Numeric values adopted for the phase coefficient CD [7].
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"5782",leadTitle:null,fullTitle:"Clinical Physical Therapy",title:"Clinical Physical Therapy",subtitle:null,reviewType:"peer-reviewed",abstract:"Physical therapy services may be provided alongside or in conjunction with other medical services. 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Dr. Franco Abuín is an expert in food science and food safety; a member of the Spanish Agency for Food Safety and Nutrition; and holder of diverse registered patents.",coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"194841",title:"Dr.",name:"Alexandre",middleName:null,surname:"Lamas",slug:"alexandre-lamas",fullName:"Alexandre Lamas",profilePictureURL:"https://mts.intechopen.com/storage/users/194841/images/system/194841.jpg",biography:"Alexandre Lamas is a postdoctoral researcher from the University of Santiago de Compostela (USC). He graduated in veterinary medicine from the USC and obtained his PhD in Innovation in Food Safety and Technology at the same university in 2019. That same year he was awarded a competitive postdoctoral scholarship. He is co-author of over 30 articles in prestigious international journals and 6 book chapters. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"38737",title:"An Analysis of the Beam-to-Beam Connections Effect and Steel-Concrete Interaction Degree Over the Composite Floors Dynamic Response",doi:"10.5772/51672",slug:"an-analysis-of-the-beam-to-beam-connections-effect-and-steel-concrete-interaction-degree-over-the-co",body:'\n\t\tNowadays steel and composite (steel-concrete) building structures are more and more becoming the modern landmarks of urban areas. Designers seem to continuously move the safety border, in order to increase slenderness and lightness of their structural systems. However, more and more steel and composite floors are carried out as light weight structures with low frequencies and low damping. These facts have generated very slender composite floors, sensitive to dynamic excitation, and consequently changed the serviceability and ultimate limit states associated to their design.
\n\t\t\tA direct consequence of this new design trend is a considerable increase in problems related to unwanted composite floor vibrations. For this reason, the structural floors systems become vulnerable to excessive vibrations produced by impacts such as human rhythmic activities. On the other hand, the increasing incidence of building vibration problems due to human activities led to a specific design criterion to be addressed in structural design [1-7]. This was the main motivation for the development of a design methodology centred on the steel-concrete composite floors non-linear dynamic response submitted to loads due to human rhythmic activities.
\n\t\t\tConsidering all aspects mentioned before, the main objective of this paper is to investigate the beam-to-beam connections effect (rigid, semi-rigid and flexible) and the influence of steel-concrete interaction degree (from total to various levels of partial interaction) over the non-linear dynamic behaviour of composite floors when subjected to human rhythmic activities [1,2]. This way, the dynamic loads were obtained through experimental tests with individuals carrying out rhythmic and non-rhythmic activities such as stimulated and non-stimulated jumping and aerobic gymnastics [7]. Based on the experimental results, human load functions due to rhythmic and non-rhythmic activities are proposed [7].
\n\t\t\tThe investigated structural model was based on a steel-concrete composite floor spanning 40m by 40m, with a total area of 1600m2. The structural system consisted of a typical composite floor of a commercial building. The composite floor studied in this work is supported by steel columns and is currently submitted to human rhythmic loads. The structural system is constituted of composite girders and a 100mm thick concrete slab [1,2].
\n\t\t\tThe proposed computational model adopted the usual mesh refinement techniques present in finite element method simulations, based on the ANSYS program [8]. This numerical model enabled a complete dynamic evaluation of the investigated steel-concrete composite floor especially in terms of human comfort and its associated vibration serviceability limit states.
\n\t\t\tInitially, all the composite floor natural frequencies and vibration modes were obtained. In sequence, based on an extensive parametric study, the floor dynamic response in terms of peak accelerations was obtained and compared to the limiting values proposed by several authors and design codes [6,9]. An extensive parametric analysis was developed focusing in the evaluation of the beam-to-beam connections effect and the influence of steel-concrete interaction degree over the investigated composite floor non-linear dynamic response, when submitted to human rhythmic activities.
\n\t\t\tThe structural system peak accelerations were compared to the limiting values proposed by several authors and design standards [6,9]. The current investigation indicated that human rhythmic activities could induce the steel-concrete composite floors to reach unacceptable vibration levels and, in these situations, lead to a violation of the current human comfort criteria for these specific structures.
\n\t\tThe description of the dynamic loads generated by human activities is not a simple task. The individual characteristics in which each individual perform the same activity and the existence of external excitation are key factors in defining the dynamic action characteristics. Numerous investigations were made aiming to establish parameters to describe such dynamic actions [1-6].
\n\t\t\tSeveral investigations have described the loading generated by human activities as a Fourier series, which consider a static part due to the individual weight and another part due to the dynamic load [1-6]. The dynamic analysis is performed equating one of the activity harmonics to the floor fundamental frequency, leading to resonance.
\n\t\t\tThis study have considered the dynamic loads obtained by Faisca [7], based on the results achieved through a long series of experimental tests with individuals carrying out rhythmic and non-rhythmic activities. The dynamic loads generated by human rhythmic activities, such as jumps, aerobics and dancing were investigated by Faisca [7].
\n\t\t\tThe loading modelling was able to simulate human activities like aerobics, dancing and free jumps. In this paper, the Hanning function was used to represent the human dynamic actions. The Hanning function was used since it was verified that this mathematical representation is very similar to the signal force obtained through experimental tests developed by Faisca [7].
\n\t\t\tThe mathematical representation of the human dynamic loading using the Hanning function is given by Equation (1) and illustrated in Figure 1. The required parameters for the use of Equation (1) are related to the activity period, T, contact period with the structure, Tc, period without contact with the model, Ts, impact coefficient, Kp, and phase coefficient, CD. Figure 2 and the Table 1 illustrate the phase coefficient variation, CD, for human activities studied by Faisca [7], considering a certain number of individuals and later extrapolated for large number of peoples. Table 2 presents the experimental parameters used for human rhythmic activities representation and Figure 3 presents examples of dynamic action related to human rhythmic activities investigated in this work.
\n\t\t\tWhen\n\t\t\t\n\t\t\t\t\t
When\n\t\t\t\n\t\t\t\t\t
Where:
\n\t\t\tF(t): dynamic loading (N);
\n\t\t\tt: time (s);
\n\t\t\tT: activity period (s);
\n\t\t\tTc: activity contact period (s);
\n\t\t\tP: person’s weight (N);
\n\t\t\tKp: impact coefficient;
\n\t\t\tCD: phase coefficient
\n\t\t\tRepresentation of the dynamic loading induced by human rhythmic activities.
Variation of the phase coefficient CD for human rhythmic activities [7].
