among these phenomena is not clear [24]. Consequently, defining these terms involves a wide range of legislation [27] and a variety of mechanisms to identify phenomena. Furthermore, even at the national level, experts have identified many discrepancies in the data reported across different sources, as in the case of Portugal [11]. These findings support the idea of numerous unreported cases (the dark figures of trafficking). Therefore the low numbers of child trafficking represent the tip of the iceberg [19, 28].
Additionally, the eligibility criteria for acquiring the status of trafficking victim and receiving assistance significantly modify the available data on this phenomenon.
Especially with regard to child trafficking, the criterion used to set the age of the victim, whether at the time of referral or at the beginning of trafficking, remains a sensitive issue. Neves and Pedra [11] drew attention to the fact that many victims have been subjected to exploitation for several years, even though their identification in the system occurred during adulthood. This consideration provides a different basis for calculating the number of child trafficking victims as well as targets and adjusts the interventions made available to people in these conditions within the legal and protection systems. In fact, according to Catch and Sustain [29], trafficked children (especially those with a long history of being exploited) tend to be treated by the legal and the protection systems according to their immigration status or the crimes in which they might have been involved, rather than the crimes that they have suffered along the way. This procedure is contrary to Directive 2011/36/EU, which recommends the development of comprehensive child-sensitive protection systems and the mobilization of recovery processes as soon as children are identified as victims of trafficking.
Another eligibility criterion concerns the distinction between the child trafficking statistics and the risk of child trafficking (i.e. trafficked children and those vulnerable to exploitation and trafficking). In line with this view, researchers have claimed that the groups of children targeted to prevent and combat trafficking (currently unaccompanied asylum-seeking children, child victims of trafficking and child victims of sexual exploitation) should be revised to include accompanied child migrants, undocumented children and homeless children [24]. According to scholars and practitioners, within the logic of prevention and effective interventions, authorities should expand their focus to include children at risk of being trafficked, favoring more comprehensive interventions in addition to focused responses. This option would imply overcoming a segmented view of the intervention, based on children’s status, to adopt an ecological, systemic, multidimensional and dynamic perspective of children’s vulnerability [24].
For the reasons outlined above and given the absence of reliable and disaggregated data [29], comparisons of national data are far from linear [24].
Nevertheless, despite the scarcity of consistent data, according to the United Nations Office on Drugs and Crime [30], human trafficking could likely reach up to 4 million victims. Most are female (72%) and trafficked for sexual exploitation, although significant regional variations are observed [30]. The Organization for Security and Cooperation in Europe (OSCE) estimates that children account for over 30% of the world’s human trafficking [31]. According to the 2018 Global Report on Trafficking in Persons, in 2016, 23% of these children were girls, which is more than double than that identified in 2004 [32]. As the International Labour Organization (ILO) specified, 26% of the 20.9 million people who were victims of forced labour between 2002 and 2011 were children [33].
As noted in the report of Catch and Sustain [29], child trafficking occurs in all European countries, without a clear division among countries of origin, destination or transit. In the European Union (EU) in 2015–2016, approximately 56% of the victims identified were from non-EU countries. The majority were female (68%), and the dominant forms of exploitation were for sexual and labour purposes (56 and 26%, respectively); relevant geographical variations were also observed. Children accounted for 23% of the victims detected and for 23% of all victims of trafficking for sexual exploitation [34]. However, considering the cases reported to/by NGOs and official agencies, experts in this field estimate that the dark figures might be five times higher than official statistics report [35].
The “official data on human trafficking (TSH) in Portugal indicate a residual phenomenon, with approximately 80 cases reported each year on average, with a total of 250 victims in 2009, 2010 and 2011. Of these cases, less than 25% were confirmed (58)” ([11], p. 23). Regarding child trafficking, prior to 2010, national data were subsumed under the general data of human trafficking. Nonetheless, since national data on human trafficking began being collected, reports indicate that few children were victims of this type of crime (e.g. in 2008, the minimum age of the reported cases was 1 year old; in 2009, the minimum age was 12 years old). In accordance with the Report of the Group of Experts on Action against Trafficking in Human Beings [35], between 2008 and 2011, 17 children met this condition, the majority of whom were female (median age = 14 years old). Sexual exploitation, labour exploitation (n = 3) and attempted adoption (n = 3) were the identified purposes of trafficking.
