Infection and aseptic loosening rate after TJA in hemophilia including current study [35-38,40-44]
\r\n\tThe aim of this book will be to describe the most common forms of dermatitis putting emphasis on the pathophysiology, clinical appearance and diagnostic of each disease. We also will aim to describe the therapeutic management and new therapeutic approaches of each condition that are currently being studied and are supposed to be used in the near future.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"278931ae110500350d8b64805c70f193",bookSignature:"Dr. Eleni Papakonstantinou",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7934.jpg",keywords:"Atopic eczema, Interleukin, Topical corticosteroids, Hand eczema, Blisters, Pruritus, Irritant contact dermatitis, Allergic contact dermatitis, Discoid eczema, Sebaceous glands, Inflammatory dermatitis, Facial rash",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"February 5th 2019",dateEndSecondStepPublish:"March 19th 2019",dateEndThirdStepPublish:"May 18th 2019",dateEndFourthStepPublish:"August 6th 2019",dateEndFifthStepPublish:"October 5th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"203520",title:"Dr.",name:"Eleni",middleName:null,surname:"Papakonstantinou",slug:"eleni-papakonstantinou",fullName:"Eleni Papakonstantinou",profilePictureURL:"https://mts.intechopen.com/storage/users/203520/images/system/203520.jpg",biography:"Dr. med. Eleni Papakonstantinou is a Doctor of Medicine graduate and board certified Dermatologist-Venereologist. She studied medicine at the Aristotle University of Thessaloniki, in Greece and she continued with her dermatology specialty in Germany (2012-2017) at the University of Magdeburg and Hannover Medical School, where she completed her dissertation in 2016 with research work on atopic dermatitis in children. During this time she gained wide experience in the whole dermatological field with special focus on the diagnosis and treatment of chronic inflammatory skin diseases and also the prevention and treatment of melanocytic and non-melanocytic skin tumors. Her research interests were beside atopic dermatitis and pruritus also the pathophysiology of blistering dermatoses. In addition to lectures at german and international congresses, she has published several articles in german and international journals and her work has been awarded with various prizes (poster prize of the German Dermatological Society for the project: 'Bullous pemphigoid and comorbidities' (DDG Leipzig 2016), 'Michael Hornstein Memorial Scholarship' (EADV Athens 2016), travel grant (EAACI Vienna 2016). Since 2017, she works as a specialist dermatologist in private practice in Dortmund, in Germany. Parallel she co-administrates an international dermatologic network, Wikiderm International and she writes a dermatology public guide for patients, as she is convinced that evidence-based knowledge has to be shared not only with colleagues but also with patients.",institutionString:"Private Practice, Dermatology and Venereology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"270941",firstName:"Sandra",lastName:"Maljavac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/270941/images/7824_n.jpg",email:"sandra.m@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"6550",title:"Cohort Studies in Health Sciences",subtitle:null,isOpenForSubmission:!1,hash:"01df5aba4fff1a84b37a2fdafa809660",slug:"cohort-studies-in-health-sciences",bookSignature:"R. Mauricio Barría",coverURL:"https://cdn.intechopen.com/books/images_new/6550.jpg",editedByType:"Edited by",editors:[{id:"88861",title:"Dr.",name:"R. Mauricio",surname:"Barría",slug:"r.-mauricio-barria",fullName:"R. 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Hemophilia A is caused by deficiency or absence of coagulation factor VIII and hemophilia B is caused by that of coagulation factor IX. The prevalence is reported as one in 5000 in the male population and one in 10000 overall. These diseases are classified into three categories according to serum coagulation factor activity; severe (<1%), moderate (1-5%), or mild (>5%). The particular hemophilic manifestation is intra-articular bleeding. Intra-articular bleeding is usually occurred by trauma but also often spontaneously. Approximately 5% of first bleeding episodes in hemophilic boys are into a joint. The average age of first intra-articular bleeding is 1.91±0.91 years old and the median age of that is 1.63 years old [1]. A joint in which four or more recurrent bleedings have occurred in the prior 6 months is defined as target joint. In the United States, 2.3% of children 2-5 years of age enrolled in the Universal Data Collection Project have target joints [2]. Most of target joints have hemophilic synovitis, which is characterized by inflammation, angiogenesis and fibrosis [3] and develop hemophilic arthroplasty which is characterized by cartilage and bone destruction.