People number | \n\t\t\t\t\t\tAerobics gymnastics | \n\t\t\t\t\t\tFree Jumps | \n\t\t\t\t\t
1 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t
3 | \n\t\t\t\t\t\t1 | \n\t\t\t\t\t\t0.88 | \n\t\t\t\t\t
6 | \n\t\t\t\t\t\t0.97 | \n\t\t\t\t\t\t0.74 | \n\t\t\t\t\t
9 | \n\t\t\t\t\t\t0.96 | \n\t\t\t\t\t\t0.70 | \n\t\t\t\t\t
12 | \n\t\t\t\t\t\t0.95 | \n\t\t\t\t\t\t0.67 | \n\t\t\t\t\t
16 | \n\t\t\t\t\t\t0.94 | \n\t\t\t\t\t\t0.64 | \n\t\t\t\t\t
24 | \n\t\t\t\t\t\t0.93 | \n\t\t\t\t\t\t0.62 | \n\t\t\t\t\t
32 | \n\t\t\t\t\t\t0.92 | \n\t\t\t\t\t\t0.60 | \n\t\t\t\t\t
Numeric values adopted for the phase coefficient CD [7].
Activity | \n\t\t\t\t\t\tT (s) | \n\t\t\t\t\t\tTc (s) | \n\t\t\t\t\t\tKp\n\t\t\t\t\t\t | \n\t\t\t\t\t
Free Jumps | \n\t\t\t\t\t\t0.44±0.15 | \n\t\t\t\t\t\t0.32±0.09 | \n\t\t\t\t\t\t3.17±0.58 | \n\t\t\t\t\t
Aerobics | \n\t\t\t\t\t\t0.44±0.09 | \n\t\t\t\t\t\t0.34±0.09 | \n\t\t\t\t\t\t2.78±0.60 | \n\t\t\t\t\t
Show | \n\t\t\t\t\t\t0.37±0.03 | \n\t\t\t\t\t\t0.37±0.03 | \n\t\t\t\t\t\t2.41±0.51 | \n\t\t\t\t\t
Experimental parameters used for human rhythmic activities representation [7].
Dynamic loading induced by human rhythmic activities.
The investigated structural model was based on a steel-concrete composite floor spanning 40m by 40m, with a total area of 1600m2. The structural system consisted of a typical composite floor of a commercial building. The floor studied in this work is supported by steel columns and is currently submitted to human rhythmic loads. The model is constituted of composite girders and a 100mm thick concrete slab [1,2], see Figures 4 and 5.
\n\t\t\tThe steel sections used were welded wide flanges (WWF) made with a 345MPa yield stress steel grade. A 2.05x105MPa Young’s modulus was adopted for the steel beams. The concrete slab has a 30MPa specified compression strength and a 2.6x104 MPa Young’s Modulus. Table 3 depicted the geometric characteristics of the steel beams and columns.
\n\t\t\tStructural model: composite floor (steel-concrete). Dimensions in (mm).
Cross section of the generic models. Dimensions in (mm).
Profile Type | \n\t\t\t\t\t\tHeight(d) | \n\t\t\t\t\t\tFlangeWidth(bf) | \n\t\t\t\t\t\tTop FlangeThickness(tf) | \n\t\t\t\t\t\tBottom FlangeThickness(tf) | \n\t\t\t\t\t\tWebThickness(tw) | \n\t\t\t\t\t
Main Beams (W610x140) | \n\t\t\t\t\t\t617 | \n\t\t\t\t\t\t230 | \n\t\t\t\t\t\t22.2 | \n\t\t\t\t\t\t22.2 | \n\t\t\t\t\t\t13.1 | \n\t\t\t\t\t
Secondary Beams (W460x60) | \n\t\t\t\t\t\t455 | \n\t\t\t\t\t\t153 | \n\t\t\t\t\t\t13.3 | \n\t\t\t\t\t\t13.3 | \n\t\t\t\t\t\t8.0 | \n\t\t\t\t\t
Columns (HP250x85) | \n\t\t\t\t\t\t254 | \n\t\t\t\t\t\t260 | \n\t\t\t\t\t\t14.4 | \n\t\t\t\t\t\t14.4 | \n\t\t\t\t\t\t14.4 | \n\t\t\t\t\t
Geometric characteristics of the building composite floor (mm).
The human-induced dynamic action was applied on the aerobics area, see Figure 6. The composite floor dynamic response, in terms of peak accelerations values, were obtained on the nodes A to H, in order to verify the influence of the dynamic loading on the adjacent slab floors, as illustrated in Figure 8. In this investigation, the dynamic loadings were applied to the structural model corresponding to the effect of thirty two individuals practising aerobics.
\n\t\t\tThe live load considered in this analysis corresponds to one person for each 4.0m2 (0.25 person/m2), according to reference [5]. The load distribution was considered symmetrically centred on the slab panels, as depicted in Figure 8. It is also assumed that an individual person weight is equal to 800N (0.8kN) [5]. In this study, the damping ratio, ξ=1% (ξ = 0.01) was considered for all cases [5].
\n\t\t\tDynamic loading: thirty two individuals practising aerobics on the investigated floor.
The proposed computational model, developed for the composite floor dynamic analysis, adopted the usual mesh refinement techniques present in finite element method simulations implemented in the ANSYS program [8]. The present investigation considered that both materials (steel and concrete) have an elastic behaviour. The finite element model is illustrated in Figure 7.
\n\t\t\tIn this computational model, all “I” steel sections, related to beams and columns, were represented by three-dimensional beam elements (BEAM44 [8]) with tension, compression, torsion and bending capabilities. These elements have six degrees of freedom at each node: translations in the nodal x, y, and z directions and rotations about x, y, and z axes, see Figure 8.
\n\t\t\tOn the other hand, the reinforced concrete slab was represented by shell finite elements (SHELL63 [8]). This finite element has both bending and membrane capabilities. Both in-plane and normal loads are permitted. The element has six degrees of freedom at each node: translations in the nodal x, y, and z directions and rotations about the nodal x, y, and z axes, see Figure 8.
\n\t\t\tSteel-concrete composite floor finite element model mesh and layout.
Finite elements used in the computational modelling.
The structural behaviour of the beam-to-beam connections (rigid, semi-rigid and flexible) present in the investigated composite floor was simulated by non-linear spring elements (COMBIN7 and COMBIN39 [8]), see Figure 8, which incorporates the geometric nonlinearity and the hysteretic behaviour effects. The moment versus rotation curve related to the adopted semi-rigid connections was based on experimental data [10], see Figure 9.
\n\t\t\tWhen the complete interaction between the concrete slab and steel beams was considered in the analysis, the numerical model coupled all the nodes between the beams and slab, to prevent the occurrence of any slip. On the other hand, to enable the slip between the concrete slab and the “I” steel profiles, to represent the partial interaction (steel-concrete) cases, the modelling strategy used non-linear spring elements (COMBIN39 [8]), see Figure 8, simulating the shear connector actions. The adopted shear connector force versus displacement curves were also based on experimental tests [11,12], see Figure 10.
\n\t\t\tMoment versus rotation curve: beam-to-beam semi-rigid connections [10].
Force versus slip curve: shear connectors.