Table 1 details the number of child trafficking referrals considering the total number of human trafficking referrals in Portugal from 2010 to 2017. Data, compiled from different sources, represent cases reported before investigation and substantiation. Absolute figures are relatively low, both in relation to human trafficking in general and to child trafficking specifically; however, notable variations have been found over time. The percentage of child trafficking referrals varied from 8.1% in 2010 to 31.2% in 2012.
According to Neves and Pedra [11], 82.4% of the trafficked children in Portugal are 15 years or older. In addition, those who began to be trafficked in childhood tend to be exploited for longer periods than those who start the process in adulthood. In fact, 44% of trafficked children have been exploited longer than 4 years. According to these authors, if the criterion used to set the age of the victim is the beginning of trafficking, then this number would be approximately 30% of all cases of trafficking.
As Clemente [10] asserted, the introduction of the issue of human trafficking in Portugal was prompted by the need to transpose the international directives issued by the United Nations and the European Commission into national law. When Portugal adopted the United Nations Convention against Transnational Organized Crime and the additional Protocol to Prevent, Suppress and Punish Human Trafficking (especially with regard to women and children) in 2004, all legislative changes that followed have given rise to the definition and development of specific policies and procedures. National Plans against Human Trafficking have played a decisive role in the adoption of an integrated intervention strategy, combining interventions at different levels and multiple actors.
In 2007, the Portuguese authorities developed the first National Plan against Human Trafficking (2007–2010) (I PNCTSH) [31], which had four strategic domains: (1) to understand the phenomenon and disseminate information; (2) to prevent, elucidate and train; (3) to protect, support and integrate; and (4) to conduct criminal investigation and prosecute traffickers [31]. With regard to children, (i) they are recognized as being among the most vulnerable populations to human trafficking (along with women), particularly those living in poverty; (ii) trafficking is considered as a violation of their right to be free and protected; and (iii) victims trafficked for reasons of sexual and/or labour exploitation require special assistance and protection. Considering children’s and youths’ vulnerability, the I PNCTSH considers the following specific areas of intervention: (i) to educate children by promoting creative debates and activities in schools about human rights, children’s rights and human trafficking and (ii) to support trafficked children by providing special measures to promote their rights and protection aiming at their global development.
This Plan led to the creation of the Observatory on Human Trafficking (OTSH) in 2008, which was established by Decree-law no. 229 on November 27, 2008. It is responsible for collecting, producing, processing and analyzing data on human trafficking, including child trafficking.
In 2009, two initiatives stood out in this domain [27]: (a) the certification of documents for foreign children attending Portuguese schools (Immigration and Borders Service [SEF] goes to school) and (b) the creation of the first temporary shelter for 6- to 18-year-old refugee children and (CACR). This centre is expected to provide specialized assistance to children and youth in the asylum process (i.e. legal, social and psychological support).
The second National Plan against Human Trafficking (2011–2013) (II PNCTSH) [42] defined the same areas of intervention as the I PNCTSH. Nonetheless, the special situation of children was not differentiated in this Plan, neither conceptually nor in terms of specific measures.
In 2013, Portugal joined (a) the “European Cross-Actors Exchange Platform for Trafficked Children on Methodology Building for Prevention and Sustainable Inclusion”, which aimed to develop an evidence-based intervention model targeting children who are vulnerable to trafficking and promote knowledge about criminal procedures concerning THB in the EU, and (b) the “Improving and Monitoring Protection Systems against Child Trafficking and Exploitation” (IMPACT) Project, which aimed to improve child protection and welfare policies to prevent and protect children, particularly those vulnerable to trafficking and exploitation [24].
The third National Plan (2014–2017) (III PNCTSH) [43] had five strategic areas: (1) prevent, inform, understand and investigate; (2) educate, train and capacitate; (3) protect, intervene and empower; (4) investigate criminality; and (5) cooperate. Regarding children, it included actions such as the development of education for children, adolescents and adults, as well as the development of protocols for the prevention, detection and protection of child victims of trafficking. The implementation of standardized care protocols for the identification, referral and intervention of victims of trafficking, among others, and for child victims of trafficking, in particular, by the National Health Service (NHS), was particularly important.
The fourth National Plan (2018–2021) (IV PNCTSH) [40] defined three strategic objectives: (i) to develop knowledge and raise awareness on THB, (ii) to improve the quality of interventions for trafficking victims, and (iii) to strengthen the fight against organized crime networks. With regard to children, an emphasis was put on the development of guidelines and protocols for intervening with children across different services as well as on reinforcing networking within different services, including the NHS and the National Network for the Support and Protection of Trafficking Victims.
As the four national plans against human trafficking show, children are included as victims of trafficking and are occasionally specifically targeted. However, none of the plans address the specificity of child trafficking.