\n\t\t\tThe average annual intra-articular bleeding number were higher in the episodic-therapy group than in the prophylaxis group and the relative risk of MRI-detected joint damage with the episodic was 6-fold greater compared with prophylactic group [4]. Therefore, it is a clear the relationship between intra-articular bleeding and hemophilic arthropathy. However, the number and volume of intra-articular bleedings which result in target joint and arthropathy is not understood. Experimental pathgenetic studies were reported and many points of the pathogenesis are still remained as poorly understood points [2,5,6].
\n\t\t\tAs for radiological evaluation methods of hemophilic arthropathy, there are three major systems. De Palma classification is classical progressive system and popular in Japan. This system classified hemophilic arthropathy from grade 0 (normal) to grade IV (end-stage) [7]. Arnold-Hilgartner classification is also classical progressive system and classified from stage 0 (normal) to stage V (end-stage) [8]. Pettersson score is recommend additive scoring system by World Federation of Hemophilia (WFH) [9]. 8 categories in this system have 0 (normal) to 2 (worse) points and totally scored from 0 (normal) to 13 points (worst). There are classical and authorized good systems but it may need to improve them, because their inter-observer reliabilities were poor [10].
\n\t\t\tAs for MRI evaluation methods, there were several reports since 2000 [11-13], and WFH recommended MRI scale and modified version were published from the international prophylaxis study group [14, 15].
\n\t\t\tThe causative mechanism of hemophilia was recognized in the 1950s [16], and concentrates for coagulation factor replacement became generally available since 1960s, however the easy administration of the concentrates resulted in transmission of viral infection including hepatitis C (HCV) and Human immunodeficiency virus infection (HIV) during the 1980s [17]. Also, the appearance of allo-immune antibody (inhibitor) against deficient coagulation factor is severe adversity. Product development such as recombinant concentrates has especially improved therapeutic safety and availability [18], resulting in the possibility of performing elective orthopedic surgery and prevention of bleeding episodes. The routine administration of prophylactic treatment has undoubtedly resulted in a greatly improvement in the quality of life and life expectancy of hemophilic patients. However, many of young hemophilic adults still have severe destructive joints as a result of repeated intra-articular bleeding during their early years.
\n\t\t\tFor hemophilic arthropathy, there are two major surgical options which are synovectomy for early stage (hemophilic synovitis) and total joint arthroplasty (TJA) for end stage. At progressive stage, there are not good surgical options so that several usual orthopedic options are tried: anti-inflammatory drugs, corticosteroids, joint infusion of hyaluronic acid or corticosteroids, braces and rehabilitation. However many of them are progressed to end-stage. Total joint arthroplasty (TJA) is effective procedure in the management of hemophilic arthropathy for them [19].
\n\t\tIn our hospital, 126 major surgeries for 80 patients have been performed between June 2006 and June 2012 in which were 96 surgeries for 63 hemophilia A, 28 surgeries for 16 hemophilia B, and 2 surgeries for 2 other coagulation disorders. 18 surgeries with inhibitor were included. The average age at operation was 39.03 years (13 ~ 60 years). As for virus infection, HBs antigen positive ratio was 1.5% (2/126 surgeries), HCV antibody positive ratio was 89.7% (113/126), HIV antibody positive ratio was 33.3% (42/126) and both HCV and HIV antibody positive ratio was 31.7% (41/126).
\n\t\t\tMajor surgery such as TJA is never easily undertaking in hemophilic patients. It required bleeding control at peri-operative periods, management for viral infection and inhibitor, and treatment for complication subsequently to bleeding.
\n\t\t\tAs for bleeding control, guidelines were published[20, 21], in which the aim serum factor level at peri-operative period is explained clearly. However it is difficult without the support of hematologists so that major surgeries are usually performed at hemophilia centers.
\n\t\t\tAs for HIV infection, CD4 cell counts had been important factor as major influence factor on bacterial infection in the early literatures [22-25]. HIV medical treatment has drastically improved during the last decade. In the recent literatures [26-29] and our experience, there was no evidence to suggest that bacterial deep infection at surgical site was influenced on the decline of CD4 cell counts. However HIV-positive patients whose CD4 cell counts is less than 50 cell/mm3 have a considerable risk of the occurrence of opportunistic infection at peri-operaive periods.
\n\t\t\tMost hemophilia adult patients infected hepatitis C virus at 1980s and long period of the virus carrier results in hepatic insufficiency and hepatoma. The treatment for chronic hepatitis has also improved, however it was not good enough to control it. According to our clinical experiences, severe hepatic insufficiency has been influenced on the fatal ratio after major surgery.