For practical purposes, a non-linear time-domain analysis was performed throughout this study. This section presents the evaluation of the composite floor vibration levels when sub-mitted to human rhythmic activities. The composite floor dynamic response was determined through an analysis of its natural frequencies and peak accelerations. The results of the dynamic analysis were obtained from an extensive parametric analysis, based on the finite element method using the ANSYS program [8].
\n\t\t\tIn order to evaluate quantitative and qualitatively the obtained results according to the proposed methodology, the composite floor peak accelerations were calculated and compared to design recommendations limiting values [6,9]. This comparison was made to access a possible occurrence of unwanted excessive vibration levels and human discomfort.
\n\t\t\tThe steel-concrete composite floor natural frequencies were determined with the aid of the numeric simulations, see Tables 4 and 5. The structural behaviour of the beam-to-beam connections (rigid, semi-rigid and flexible joints) and the stud connectors (from total to various levels of partial interaction cases) present in the investigated structural model were simulated objectifying to verify the influence of these connections and the steel-concrete interaction degree on the composite floor dynamic response.
\n\t\t\t\tFrequencies (Hz) | \n\t\t\t\t\t\t\tTotal Interaction | \n\t\t\t\t\t\t\tPartial Interaction (50%) | \n\t\t\t\t\t\t||||
Rigid | \n\t\t\t\t\t\t\tSemi-rigid | \n\t\t\t\t\t\t\tFlexible | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\tSemi-rigid | \n\t\t\t\t\t\t\tFlexible | \n\t\t\t\t\t\t|
f01\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t6.57 | \n\t\t\t\t\t\t\t6.14 | \n\t\t\t\t\t\t\t6.00 | \n\t\t\t\t\t\t\t6.32 | \n\t\t\t\t\t\t\t5.91 | \n\t\t\t\t\t\t\t5.76 | \n\t\t\t\t\t\t
f02\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t6.69 | \n\t\t\t\t\t\t\t6.41 | \n\t\t\t\t\t\t\t6.30 | \n\t\t\t\t\t\t\t6.45 | \n\t\t\t\t\t\t\t6.19 | \n\t\t\t\t\t\t\t6.05 | \n\t\t\t\t\t\t
f03\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.03 | \n\t\t\t\t\t\t\t6.52 | \n\t\t\t\t\t\t\t6.37 | \n\t\t\t\t\t\t\t6.76 | \n\t\t\t\t\t\t\t6.27 | \n\t\t\t\t\t\t\t6.31 | \n\t\t\t\t\t\t
f04\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.04 | \n\t\t\t\t\t\t\t6.71 | \n\t\t\t\t\t\t\t6.58 | \n\t\t\t\t\t\t\t6.77 | \n\t\t\t\t\t\t\t6.46 | \n\t\t\t\t\t\t\t6.31 | \n\t\t\t\t\t\t
f05\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.11 | \n\t\t\t\t\t\t\t6.97 | \n\t\t\t\t\t\t\t6.85 | \n\t\t\t\t\t\t\t6.87 | \n\t\t\t\t\t\t\t6.72 | \n\t\t\t\t\t\t\t6.58 | \n\t\t\t\t\t\t
f06\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.28 | \n\t\t\t\t\t\t\t7.10 | \n\t\t\t\t\t\t\t6.98 | \n\t\t\t\t\t\t\t7.01 | \n\t\t\t\t\t\t\t6.83 | \n\t\t\t\t\t\t\t6.68 | \n\t\t\t\t\t\t
Composite floor natural frequencies (Beam-to-beam semi-rigid connections: Sj = 12kNmm/rad. Stud 13mm: Sj = 65kN/mm).
Considering the investigated composite floor natural frequencies, a small difference between the numeric results obtained with the use of total interaction or partial interaction (50%) can be observed. The largest difference between the natural frequencies was approximately equal to 5% to 7%, as presented in Tables 4 and 5 and illustrated in Figure 11.
\n\t\t\t\tAnother interesting fact concerned that when the joints flexibility (rigid to flexible) and steel-concrete interaction degree (from total to partial) decreases the composite floor natural frequencies become smaller, see Tables 4 and 5. This conclusion is very important due to the fact that the structural system becomes more susceptible to excessive vibrations induced by human rhythmic activities.
\n\t\t\t\tFrequencies (Hz) | \n\t\t\t\t\t\t\tTotal Interaction | \n\t\t\t\t\t\t\tPartial Interaction (50%) | \n\t\t\t\t\t\t||||
Rigid | \n\t\t\t\t\t\t\tSemi-rigid | \n\t\t\t\t\t\t\tFlexible | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\tSemi-rigid | \n\t\t\t\t\t\t\tFlexible | \n\t\t\t\t\t\t|
f01\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t6.63 | \n\t\t\t\t\t\t\t6.18 | \n\t\t\t\t\t\t\t6.06 | \n\t\t\t\t\t\t\t6.39 | \n\t\t\t\t\t\t\t5.98 | \n\t\t\t\t\t\t\t5.84 | \n\t\t\t\t\t\t
f02\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t6.75 | \n\t\t\t\t\t\t\t6.46 | \n\t\t\t\t\t\t\t6.36 | \n\t\t\t\t\t\t\t6.52 | \n\t\t\t\t\t\t\t6.26 | \n\t\t\t\t\t\t\t6.13 | \n\t\t\t\t\t\t
f03\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.10 | \n\t\t\t\t\t\t\t6.58 | \n\t\t\t\t\t\t\t6.43 | \n\t\t\t\t\t\t\t6.84 | \n\t\t\t\t\t\t\t6.35 | \n\t\t\t\t\t\t\t6.19 | \n\t\t\t\t\t\t
f04\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.11 | \n\t\t\t\t\t\t\t6.77 | \n\t\t\t\t\t\t\t6.65 | \n\t\t\t\t\t\t\t6.85 | \n\t\t\t\t\t\t\t6.54 | \n\t\t\t\t\t\t\t6.40 | \n\t\t\t\t\t\t
f05\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.17 | \n\t\t\t\t\t\t\t7.02 | \n\t\t\t\t\t\t\t6.91 | \n\t\t\t\t\t\t\t6.94 | \n\t\t\t\t\t\t\t6.79 | \n\t\t\t\t\t\t\t6.67 | \n\t\t\t\t\t\t
f06\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t7.35 | \n\t\t\t\t\t\t\t7.16 | \n\t\t\t\t\t\t\t7.05 | \n\t\t\t\t\t\t\t7.08 | \n\t\t\t\t\t\t\t6.91 | \n\t\t\t\t\t\t\t6.78 | \n\t\t\t\t\t\t
Composite floor natural frequencies (Beam-to-beam semi-rigid connections: Sj = 12kNmm/rad. Stud 19mm: Sj = 200kN/mm).
Steel-concrete composite floor fundamental frequency (f01) variation.
In sequence, Figure 12 presents the composite floor vibration modes when total and partial interaction situations were considered in the numerical analysis. It must be emphasized that the composite floor vibration modes didn’t present significant modifications when the connections flexibility and steel-concrete interaction was changed. It must be emphasized that the structural model presented vibration modes with predominance of flexural effects, as illustrated in Figure 12.