The Portuguese Penal Code (Law no. 59/2007) defines child trafficking as enticing, transporting, harboring or housing a child or transferring, offering or accepting the child for the purpose of sexual exploitation, labour exploitation, begging, slavery, the removal of organs or adoption, among other criminal activities (Article 160) [24]. In fact, research shows that, most of the time, children are trafficked for labour exploitation (e.g. domestic servitude, agricultural labour, car washing, factory labour), sexual exploitation (e.g. prostitution, pornography and forced marriage), illegal adoption and criminal exploitation (e.g. cannabis cultivation, robbery, begging) [44].
As detailed, this definition entails the intentional action of the offender to exploit a vulnerable person. In the case of children, the critical elements of the definition of human trafficking do not apply [34]: the means (coercion, fraud or deception, abuse of power or of a position of vulnerability) and the victim’s consent. However, the issue of consent is not exempted from controversy because of the debate concerning the age of consent [19], an issue related to the criterion used to set the age of the victim, as previously discussed.
Associated with age and the corresponding development level are, among others, the issues of: a) capacity to consent to their involvement in certain activities or relationships, especially those that are deviant or criminal; b) the responsibility inherent to this (in)capacity; c) the different developmental needs that children might exhibit depending on their age; d) their plasticity and vulnerability to the external world and the consequent and serious implications of their involvement in harmful relationships and activities; e) longer periods of exploitation than those of trafficked adults; and f) more support needed, given the higher likelihood to develop trauma.
As Greenbaum, Yun and Todres ([45], p. 161) argued, “Given the often multiple vulnerabilities leading to trafficking, the complex trauma experienced during (and sometimes before) the trafficking period, and the myriad adverse effects of exploitation, the needs of the child victim may be extensive and multi-faceted”. In fact, the exploitation and trafficking of children are often associated with adverse experiences of abuse, neglect and other forms of violence that harm their physical and mental health. Empirical evidence indicates that between approximately one-fourth and one-half of all trafficked children are victims of physical or sexual abuse [26, 46]. These forms of maltreatment are associated with psychological disorders such as posttraumatic stress disorder, substance abuse, self-harming behaviour, suicide ideation/attempts, depression and various affective disorders and behavioral difficulties [26].
Thus, interventions must be multidimensional and address different groups and levels of victims’ needs, not only those that result from trafficking but also those that are at its origin. Child trafficking shares with the other forms of human trafficking multiple, complex and systemic causes, especially specific structural features of the life contexts. Family dynamics are especially relevant for children: dysfunctional families, family abuse/neglect and violence are key variables [24]. Social and political factors such as poverty, social and economic exclusion, limited opportunities to pursue education, scarce and precarious employment, gender-based violence and social inequalities between countries and regions are also important enabling factors. Likewise, more distal elements, such as exploitative relationships, organized crime, illegal migration, corruption and armed conflict play key roles in this phenomenon [43].
Child protection systems are the fundamental framework for providing assistance to child victims or those vulnerable to trafficking [20]. In Portugal, a victim of child trafficking is considered as at risk, falling under Protection Law no. 23/2017 and subject to the same assistance as any national child, regardless of nationality or type of risk. Any person identified as a victim of child trafficking should be reported to a public prosecutor who activates a protection measure.
Based on a review of 20 multinational projects funded by the European Commission on child victims of trafficking, including Portugal, Palmer [20] concluded that “trafficked children across Europe are not receiving the state care and protection that they, as children, are entitled to” (p. 7). This study showed the structural flaws in child protection services at three levels: victim detection, support and service coordination. In short, services fail to adjust their level and type of support to the specific needs of these children and youth, either initially, when detecting and referring cases, or in the provision of appropriate assistance. If in some cases a dominant concern exists with the child’s immigration status that overrides the response to their needs, in others the response to these children’s needs is subsumed within the generic provision available to all children at risk. Solutions range from those that compromise children’s freedom to those that do not guarantee their protection or meet their needs, providing overly bureaucratic services. In these circumstances, child protection systems are more harmful than protective and can even facilitate the exploitation and trafficking of these children [47], actively compromising their rights.
Additionally, combating and preventing child trafficking requires a systemic approach in which communication among the health, education, welfare and justice systems is critical [20]. Regarding Portugal, international reports have mentioned the lack of standardized procedures and referral mechanisms [24].