\n\t\t\tBetween 10-30% of patients with hemophilia A and 2-5% of patients with hemophilia B develop an inhibitor to FVIII or FIX [30]. Intra-articular bleedings in inhibitor patients have a more negative impact on their joint function and daily life. They desire to reduce the pain and improve function at affected joint. However surgical treatments may be deferred until patients suffer from increasingly severe pain and progressive physical incapacity find no other options, due to the higher bleeding risks associated with surgery [31]. In fact, there are some surgical reports for inhibitors, but many of them are a few cases reports [32]. Guidelines for inhibitor [33] were also published, but bleeding control plan at peri-operative periods is not established. We believe surgical treatments for inhibitor should be performed at hemophilic center.
\n\t\t\tDelayed wound healing is the major complication subsequently to bleeding. In hemophilia B mice, dermal wound healing is delayed and can be treated with factor IX replacement therapy to restore thrombin generation. This delay is associated with bleeding into granulation tissue [34]. In our hospital, there were 3.3% delayed wound healings.
\n\t\tTJA has been available as a final option in the end-stage hemophilic arthropathy with significantly reduced quality of life. The major objectives of TJA are to reduce the pain in the affected joint and improve the joint function. These effects are influenced on the adjacent joints beneficially. In addition, the frequency and number of the intra-articular bleeding is significantly reduced. Thereby, their life-style and quality of life significantly improved. The indications are adult hemophilia patients with severe destructive arthropathy and subjective dysfunction. Adult means the patient’s epiphyses are closed. We have performed total hip arthroplasty for 18 years boy. Their dysfunction have been started from childhood and made worse gradually. They are satisfied to live on their ability even if they have severe destructive arthropathies. These cases are not indication subjectively. Total knee or hip arthroplasty (TKA, THA) are generally performed and total elbow and ankle arthroplasty (TEA, TAA) are rarely performed. However the most common affected joints are elbow, knee and ankle. The average of operation is around forty [35, 36]. In our hospital, 81 total joint arthroplasty were performed included 59 TKAs, 20 THAs, 1TEA and 1TAA and average age were 44.5. (figure 1, 2)
\n\t\t\tA 18-year-old hemophilia A patient without inhibitor. No virus infection. Severe hemophilic arthropathy of the right knee. His knee range of motion was improved at four years after surgery: extension was -30 degrees to 0 degrees and flexion was 90 degrees to 110 degrees. (a): pre-operative radiography; (b) at one month radiography (c) at four year radiography
A 23-year-old hemophilia A patient with inhibitor. HCV is positive. Severe hemophilic arthropathy of the right hip. His back pain and tilted pelvis are improved after surgery. (a): pre-operative radiography; (b) just operative radiography (c) at five year radiography
As for surgical technique, we believe orthopedic surgeons do not need special skills when deficient factor level is kept with concentrate adequately. However, there are some careful points on TJA for hemophilia, in addition to the aforementioned surgical risks for hemophilia, which are higher infection rate, higher revision rate or shorter durability (table 1), and the occurrence of deep venous thrombosis (DVT).
\n\t\t\t\n\t\t\t\t\t\tAuthers | \n\t\t\t\t\t\tYear | \n\t\t\t\t\t\tNumber ofTJA | \n\t\t\t\t\t\tAverage ofFollow-up(years) | \n\t\t\t\t\t\tInfectionrate(%) | \n\t\t\t\t\t\tAseptic looseningrate(%) | \n\t\t\t\t\t
Cohen et al | \n\t\t\t\t\t\t2000 | \n\t\t\t\t\t\t21 TKAs | \n\t\t\t\t\t\t5.6 | \n\t\t\t\t\t\t10 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t
Norian et al | \n\t\t\t\t\t\t2002 | \n\t\t\t\t\t\t53 TKAs | \n\t\t\t\t\t\t5 | \n\t\t\t\t\t\t13.2 | \n\t\t\t\t\t\t9.4 | \n\t\t\t\t\t
Sheth et al | \n\t\t\t\t\t\t2004 | \n\t\t\t\t\t\t14 TKAs | \n\t\t\t\t\t\t6.4 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t
Goddard et al | \n\t\t\t\t\t\t2010 | \n\t\t\t\t\t\t70 TKAs | \n\t\t\t\t\t\t9.2 | \n\t\t\t\t\t\t1.5 | \n\t\t\t\t\t\t8.6 | \n\t\t\t\t\t
Habermann et al | \n\t\t\t\t\t\t2007 | \n\t\t\t\t\t\t15 THAs | \n\t\t\t\t\t\t11 | \n\t\t\t\t\t\t6.7 | \n\t\t\t\t\t\t6.7 | \n\t\t\t\t\t
Miles et al | \n\t\t\t\t\t\t2008 | \n\t\t\t\t\t\t34 THAs | \n\t\t\t\t\t\t6.3 | \n\t\t\t\t\t\t3 | \n\t\t\t\t\t\t9 | \n\t\t\t\t\t