\n\t\t\t\tInvestigated structural model vibration modes (total and partial interaction).
The present study proceeded with the evaluation of the structural model performance in terms of human comfort and vibration serviceability limit states. The peak acceleration analysis was focused in aerobics and considered a contact period carefully chosen to simulate this human rhythmic activity on the analysed composite floor.
\n\t\t\t\tThe present work considered a contact period, simulating aerobics on the composite floor, Tc, equal to 0.34s (Tc = 0.34s) and the period without contact with the structure, Ts, of 0.10s (Ts = 0.10s). Based on the experimental results [7], the floor dynamic behaviour was evaluated keeping the impact coefficient value, Kp, equal to 2.78 (Kp = 2.78). Figures 13 and 14 illustrate the dynamic response (displacements and accelerations) related to nodes A and B (see Figure 6) when thirty two people are practising aerobics on the composite floor.
\n\t\t\t\tBased on the results presented in Figures 13and 14, it is possible to verify that the dynamic actions coming from aerobics, represented by the dynamic loading model (see Equation (1) and Figure 6), have generated peak accelerations higher than 0.5%g [6,9]. This trend was confirmed in several other situations [1,2], where the human comfort criterion was violated.
\n\t\t\t\tComposite floor dynamic response. Semi-rigid connections and partial interaction): Node A.
Composite floor dynamic response (Semi-rigid connections and partial interaction): Node B.
In sequence of the study, Tables 6 and 7 show the peak accelerations, ap (m/s2), corresponding to nodes A to H (Figure 6), when thirty two dynamic loadings, simulating individual practising aerobics were applied on the composite floor.
\n\t\t\t\tInteraction | \n\t\t\t\t\t\t\tModel | \n\t\t\t\t\t\t\tap (m/s2)Node A | \n\t\t\t\t\t\t\tap (m/s2)Node B | \n\t\t\t\t\t\t\tap (m/s2)Node C | \n\t\t\t\t\t\t\tap (m/s2)Node D | \n\t\t\t\t\t\t
Complete | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\t0.26 | \n\t\t\t\t\t\t\t0.17 | \n\t\t\t\t\t\t\t0.17 | \n\t\t\t\t\t\t\t0.26 | \n\t\t\t\t\t\t
Semi-rigid | \n\t\t\t\t\t\t\t0.28 | \n\t\t\t\t\t\t\t0.20 | \n\t\t\t\t\t\t\t0.20 | \n\t\t\t\t\t\t\t0.28 | \n\t\t\t\t\t\t|
Flexible | \n\t\t\t\t\t\t\t0.30 | \n\t\t\t\t\t\t\t0.44 | \n\t\t\t\t\t\t\t0.43 | \n\t\t\t\t\t\t\t0.30 | \n\t\t\t\t\t\t|
Partial (50%) | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.53\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t0.36 | \n\t\t\t\t\t\t\t0.36 | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.53\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t
Semi rigid | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.62\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.63\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.63\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.62\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t|
flexible | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.60\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.80\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.80\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t0.60\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t|
Limiting Acceleration: alim = 0.50m/s2 (5%g - g: gravity) [6,9] | \n\t\t\t\t\t\t
Composite floor peak accelerations: Nodes A, B, C and D (see Figure 6).
Interaction | \n\t\t\t\t\t\t\tModel | \n\t\t\t\t\t\t\tap (m/s2)Node E | \n\t\t\t\t\t\t\tap (m/s2)Node F | \n\t\t\t\t\t\t\tap (m/s2)Node G | \n\t\t\t\t\t\t\tap (m/s2)Node H | \n\t\t\t\t\t\t
Complete | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\t0.035 | \n\t\t\t\t\t\t\t0.035 | \n\t\t\t\t\t\t\t0.035 | \n\t\t\t\t\t\t\t0.035 | \n\t\t\t\t\t\t
Semi rigid | \n\t\t\t\t\t\t\t0.087 | \n\t\t\t\t\t\t\t0.036 | \n\t\t\t\t\t\t\t0.036 | \n\t\t\t\t\t\t\t0.087 | \n\t\t\t\t\t\t|
flexible | \n\t\t\t\t\t\t\t0.088 | \n\t\t\t\t\t\t\t0.09 | \n\t\t\t\t\t\t\t0.09 | \n\t\t\t\t\t\t\t0.088 | \n\t\t\t\t\t\t|
Partial (50%) | \n\t\t\t\t\t\t\tRigid | \n\t\t\t\t\t\t\t0.30 | \n\t\t\t\t\t\t\t0.13 | \n\t\t\t\t\t\t\t0.13 | \n\t\t\t\t\t\t\t0.30 | \n\t\t\t\t\t\t
Semi-rigid | \n\t\t\t\t\t\t\t0.40 | \n\t\t\t\t\t\t\t0.14 | \n\t\t\t\t\t\t\t0.14 | \n\t\t\t\t\t\t\t0.40 | \n\t\t\t\t\t\t|
Flexible | \n\t\t\t\t\t\t\t0.32 | \n\t\t\t\t\t\t\t0.24 | \n\t\t\t\t\t\t\t0.24 | \n\t\t\t\t\t\t\t0.32 | \n\t\t\t\t\t\t|
Limiting Acceleration: alim = 0.50m/s2 (5%g - g: gravity) [6,9] | \n\t\t\t\t\t\t
Composite floor peak accelerations: Nodes E, F, G and H (see Figure 6).
The results presented in Tables 6 and 7 have indicated that when the joints flexibility (rigid to flexible) and steel-concrete interaction degree (total to partial) decreases the composite floor peak accelerations become larger. These variations (joints flexibility and steel-concrete interaction) were very relevant to the composite floor non-linear dynamic response when the human comfort analysis was considered.
\n\t\t\t\tIt must be emphasized that individuals practising aerobics on the structural model led to peak acceleration values higher than 5%g [6,9], when the composite floor was submitted to thirty two people practising aerobics, violating the human comfort criteria (amax = 0.50m/s2> alim = 0.50m/s2), see Tables 6 and 7. However, these peak acceleration values tend to decrease when the floor dynamic response obtained on the nodes E to H (see Figure 6) was compared to the response of nodes A to D (see Figure 6), see Tables 6 and 7.
\n\t\t\tThe main objective of this paper was to investigate the beam-to-beam structural connections effect (rigid, semi-rigid and flexible) and the influence of steel-concrete interaction degree (from total to various levels of partial interaction) over the non-linear dynamic behaviour of composite floors when subjected to human rhythmic activities. This way, an extensive parametric analysis was developed focusing in the determination quantitative aspects of the composite floors dynamic response.
\n\t\t\tThe investigated structural model was based on a steel-concrete composite floor spanning 40m by 40m, with a total area of 1600m2. The structural system consisted of a typical composite floor of a commercial building. The composite floor studied in this work is supported by steel columns and is currently submitted to human rhythmic loads. The structural system is constituted of composite girders and a 100mm thick concrete slab.