Despite the recognition of the inclusive nature of health, education, welfare and justice sectoral policies, the status of children continues to determine the type of services and rights to which they have access. In accordance with experts, legislation is inclusive, but procedures are not, and the strategic planning and coordination of actions have flaws. However, others argue for the need for comprehensive laws to integrate the different policy domains, procedures and services [24].
Despite the centrality of the child protection system regarding child victims or those at risk of being trafficked, referrals in Portugal are made through two types of entities: criminal agencies (OPC) and NGOs and the Authority for Working Conditions (ACT). Regarding the OPC, the Immigration and Borders Service (SEF) is responsible for detecting unaccompanied asylum-seeking children and for referring them to the Portuguese Refugee Council (CPR) [24] because it is assumed that they are vulnerable to trafficking [19]. The identification of a case triggers its referral for criminal investigation and court proceedings [24]. After referral, the cases proceed through an assessment process during which they can be classified as confirmed, not confirmed or under investigation. This assessment is complex. Usually a panoply of related offenses exist whose legal distinction is challenging, such as aid to illegal immigration, recruitment of illegal labour, kidnapping, slavery, sham marriages/marriages of convenience, counterfeit and false documents, criminal association, domestic violence or violation [36] and smuggling [12]. This procedure confirms, once again, the police and security framing of an essentially sociopolitical phenomenon.
A common reference in the literature on this topic is the need for the specific training of practitioners who, at different levels, work with and for children, especially those with child victims or children at risk of being trafficked to identify and provide the necessary and appropriate assistance. Immigration officers and other police forces are the frontline staff in most of the national referral mechanisms in Europe. Experts contend that the adequate training of these professionals is critical to ensure that victims are correctly identified and receive appropriate support [19], including cooperation with relevant agencies. In addition to these officials [24], from a comprehensive perspective, teachers [32] and healthcare professionals [48] would benefit from training to identify the signs of exploitation and effectively intervene.
Following Pinto et al. [13], child trafficking is not merely a “distant history” taught in school, but it is also geographically and sociologically distant, as portrayed by the media, and a distant interest for research. Law, politics/public administration, research and education are key instruments for fostering the social appreciation of this problem.
The law is a fundamental constituent of society; it is based on justice and is a driver of awareness. Retrospectively, it reflects social and cultural historicity. Looking forward, however, the law sets the standards for behaviour within a culture and promotes psychosocial adjustment. In this sense, legal changes, under international agreements, build an ethical mindset based on human rights that is progressively shared by populations. Education, training and social pedagogy for the community, their organizations and the media must be an ongoing and relentless social task to create a relational culture that is inclusive, rights-based and culturally sensitive.
Child trafficking results from the failure to protect children and safeguard their rights. It is thus a failure towards all children. Protection should be a systemic and ecological feature of the contexts of children’s lives. Additionally, the response to the various needs of each and every child should be the primary concern of children’s services and programmes. For this purpose, instead of a policymaking approach based on a patchwork of scattered and partial measures, we need effective and integrated public policies, particularly child and family policies, informed by rigorous and systematic research. Moreover, the proliferation of plans with goals insufficiently developed and evaluated should give way to a cohesive national anti-child trafficking approach.
The identification of the child victims of trafficking is a critical condition for the provision of the appropriate support. The gaps observed in most EU countries, including Portugal [20], led the experts to recommend the revision of the criteria and thresholds to be eligible for the child protection system. With respect to Portugal, the Protection Law, based on the distinction between risk and danger, excludes from the threshold for referral children in low-risk situations. In these conditions, the probability of not detecting potential victims of trafficking is not negligible [20]. Therefore, within a broader perspective of the concept of child protection, we endorse the revision of these thresholds and the consideration of different levels of intervention according to the level of risk identified.
Another crucial aspect of the intervention is the first service with which the child victims come into contact for the first time because it determines the subsequent provision made available to them. Regarding children at risk, the interaction among the services involved (e.g. health, education, Immigration and Borders Service, criminal justice, labour), should occur, in any situation, under the coordination of the Child Protection System, regardless of the specific status of the child (e.g. immigration status).
These changes will lead to the cultural shift [16] necessary to assign importance to the big and small issues of our time, regardless of the condition of the people affected, their age or number, so that a problem that affects other people is, by definition, a social problem.
This study had the financial support of Research Centre on Child Studies (CIEC), by the Strategic Project UID/CED/00317/2013, through the National Funds through the Foundation for Science and Technology (FCT) and co-financed by European Regional Development Funds (FEDER) through the Competitiveness and Internationalization Operational Program (POCI) with the reference POCI-01-0145-FEDER-007562.
The authors declare no conflict of interest.
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