Yoo et al | \n\t\t\t\t\t\t2009 | \n\t\t\t\t\t\t23 THAs | \n\t\t\t\t\t\t7.7 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t\t4.8 | \n\t\t\t\t\t
Powell et al | \n\t\t\t\t\t\t2005 | \n\t\t\t\t\t\t35 TKAs 16 THAs | \n\t\t\t\t\t\t6.9 | \n\t\t\t\t\t\tTKA:14.3 THA:6.3 | \n\t\t\t\t\t\t0 | \n\t\t\t\t\t
Wang et al | \n\t\t\t\t\t\t2012 | \n\t\t\t\t\t\t40 TKAs 18 THAs | \n\t\t\t\t\t\t10.7 | \n\t\t\t\t\t\tTKA:13 THA:0 | \n\t\t\t\t\t\tTKA:2.5 THA:5.6 | \n\t\t\t\t\t
As for the infection after TJA, most reports show a deep infection rate of 10-16% [35-41], and our result shows much lower infection rate of 2.5 (2/81). However, these rates are higher than infection rate of 1-2% consistently reported in the literatures in the general population [45]. HIV infection had been important factor as major influence factor on bacterial infection, however the recent clinical results suggest there is no difference in the infection rate between HIV positive and negative. It is unclear why the ratio after TJA for hemophilia is much higher than that for non-hemophilia. The hypothesis that much frequent venous self-infusion is influenced on highly infection rate [40] is one of possibilities, but there is no evidence as yet.
\n\t\t\tAs for durability, there are a few long clinical reports. When revision for aseptic mechanical failure was considered as endpoint, the survival rate for 40 TKAs at 10 years was 93%. The survival rate for 18 THAs at 8.5 years was 89% [36]. According to clinical result for 60 TKAs at a mean follow-up of 9.2 years, Kaplan-Meier analysis using infection and aseptic loosening as endpoint showed the survival rate at 20 years to be 94.0% [35]. These survival rates were similar to that in young non-hemophilia patients [46,47], however we believe it is not good enough rate for young hemophilic patients who have multi-arthropathy.
\n\t\t\tHemophilia patients are often considered that their risk of DVT is lower by virtue of their bleeding disorder. However, they have as same risk of DVT as non-hemophilic patients, because the coagulation factor level is normalized by administration of concentrates at peri-operative periods. In our hospital, all patients use compression devices and not administrate drugs such as heparin or aspirin. Thrombosis has been checked by ultrasound and not detected at pre- and post-operation. Subclinical DVT was observed in 10% of hemophilia patients undergoing major orthopedic surgery [48]. According to the simple questionnaires survey at hemophilia treatment center in the United States, 78% provided thrombo-prophylaxis to selected patients. Of those providing of thrombo-prophylaxis, 67% used compression stocking or devices, 24% used low molecular weight heparin, 1% fondaparinux, 3% unfractionated heparin. 4% warfarin and 1% aspirin [49].
\n\t\t\tFinally, the cost of hemophilia treatment is major economical concern. The concentrate cost is occupied of major part of TJA cost and it is depended on the patient body weight. And the price of concentrates and insurance situation are quite differences internationally, so that we introduced our situation in this chapter. The cost is too expensive to performed surgery without insurance coverage, however Japanese general health insurance is covered with most of the cost, fortunately. The cost of TJA at peri-operative periods about two weeks is forty to fifty thousand dollars for average Japanese hemophilic patients without inhibitor (50-70kg). The cost for inhibitor cases is about 5 to 10-folds.
\n\t\t\tTotal joint arthroplasty for hemophilia is a challenging surgery and never a simple undertaking, however hemophilia patient need to improve their life style and release severe pain. We believe it is a safe and effective procedure in the management of hemophilic arthropathy at hemophilia centers.
\n\t\tThe evaluation of liquefaction-induced settlements has become an extremely significant issue about the foundations of different buildings, nuclear power plants, and earth dams on sandy soil deposits. Saturated sand deposits when are endured during an earthquake, pore water pressures are known to develop contributing to liquefaction or loss of shear strength. The pore water pressure then begin to dissipate primarily towards the ground surface, followed by a change in the volume of soil deposits which is manifested on the ground surface as settlements. Settlements caused by liquefaction are conventionally predicted using analytical or numerical methods.