\n\t\t\tThe proposed computational model adopted the usual mesh refinement techniques present in finite element method simulations, based on the ANSYS program. The numerical model enabled a complete dynamic evaluation of the investigated steel-concrete composite floor especially in terms of human comfort and its associated vibration serviceability limit states.
\n\t\t\tThe influence of the investigated connectors (Stud Bolts: 13mm and 19mm) on the composite floor natural frequencies was very small, when the steel-concrete interaction degree (from total to partial) was considered in the analysis. The largest difference was approximately equal to 5% to 7%.
\n\t\t\tOn the other hand, when the joints flexibility (rigid to flexible) and steel-concrete interaction degree (from total to partial) decreases the composite floor natural frequencies become smaller. This fact is very relevant because the system becomes more susceptible to excessive vibrations.
\n\t\t\tThe composite floor vibration modes didn’t present significant modifications when the connections flexibility and steel-concrete interaction was changed. The investigated structure presented vibration modes with predominance of flexural effects. The results have indicated that when the joints flexibility (rigid to flexible) and steel-concrete interaction degree (total to partial) decreases the composite floor peak accelerations become larger.
\n\t\t\tThe maximum acceleration value found in this work was equal to 0.80m/s2 (ap = 0.80 m/s2: flexible model) and 0.63m/s2 (ap = 0.63 m/s2: semi-rigid model), while the maximum accepted peak acceleration value is equal to 0.50m/s2 (alim = 0.50m/s2) [6,9]. The structural system peak accelerations were compared to the limiting values proposed by several authors and design standard [6,9]. The current investigation indicated that human rhythmic activities could induce the steel-concrete composite floors to reach unacceptable vibration levels and, in these situations, lead to a violation of the current human comfort criteria for these specific structures.
\n\t\tThe authors gratefully acknowledge the support for this work provided by the Brazilian Science Foundation CAPES, CNPq and FAPERJ.
\n\t\tAn Unmanned Aerial Vehicle (UAV) is a type of aircraft that operates without a human pilot on-board. There are different types of UAVs employed for different purposes. Originally, the technology was employed by the military for anti-aircraft target practice, intelligence gathering and surveillance of some enemy territories. The technology has however grown beyond its initial purpose and in recent years has gained prominence in different spheres of human endeavor. Advancements in technology has allowed for the increased adaptation of unmanned aerial vehicles for various purposes. Without an on-board pilot, UAVs are controlled either remotely by a pilot at a ground station or autonomously, steered by a pre-programmed flight plan.
\nThere is a huge potential for the application of UAVs in Agriculture. One such application is in accurate and evidence-based forecasting of farm produce using spatial data collected by the UAV. UAVs also allow farmers to observe their fields from the sky. This sky-view can reveal many issues on the farm, common among which is irrigation related problems, soil variations, fungal and pest infestations. Further information relating to water access, changing climate, wind, soil quality, the presence of weeds and insects, variable growing seasons, and more can all be monitored with UAVs. From a livestock perspective, UAVs are being used to perform head counts, monitoring animals and also studying eating habits and health related patterns. Utilizing the information gathered, farmers can provide fast and efficient solutions to detected problems and issues, make better management decisions, improve farm productivity, and ultimately generate higher profit. In this chapter, various applications of UAVs in Agriculture are discussed both in commercial livestock farming and crop farming. This chapter also presents some of the open challenges to the application of UAVs in Agriculture.
\nImmediately following this introduction is a discussion of the various types of UAVs which is done in Section 2. This is followed by the applications of UAVs in crop farming and in livestock in sections 3 and 4 respectively. Advantages of UAVs and corresponding challenges are discussed in Section 5, the chapter ends with the 6th and concluding section.
\nUAVs can be classified based on usage, with some being used for photography, aerial mapping, surveillance, cinematography etc. However, a better classification can be made based on their feature sets. Vroegindeweij, et al. in their paper [1], presented an overview of the different types of UAVs applied in Agriculture and categorized them into three main groups – fixed-wing, Vertical Take Off and Landing (VTOL) and bird/insect. The authors identified the VTOL with its agility, great maneuverability and hovering ability as best suited for Agricultural application. In [2], the authors however argued in favor of the fixed-wing UAVs, stating that their long flight time and speed makes them better suited in comparison to the VTOL, which have comparatively shorter flight time and slow speed. In other works, authors have argued in favor of unmanned helicopters such as the monocopter or single-rotor UAV [3, 4]. These types of UAVs have long flight time, can fly at different altitudes and have good hovering abilities. However, they are much more complex to fly. A comprehensive survey of various UAVs was also done in [5]. From these literatures, four major types of UAVs are identified, which are:
Multi-rotor UAVs
Fixed-wing UAVs
Single-rotor Helicopter
Fixed-wing-multi-rotor Hybrid UAVs
These are the most common type of UAV, evident by their wide popularity among professionals and hobbyists alike. They find applications in photography, aerial video surveillance, recreational sports and games etc. They are the easiest to manufacture and also the cheapest type of UAV. Multi-rotor UAVs are further classified based on the number of rotors on the platform. There are those with three rotors called tricopter, with four rotors called quadcopter, with six rotors called hexacopters and those with eight rotors called octocopter. Flying a multi-rotor UAV does not require exceptional skill unlike the other types of UAVs.
\nMulti-rotor UAVs though cheap and easy to manufacture have a few drawbacks which include: limited flying time, endurance and speed. They can only sustain an average flying time of between 20 and 30 minutes. This is because a large percentage of their energy is expended fighting gravity and wind to remain stable in the air. Figure 1 shows an octocopter used for precision spraying of liquid pesticides and herbicides.
\nMulti-rotor UAV [6].
These types of UAVs have wings similar to normal aircrafts. Unlike the Multi-Rotor UAVs, they do not exert a lot of energy to stay afloat in the air, hence able to fly longer; having average flight times of over an hour. Longer flight time makes them most ideal for long distance operations. However, they cannot hover on a spot and are thus not suitable for aerial photography. Furthermore, they are more expensive and require exceptional flying skill to operate. Figure 2 shows a sample fixed wing UAV used for capturing images across large acres of farmland.
\nA fixed-wing UAV [7].
Single rotor UAVs are also called monocopters and look very much like helicopters in design and structure. Though they are called single rotor UAVs, they actually have two rotors - a large on top and a smaller one at the tail. The bigger rotor is for lift while the smaller is used for control. They have significantly longer flying time than their multi-rotor counterpart, as they are often powered by gas engines. These UAVs are also highly maneuverable and much more efficient than the multi-rotor types. Similar to the multi-rotor, they are also able to hover, hence useful for aerial photography and precision spraying. Despite these beneficial attributes, they come with higher operational risks as the large sized rotor blades usually pose a risk which is mostly fatal in nature. Like the fixed wing UAVs, these also require special flying training. Figure 3 shows a sample single-rotor UAV.
\nA single-rotor UAV [8].