Tokimatsu and Seed [1] developed a technique for predicting ground post liquefaction settlements based on volumetric strain, SPT N-value and cyclic stress ratio (CSR) relationships in the case of completely liquefied saturated sands transformed from an experimental relationship between relative sand density, volumetric strain, and maximum shear strain. Ishihara and Yoshimine [2] used an alternative approach to estimate ground settlements based on the safety factor, by means of the maximum shear strain which is an essential factor affecting the post-liquefaction volumetric strain. The liquefaction-induced settlement during the earthquake can be identified if the safety factor and relative density are established. Furthermore, the simplified method was constructed only by a relation between relative density, the factor of safety against liquefaction (FS) and volumetric strain (εv) to quantify the settlement of a site where the safety factor of safety against liquefaction was obtained By combining earthquake intensity and SPT N-value with empirical equations to cause measurement error and lead to significant prediction error [3].
Analytical method used to assess liquefaction-induced settlements is based on the effective stress analysis of dynamic response which accounts for the generation and dissipation of excess pore water pressures. When used to evaluate post-liquidation settlements in saturated sand deposits, the volume compressibility coefficient of the sand is required which is very difficult to determine for the liquefied sand layer [4]. Shamoto et al., [4] suggested a simplified approach for estimating liquefaction-induced settlements of saturated sand deposits, based on the experimental evidence that there is an almost linear relationship between the function of the void ratio and the logarithm of the maximum shear strain induced during cyclic loading.
In numerical analysis, earthquake-induced liquefaction in the free-field may be interpreted as a 1D phenomenon occurring along a vertical soil column in which seismic-induced cyclic shear and compressive forces increase the pore pressure and hence cause a reduction in the transient soil strength and stiffness. Reconsolidation arises in the soil after liquefaction due to the dissipation of the excess pore pressure (∆u) by means of water flow, resulting in the vertical settlement of the ground surface [5].
Park et al. [6] established a simple and sustainable method for predicting liquefaction-induced settlement using ANN. Tang et al. [3] found that the ANN and Bayesian Belief Networks (BBN) predictive outcomes are better than the Ishihara and Yoshimine simplified approach.
Pohang earthquake (Mw = 5.4) that hit the Heunghae Basin around Pohang city had a liquefaction-induced damages—settlement and lateral displacement. In this study liquefaction-induced settlement is considered as a case of illustration. Several efforts have been made since the event to evaluate the post-earthquake damages [7, 8, 9, 10, 11]. Nevertheless, the liquefaction-induced settlement has received little attention. Settlement caused by liquefaction is commonly calculated by taking into account various factors and following several sophisticated analytical and numerical procedures. Nevertheless, in most cases it may not be possible to acquire such parameters in the field, as some of the required data may not be obtainable. The main purpose of this study is to evaluate liquefaction-induced settlement based on the database of field observations. To achieve this purpose, the random forest and REP tree techniques are used to develop two new models for evaluation of liquefaction-induced settlement. Although these techniques have been successfully applied in many domains, the application in geotechnical earthquake engineering is limited based on the literature surveys.
The remainder of this chapter is organized as follows: Section 2 briefly provides the description of data acquisition for liquefaction-induced settlement calculation. Section 3 presents the methodology used to evaluate settlement caused due to earthquakes; an overview of the random forest and Rep tree techniques. Section 4 presents the development of the liquefaction-induced settlement models. Detailed results of the proposed models are discussed by performance evaluation measures are presented in Section 5, followed by conclusions in Section 6.
In this study, Park et al. [6] collected database from the Integrated DB Centre of National Geotechnical Information, Korea [12] and the UBCSAND constitutive effective stress model [13] was used to develop predictive models. SPT data were obtained for five different borehole sites near the epicenter of the earthquake at Pohang. The input parameters for the RF and REP Tree models are depth (m), unit weight (kN/m3), corrected SPT blow count (N1(60)) and cyclic stress ratio (CSR) and the output is the observed settlement (mm). For details about the database, readers can refer to Park et al. [6]. The summary of the data base comprised 100 data points (20 data for each borehole) along with the corresponding settlement values is shown in Table 1.
Borehole | Depth (m) | Unit Weight (kN/m3) | N1(60) | CSR | Settlement (mm) |
---|---|---|---|---|---|
BH-A-1 | 1 | 20 | 11 | 0.33 | 0.5 |
2 | 20 | 11 | 0.31 | 0.5 | |
3 | 20 | 14 | 0.29 | 0.8 | |
4 | 20 | 16 | 0.28 | 1.4 | |
5 | 20 | 5 | 0.27 | 3.3 | |
6 | 20 | 10 | 0.26 | 3.4 | |
7 | 20 | 5 | 0.27 | 3.4 | |
8 | 20 | 6 | 0.29 | 2.5 | |
9 | 20 | 9 | 0.3 | 1.6 | |
10 | 20 | 9 | 0.31 | 1 | |
11 | 18 | 25 | 0.31 | 0.4 | |
12 | 18 | 25 | 0.31 | 0.3 | |
13 | 18 | 25 | 0.32 | 0.2 | |
14 | 18 | 25 | 0.32 | 0.3 | |
15 | 18 | 25 | 0.32 | 0.3 | |
16 | 18 | 25 | 0.32 | 0.3 | |
17 | 18 | 25 | 0.32 | 0.3 | |
18 | 18 | 25 | 0.32 | 0.2 | |
19 | 18 | 25 | 0.31 | 0.3 | |
20 | 18 | 25 | 0.31 | 0.3 | |
BH-A-2 | 1 | 20 | 15 | 0.35 | 0.4 |
2 | 20 | 17 | 0.32 | 0.8 | |
3 | 20 | 17 | 0.3 | 1.6 | |
4 | 20 | 7 | 0.29 | 3.1 | |
5 | 20 | 6 | 0.27 | 2.8 | |
6 | 21 | 13 | 0.31 | 1.4 | |
7 | 21 | 18 | 0.34 | 0.8 | |
8 | 21 | 13 | 0.36 | 0.9 | |
9 | 21 | 11 | 0.37 | 1.1 | |
10 | 21 | 13 | 0.36 | 0.8 | |
11 | 16 | 2 | 0.36 | 0 | |
12 | 16 | 1 | 0.37 | 0 | |
13 | 16 | 1 | 0.38 | 0 | |
14 | 16 | 1 | 0.39 | 0 | |
15 | 16 | 1 | 0.39 | 0 | |
16 | 16 | 1 | 0.39 | 0 | |
17 | 16 | 1 | 0.39 | 0 | |
18 | 16 | 1 | 0.39 | 0 | |
19 | 16 | 1 | 0.38 | 0 | |
20 | 16 | 1 | 0.37 | 0 | |
BH-A-3 | 1 | 18 | 6 | 0.24 | 0.6 |
2 | 18 | 8 | 0.28 | 1.4 | |
3 | 18 | 10 | 0.3 | 2 | |
4 | 18 | 10 | 0.29 | 2.3 | |
5 | 18 | 11 | 0.28 | 2 | |
6 | 18 | 10 | 0.3 | 1.8 | |
7 | 18 | 11 | 0.32 | 1.4 | |
8 | 18 | 11 | 0.33 | 1.3 | |
9 | 18 | 12 | 0.34 | 1.2 | |
10 | 18 | 13 | 0.34 | 1 | |
11 | 21 | 25 | 0.34 | 0.7 | |
12 | 21 | 25 | 0.33 | 0.6 | |
13 | 21 | 25 | 0.33 | 0.6 | |
14 | 21 | 25 | 0.33 | 0.5 | |
15 | 21 | 25 | 0.32 | 0.5 | |
16 | 21 | 25 | 0.32 | 0.4 | |
17 | 21 | 25 | 0.31 | 0.5 | |
18 | 21 | 25 | 0.31 | 0.4 | |
19 | 21 | 25 | 0.3 | 0.4 | |
20 | 21 | 25 | 0.3 | 0.5 | |
BH-A-4 | 1 | 20 | 5 | 0.23 | 1.1 |
2 | 20 | 7 | 0.27 | 1.9 | |
3 | 20 | 18 | 0.27 | 1.6 | |
4 | 20 | 9 | 0.27 | 2.8 | |
5 | 20 | 6 | 0.26 | 2.8 | |
6 | 20 | 11 | 0.31 | 1.6 | |
7 | 20 | 9 | 0.34 | 1.4 | |
8 | 21 | 25 | 0.36 | 0.6 | |
9 | 21 | 25 | 0.38 | 0.6 | |
10 | 21 | 25 | 0.38 | 0.6 | |
11 | 21 | 25 | 0.38 | 0.6 | |
12 | 21 | 25 | 0.37 | 0.5 | |
13 | 16 | 7 | 0.22 | 0 | |
14 | 16 | 1 | 0.21 | 0 | |
15 | 16 | 0 | 0.21 | 0 | |
16 | 16 | 2 | 0.22 | 0 | |
17 | 16 | 3 | 0.22 | 0 | |
18 | 16 | 3 | 0.22 | 0 | |
19 | 16 | 3 | 0.22 | 0 | |
20 | 16 | 3 | 0.22 | 0 | |
BH-A-5 | 1 | 20 | 11 | 0.32 | 0.5 |
2 | 20 | 10 | 0.31 | 1.6 | |
3 | 20 | 9 | 0.29 | 2.6 | |
4 | 20 | 11 | 0.3 | 1.9 | |
5 | 20 | 11 | 0.32 | 1.5 | |
6 | 20 | 10 | 0.33 | 1.4 | |
7 | 20 | 15 | 0.34 | 0.9 | |
8 | 20 | 15 | 0.35 | 0.8 | |
9 | 21 | 25 | 0.34 | 0.6 | |
10 | 21 | 25 | 0.34 | 0.6 | |
11 | 21 | 25 | 0.34 | 0.6 | |
12 | 21 | 25 | 0.33 | 0.6 | |
13 | 21 | 25 | 0.33 | 0.7 | |
14 | 21 | 25 | 0.33 | 0.7 | |
15 | 18 | 15 | 0.33 | 1.1 | |
16 | 18 | 11 | 0.33 | 1.4 | |
17 | 18 | 12 | 0.32 | 1 | |
18 | 16 | 14 | 0.32 | 0.1 | |
19 | 16 | 10 | 0.32 | 0 | |
20 | 16 | 7 | 0.32 | 0 |
Summary of liquefaction-induced settlement database.