These types of UAV combine features of the fixed-wing and the multi-rotor UAVs, with the hybridization gives these UAVs a best-of-both-worlds feature set. They are able to perform vertical take-off and land (VTOL) as well as hovering in place like the multi-rotor and single-rotor. Similar to the fixed-wing and single-rotor UAVs, these also benefits from long flight-time, but can stay in flight for much longer. Figure 4 shows an image of one such UAV that is versatile enough to be used for image capturing, surveillance as well as precision spraying.
\nA hybrid fixed-wing-multi-rotor UAV [9].
Though these are the four common types of UAVs, there is a unique type of UAV called the Flexible Membrane Wing (FMW) UAV [10]. The FMW has wings made from flexible membrane material, with the advantages of this being easy of storage (as the wings can simply be folded up) and better control and maneuverability in windy conditions (as the flexible wings dynamically adjust to cater for wind preventing “adaptive washout”). The FMW is a niche UAV, targeted flying in harsh and windy conditions. Flexible membrane also implies lighter weight and by extension the possibility of carrying larger payloads.
\n\nFigure 5 shows a comparison of the four different types of UAVs based on their average weights, payload size and flight time; Table 1 on the other hand summarizes their in-flight specifications. For each category, a model UAV was selected. The values shown were obtained from the respective manufacturer documentation and/or operator’s manual of each product. On Table 1, the advantage of the hybridization can clearly be seen, as it resulted in higher flying altitude, wider control range, increased speed and longer flight time compared to the other UAV types.
\nUAV weight and payload vs. flight time.
UAV type | \nAltitude (km) | \nAvg. control range (km) | \nAvg. airspeed (m/s) | \n
---|---|---|---|
Multi-rotor UAVs (DJI Agras MG-1P [6]) | \n2 | \n3–5 | \n7 | \n
Fixed-wing UAVs (AgEagle RX60 [7]) | \n0.125 | \n2 | \n18.8 | \n
Single-rotor (Alpha 800 [8]) | \n3 | \n30 | \n15.2 | \n
Fixed-wing-multi-rotor hybrid UAVs (Jump 20 [9]) | \n4 | \n500–1000 | \n30 | \n
Feature-based comparison of UAVs.
According to Massachusetts Institute of Technology (MIT), UAV technology will give the Agriculture industry a high-technology makeover, with planning and strategy based on real-time data gathering and processing. PwC put a $32.4 billion valuation on the UAV-powered Agriculture solutions market [11]. The application of UAV technology in Agriculture has become increasingly necessary with the increase in global population and the resultant pressure on agricultural consumption. The ever growing international population is not proportionately matched with crop growth; hence, there is a growing concern about food sustainability. In a bid to tackle this challenge, farmers around the globe have had to adapt modern and automated solutions in order to keep up with the agricultural needs of the world population that is in constant flux. UAVs are one such technology that could help improve crop yield. A number of UAV application areas are presented in the following subsections.
\nThe use of UAVs for soil information sourcing is helpful at the early start of a crop cycle. The data collected helps in early soil analysis, and is also useful in planning seed planting patterns. These data can also assists the farmer in making irrigation plans as well as determining the quantity of fertilizer needed on the soil or field after planting. Using a data-driven approach, the farmers can improve the overall yield quantity of agricultural produce, while significantly saving on fertilizers and pesticides. All these are made possible through the analysis of remote images captured with UAV. UAV imagery also has a huge potential in designing site-specific weed control treatments. With the high resolution images, farmers can quickly and precisely spot weeds almost immediately they spring up and apply minimal pesticide to contain them. The authors in [12] developed an Object-Based Image Analysis (OBIA) on a series of UAV images using six-band multi-spectral cameras on a maize field in Spain. While in [13] the technical specifications and configuration of a UAV which could be used to capture remote images for Early Season Site-Specific Weed Management (ESSWM) were given. The study also evaluated the image spatial and spectral properties necessary for weed seedling discrimination. They deployed an UAV equipped with multi spectral cameras and analyzed the technical specifications and configuration of the UAV to generate images at different attitudes; with the high spectral resolution required for the detection and location of weed seedlings in a sunflower field. The result of the study can be of help in the selection of an adequate sensor and configuration of the flight mission for ESSWM.
\nPlanting crops is a costly and cumbersome endeavor that has traditionally requires a lot of manpower. UAVs have simplified crop planting for farmers, with their abilities to cover large acres of land within a short period with utmost precision and accuracy. Today’s high-end UAV farming technology offers UAV-powered planting techniques that reduce planting costs by up to 85%. The reduction in planting costs is a result of the UAV’s capability of performing multiple tasks at the same time.
\nUAVs have become increasingly popular in recent years in agricultural research applications. They have been found to have capabilities of acquiring images with high spatial and temporal resolutions in Agriculture. Reference [14] evaluated the performance of a UAV-based remote sensing system for quantification of crop growth parameters of six sorghum hybrids. Factors such as Leaf Area Index (LAI), fractional vegetation (fc) and yields were considered. The evaluation was carried out using a fixed-wing UAV, equipped with a multi spectral sensor to collect images during the 2016 growing season with flight missions carried out 50 days after planting. The flight missions provided data covering the different growth periods of the sorghum hybrids. The authors inferred that high resolution images acquired using UAV can be effectively utilized for in-season data collection from the field. The results obtained proved the relationship between Normalized Difference Vegetation Index (NDVI) and LAI, and between NDVI and fc. It was thus possible to determine/estimate LAI and fc from UAV derived NDVI values. It was shown also that imagery taken at flowing stage could be better indicator of yield, rather than NDVI obtained at earlier growth stage of sorghum crop. Furthermore, it was also established that early season NDVI measurement is useful index for estimating plant population density of sorghum.
\nThe authors in [15] sought to develop a novel method to quantify the distance between maize plants at field scale using an UAV. The distance between roots and plants are essential in determining the final grain yield in row cops. An UAV-based image algorithm was developed to calculate maize plant distances. Knowledge of the exact number of plants per square meter is essential and helps to improve yields by deducing the fertilizer and pesticide application to match plant demand. Determining plant population is essential for several other processes such as soil-to-plant balance, nutrient recycling and resource use efficiency. The study demonstrated the possibility of quantifying the distance between maize plants and provided an innovative approach to quantify plant-to-plant variability and by extension crop yield estimates.
\nCrop spraying is usually a tough and onerous task for farmers and agricultural production companies. It involves covering extremely large expanses of land comprehensively to ensure proper growth of crops. Agricultural UAVs have simplified crop spraying for farmers; as they can cover large expanse of land within a very short time interval. Using sensors, UAVs can automatically adjust their height when spraying across uneven fields. This improves the spraying accuracy and conserves resources. The advantages of using UAVs for crop spraying include: time and cost savings for the farmer, efficient spraying as both the plants and the soil below can be reached, and protecting farmers from prolonged exposure to potentially harmful chemicals that are hitherto associated with manual spraying. Agricultural UAVs utilize state-of-the-art topographical scanning techniques to dispense the optimal amount of fluid required for proper crop growth. This ensures even coverage with limited wastage. Lv et al. [16] demonstrated the practicability of infrared thermal imaging in evaluating the droplet deposition in the field of aerial spraying. In the study, the effect of UAV flight speed on the spray droplets was investigated and the variable spray test was conducted by a UAV simulation platform, with airborne spray system under controllable environment. Several conclusions were drawn from the study among which were that deposition density decreases with the flight speed and droplet diameter (i.e. the distribution uniformity of particle size) decreases with an increased flight speed resulting in the worse uniformity of the sprayed droplets. The authors therefore provided a theoretical support for optimizing the spraying parameters of plant protection UAV, aimed at improving plant yield.
\nSpot spraying is similar to crop spraying but targets weeds. With the use of high resolution cameras, the UAV can identify weeds and precisely spray a jet of herbicide. Spot spraying can save up to 90% on chemical herbicides. Numerous research works [17, 18, 19] have been done in determining the efficacy of UAVs for spot spraying. Some factors considered were balancing UAV altitude and speed with spraying height and accuracy as well as droplet sizes, spray pressure and the possible effects of the UAVs’ propeller(s) airflow direction.
\nA combination of large farm fields and low efficiency in crop monitoring system are some of the greatest farming challenges. The challenge of monitoring is further aggravated by unpredictable weather conditions, which drive up risk and field maintenance costs. An agricultural UAV helps the farmer overcome some of these challenges. UAVs with thermal imaging cameras enable the farmer to monitor his farm. The farmer can check the state of crops in the farm, as well as areas that need urgent attention. The result is improved yield and greater profit. [20] demonstrated the possibility of generating quantitative mapping products such as crop stress maps from UAV images and highlighted the value of UAV remote sensory when applied in precision Agriculture. The study applied a single-rotor UAV (monocopter), equipped with multiple spectral cameras, and then developed a framework to process the UAV images and generate mosaic images which can be aligned with maps for GIS integration at a later stage.
\nAgricultural UAVs fitted with thermal imaging cameras have the capability to providing tremendous insights into specific troubled areas in the farm. Using the thermal cameras, the farmers are able to determine areas with low soil moisture, pinpoint crops that are dehydrated, locate areas that are water-logged and in general have a sense of the overall health status of crops in the field. Such precise and specific monitoring were either not possible with traditional farming, inefficiently done or extremely expensive as experts have to be contracted to carry out the task and proffer adequate solutions. UAVs now give the farmers that ability to do these themselves. In [21], the authors carried out a study on vineyard water status variability by thermal and multispectral imagery using an UAV. Assessment of the water status variability of a commercial rain-fed Tempranillo vineyard was done, and concluded that an UAV can be used to assess vine water status, and to map within vineyard variability which could be useful for irrigation practices. The work done in [22] focused on the application of thermal remote sensory in precision Agriculture, and some of the concerns relating to its application. Gonzalez-Dugo et al. [23] further dealt with the assessment of heterogeneity in water status in a commercial orchard as a prerequisite for precision irrigation margent. High resolution airborne thermal imagery was employed. A UAV with thermal camera on board was flown three times during the day over a commercial orchard; and the indicators derived from the thermal imagery described the spatial variability in crop water status and thus allows the mapping of an orchard on a tree by tree basis. It therefore becomes a valuable tool for water management in precision Agriculture.
\nFarm health assessment is crucial for detecting fungal and bacterial diseases on the farm. By scanning a crop using both visible and near-infrared light, UAV-carried devices can detect temporal and spatial reflectance variations and associate it to the farms health for early interventions, which ultimately saves the entire farm. These two possibilities increase a plant’s ability to overcome disease. And in the case of crop failure, the farmer will be able to document losses more efficiently for insurance claims. UAVs offer new and modern methods of accurately monitoring and assessing pest damage needs to be investigated. The authors in [24] explored the combination of UAVs, remote sensory and machine learning techniques as a promising technology to address the problem of agricultural pests in farmlands. UAV platform was deployed over a sorghum crop in South-East Queensland, Australia, to collect high resolution RGB images of certain areas which were severely damaged by white grub pest. An image processing pipeline was implemented prior to image analysis. The study demonstrates how UAV-based remote sensitivity and machine learning could be used to achieve biosecurity surveillance and pest management. The work presented in [25] also corroborated the use of UAV in crop health assessment, and outlined the benefits of deploying UAV remote sensing over the traditional methods. They developed a method that can quickly monitor crop pest, based on UAV remote sensing, which was deployed for inspection pests in Baiyangdian agricultural zone during the growth season. An improved SIFT Algorithm was adapted for image matching and mosaic with good result. The method adopted by [24] was used to check the status information of crop pest. Similarly, in the work done by Yinka-Banjo et al. [26], the authors proposed the use of UAVs for bird control in farmlands. Their solution combined the use of autonomous vehicles with bird scare tactics. The combination was reported to be more efficient than the traditional human-based manual approaches.
\nLivestock farming is the act of rearing animals for food and/or other uses such as medicine, leather, fur and fertilizer. The authors in [27, 28] showed that traditionally Livestock Production Systems (LPS) were grouped into three major classes, namely: livestock production integrated with crop, land based and agro-ecological. They further sub-divided LPS into 11 groups – solely livestock production, temperate and tropical highlands grassland-based, arid and subtropics grassland, humid and subtropical mixed-farming based, temperate and tropical highlands rain-fed mixed farming, humid and subtropics rain-fed mixed, temperate and highlands irrigated mixed farming, humid and subtropics irrigated mixed farming, arid and subtropics irrigated mixed farming, landless monogastrics and landless ruminant farming. Similarly, in [29], the authors reviewed five (5) types of livestock production systems in tropical areas based on factors such as agro-ecological zones, animal type, function and management. The identified classes were Pastoral Range, Crop-livestock (low and highlands), Ranching and landless.
\nWith respect to livestock, sheep and goats are the most farmed animals, followed by cattle. Table 2 shows a numerical distribution of global livestock produce adapted from [30].
\nAnimal type and/or produce | \nNumber/quantity (10^6) | \n
---|---|
Animal | \n\n |
Sheep and goats | \n1777 | \n
Cattle and buffaloes | \n1526 | \n
Animal produce | \n\n |
Milk | \n594.4 | \n
Pork | \n95.2 | \n
Poultry meat | \n73.7 | \n
Beef | \n60.7 | \n
Eggs | \n58.9 | \n
Mutton | \n11.9 | \n
Global livestock produce.