Note: Borehole (BH-A-5) data comprised of 20 data points is used as testing dataset in this study.
Random Forest (RF) is an ensemble machine learning technique driven by the development of a large number of decision trees that is produced by Leo Breiman [14]. Unlike DT, which uses all the features to construct a tree-like classification graph, RF uses an “efficient bagging” learning algorithm which integrates random selection of features with bagging. If one or a few features are very good predictors for target performance, it will pick this subset of features to construct a tree-like graph. This type of sample is known as the Bootstrap Sample. Using bagging techniques, these models are fitted with the above bootstrap samples, and then combined by voting. RF improves reliability and precision, reduces uncertainty and helps avoid overfitting.
Bootstrap aggregation or bagging is used to determine an appropriate number of trees with the size and nature of the training set. The RF prediction can be expressed as: by averaging the predictions from the individual regression trees;
An optimal number of trees are calculated by bootstrap aggregation or bagging with the size and nature of the training set. By averaging the predictions from the individual regression trees; The RF prediction can be expressed as:
where
Figure 1 demonstrates the method of classifying RF with the N trees. Starting from the root node (νn), after comparison with certain parameters or threshold values, samples are moved to the right node (νR) or the left node (νL). Repeat this partition until a terminal node is reached and get a classification tag (in this case, classes A or B). For classification task, the ensemble prediction is achieved by majority voting rule as a combination of the results of the individual trees [15].
Schematic representation of a RF classifier with N trees.
The reduced error pruning tree (REP Tree) is an ensemble model of decision tree (DT) and The REP Tree (Reduced Error Pruning Tree) is an ensemble model of decision tree (DT) and reduced error pruning (REP) algorithms, equally good for classification and regression problems [16]. The REP Tree algorithm generates a decision regression tree by dividing and pruning the regression tree based on the importance of the highest knowledge benefit ratio (IGR) [17]; The IGR values were determined via Eq. (3) based on the entropy (E) function.
The IGR considers all the predictors of liquefaction-induced settlement with subset Si from the training dataset (S): i = 1, 2,. .., n successive pruning steps. Since complex decision trees can result in a model being overfitted and less interpretable, REP helps to reduce complexity by removing the DT structure’s leaves and branches [16, 18, 19, 20].
The manner in which data are divided into training and test data sets in data mining procedures has a substantial effect on the results [21, 22, 23]. The statistical parameters for the input variables include the minimum, maximum, mean and standard deviation of the training and test datasets, as shown in Table 2. Data set splitting was done to assess the generalization efficiency and predictive ability of the developed models. The related performance of the training and testing datasets suggests that the developed models can be applied to the trained ranges. In the testing the ranges of input and output parameters often occur in the training datasets as shown in Table 2. The training and testing datasets’ statistical consistency enhances the performance of the developed models and thus helps to properly assess them.
Dataset | Statistical parameter | Depth (m) | Unit Weight (kN/m3) | N1(60) | CSR | Settlement (mm) |
---|---|---|---|---|---|---|
Training | Minimum | 1 | 16 | 0 | 0.21 | 0 |
Maximum | 20 | 21 | 25 | 0.39 | 3.4 | |
Mean | 10.50 | 18.85 | 13.14 | 0.31 | 0.89 | |
Standard deviation | 5.80 | 1.89 | 9.14 | 0.05 | 0.92 | |
Testing | Minimum | 1 | 16 | 7 | 0.29 | 0 |
Maximum | 20 | 21 | 25 | 0.35 | 2.6 | |
Mean | 10.5 | 19.4 | 15.55 | 0.3255 | 0.93 | |
Standard deviation | 5.92 | 1.76 | 6.66 | 0.01 | 0.65 |
Statistical parameters of the training and testing datasets.
To ensure comparability, the RF and REP Tree models are proposed using the same training and test datasets. Using these models, liquefaction-induced settlements are predicted, and an analysis of the detailed performance of these models will find the optimum model afterwards. If the performance of this model on the training and test datasets is adequate then it can be adopted for development.
In this study, three evaluation measures, mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (r) are used to evaluate and compare the performance of the models. The MAE, RMSE and r are three useful statistical measures which provide some useful insights into the prediction model, of which the MAE is an average of the sum of the differences between the values predicted by a model and the actual values, the RMSE is a standard deviation of the differences, and the correlation coefficient (r) is a statistical measure representing the percentage of the variance for a model a dependent variable that’s described by an independent variable, and their expressions are as follows [24]:
where
Theoretically, a specific model can be obtained when the model parameters are correctly selected and updated. The optimum values are obtained by trial and error using parameter setting. The optimum value for each machine learning parameter is illustrated in Table 3. In the proposed RF and REP Tree models the most significant parameters are the number of seeds and the minimum total weight of instances in a leaf during the modeling process.
Algorithm | Parameters |
---|---|
RF | Minimum total weight of instances in a leaf: 1; minimum portion of the variance of all the data to be present in a node to be split in regression tress: 0.001; random number seed used to pick attributes: 1; K value: 0 |
REP Tree | Maximum tree depth: −1; minimum total instance weight in the leaf: 2; minimum likelihood of variance: 0.001; fold number: 3; seed number: 1 |
Model optimum modeling parameters.
The RF and REP Tree predictive results were obtained from the datasets for training and testing datasets. The MAE, RMSE and correlation coefficient (r) were subsequently determined on the basis of the Eqs. (4)–(6) shown in Figure 2 that depicts RF and REP Tree models performance, respectively. For the RF model the training data prediction is higher than the test dataset prediction. The r values for the training data and testing data are found 0.9935 and 0.8833, respectively. For the REP Tree model, the training data r value (= 0.9405) indicates marginally better results than that for the testing data (= 0.777). It is obvious to judge that the performance of RF model in training and testing datasets is higher than that of REP Tree model. Figure 2 presents bar graphs comparing the mean absolute error (MAE), the root mean squared error (RMSE), and the correlation coefficient (r) for both models’ training and test datasets. The MAE calculates the variance in the error term by term and reduces the significance of large errors; the RMSE value is more concentrated on large errors than on small ones. The RF model has lower MAE and RMSE values while higher r value, showing that in both training and testing datasets, the RF model provides adequate prediction of liquefaction-induced settlement. Additionally, the results of training and testing were shown in \tFigures 3 and 4, showing the projected settlements are plotted with the actual data. One can see that settlements were predicted more accurately by the RF model than by the REP Tree model. While the REP Tree model few settlements cases are relatively under predicted as compared to the RF model.
Comparison of MAE, RMSE, and r values from the RF and REP tree models.
Training and testing of the RF model.
Training and testing of the REP tree model.
This paper explores the potential of RF and REP Tree models for predicting liquefaction-induced settlement using field data. The models were trained and tested based on the Pohang city liquefaction-induced settlement database. Both models assess liquefaction-induced settlement with substantial contributing factors such as depth, unit weight, corrected SPT blow count and cyclic stress ratio. The performance of the models presented is measured using statistical parameters such as the correlation coefficient (r), MAE, and RMSE. The RF model indicates a better performance with respect to the training and testing datasets. From this analysis it can be inferred that the RF model works well in predicting liquefaction-induced settlement as opposed to the REP Tree model. Since, artificial intelligence-based approaches are data-dependent and their output can vary depending on the dataset, the quality and number of training datasets and the size of the experiments. Finally, it is obvious that the proposed models are open to develop and accumulation of more data will provide much better evaluation of liquefaction-induced settlements.
The work presented in this paper was part of the research sponsored by the Key Program of National Natural Science Foundation of China under Grant No. 51639002 and National Key Research and Development Plan of China under Grant No. 2018YFC1505300-5.3.
The authors declare no conflict of interest.
IntechOpen publishes different types of publications
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