Livestock farming as with other aspects of Agriculture can be monotonous and laborious. Humans are however not well suited to such task over a prolonged period of time. Machines therefore can find practically applications in this arm of Agriculture, as they are designed to perform repeatable tasks, faster and possibly more efficiently (over a long period of time) than humans can. UAVs are therefore no exceptions and have found practical applications in livestock farming. Applications of UAVs in livestock farming are discussed as follows:
\nTo further put Table 2 in perspective, according to the National Development Agency of South Africa, there were over 13 million units of cattle, 30 million sheep and 6.6 million goats and 1.6 million pigs bred in each province annually between up on to 2003. The figures are even significantly higher in European countries according to Eurostat. These are staggering numbers, hence monitoring and daily head counts of these large number of animals can be challenging. UAVs can thus find application here and be used to perform headcounts of livestock across these large grazing areas [31, 32, 33]. Animal counting can be done either by using image recognition [31] or using heat detecting infra-red cameras [34]. For image processing, Convolutional Neural Network (CNN) has emerged in recent times as the most widely used [35]. In large grazing areas, the UAVs can also be used to detect and count the number of animals present. In most of these works, the UAVs fly across the field, and counting the number of animals present. In the work done by [33] however, the authors proposed an approach, wherein the number of goats are counted and tracked using fewer numbers of pictures, sometimes only one. The authors reported 73% count accuracy and 78% tracking accuracy.
\nIn contrast, in their book [34], the authors reviewed numerous methods of performing thermal imaging for monitoring animals in the wild. Among many other factors, the authors argued that thermal imagining is not dependent on time of the day unlike image processing. This therefore provides a unique opportunity to observe animals in their natural habitats without causing disturbances – which can lead to dispersion and possibly double or inaccurate counts.
\nBeyond counting, research work is underway at the Texas A&M University, to investigate the use of infra-red cameras mounted on UAVs to monitor the health of animals. The research is based on the premise that, animals with fever tend to have heightened temperature. This can easily be detected by the UAV and appropriate medication can be administered [36]. Similar research targeted at monitoring health has also been carried out in [37]. Figure 6 shows a sample heat map of a herd of cattle captured by a UAV.
\nHeat map of herd of cattle [36].
On an individual levels, animals can be tagged with RFIDs or similar sensors and can then be monitored using UAVs. With this, farmers can effectively monitor the movements and feeding behavior of a specific animal [38]. This has also been extensively used in monitoring endangered animals, raised in captivity and released into the wild. Similar to the two application areas discussed above, the identification can be carried out using UAVs fitted with normal cameras or IR cameras (which detect heat emissions from the animal) or RFID readers.
\nA major challenge to the application of RFID is that passive RFID tags have very short range, hence might be difficult to use. Potential solutions might include:
Painting QR codes on cattle, which the cameras fitted on the drone, can simply scan in order to identify the animal.
The use of a relay drone, such as the RFly being researched at MIT [39]. The RFly acts as a relay between the RFID tags and the reader. Using RFly, the authors recorded up to 50 meter range extension for passive RFID tags.
These technologies can be borrowed and used for animal identification and monitoring in Agriculture.
\n\nFigure 7 shows a potential use case of UAVs and RFID tags in animal identification.
\nAnimal identification using relay UAVs.
Mustering is the process of using aircraft to locate and gather animals across a large span of land. Dogs (sheep dogs) and human on horses (cow boys) or motorcycles have traditionally been used to direct livestock along specific paths. For larger expanse of land, small sized helicopters are used. These helicopters are often piloted by one person and have highly maneuverable and agile. The challenges with the use of helicopters are the need for extensive training, the cost of licenses and certifications, the cost of fuel and most especially the high level of risk exposure and casualties associated with it.
\nUAVs provide a unique opportunity for aerial mustering as they are comparatively risk free, cheaper to fly and require shorter training period, yet able to achieve similar results. UAVs have successfully been used in Australia and New Zealand to muster sheep and cattle [40]. According to [41], aerial mustering UAVs are fitted with sirens to herd sheep, deer and cattle. The UAVs can also be used to guide the animals to feeding, drinking and milking areas. Numerous case studies of the application of the DJI Phantom in Agriculture are given in [41]. Figure 8 shows a use case of UAVs for sheep mustering.
\nAerial mustering using a UAV.
Geo-fencing, virtual perimeter or geo-zoning simply means creating a virtual barrier or perimeter around a geographical area of interest [42, 43]. It has also been defined as an enclosure, or a boundary without the use of physical barriers. It can be accomplished by using a combination of RFID, LoRaWAN and GPS based location sensors for instance. Sensors obtain the location of the subject of interest relative to a map. Geo-fencing has been used in numerous fields such as fleet management and logistics – to monitor movement of vehicles, proximity marketing – which prompt users of products when they are close enough, asset management – which send alerts when an asset is moved without authorization, people monitoring – such as in monitoring movement of children and employees and in law enforcement – to restrict and/or monitor persons of interest.
\nGeo-fencing has also seen immense application in Agriculture, more specifically in free-range livestock farming. Sensors are placed on collars of cattle, goats etc. and these send location data to the farmer. There are two major forms of application of geo-fencing in agriculture: in the first, the sensors simply notify the farm owner when animals have grazed outside a pre-defined perimeter [44]. In this system, the farmer has to actively go muster the animals back into the perimeter. In the second approach, the animals are given subtle stimulations when they wonder outside set perimeter. Such simulations might include high frequency sounds or low voltage jolts – this approach depends on associative learning [45]. An illustration of a geo (virtual)-fence is shown in Figure 9, with the red boundary showing the grazing area and the blue circle showing an animal grazing outside the boundary.
\nA virtual fence around a grazing area.
Recent research work has focused on improving the efficiency of geo-fencing technologies. Low cost GPS being the most commonly used geo-fencing sensors have an error range of between 5 and 10 m and sometimes take long to locate and lock on to the required number of satellites. In a bid to improve on them, Assisted-GPS and WiFi have been used to respectively improve accuracies and reduce the time-to-first-fix [43]. LoRaWAN has recently been introduced as an alternative protocol for accurate location of animals [44, 46].
\nThough some arguments have been raised with respect to the effectiveness of geo-fencing, such as in the work of [47], rather than purely depending on stimuli, UAVs can be used to steer the animals back into range when they roam out of grazing perimeters. UAVs can therefore provide a cheap and effective way of getting animals back “inline” and are particularly useful when a number of animals stray outside different ends of the perimeter.
\n\n
Limited Constraints: Being air borne they are not hindered by physical constraints such as road/soil terrain, uneven paths and obstacles. They can simply fly over them all.
Shorter travel path: It is well known that the shortest distance between two points is a straight path. UAVs are best suited for this, as they can fly directly in straight paths. This is not always the case with land based vehicles.
Flying dark: In the case of autonomous UAVs, the UAVs can be programmed to fly in pitch darkness or at times with near zero visibility when it would be difficult for humans to manually control them.
Time and labor savings: Activities such as head count, monitoring and mustering often require the employment of more hands to help out. These can be both labor intensive and time consuming. With the use of UAVs, the number of extra laborers required is significantly cut down, while simultaneously saving time. Similarly in crop farming, UAVs can spray crops about 40–60 times faster than human laborers can.
Cost: Beyond savings in time, cutting down on laborers directly translates to cost savings. Though, capable UAVs are not cheap and there is also the added cost incurred in form of electricity to recharge the batteries; the cost savings and advantages of UAVs still significantly outweighs the manual and labor intensive processes of traditional/crude agriculture.
Aerial photography and imaging: With the use of UAVs, farmers can quickly obtain aerial images of their entire farm or select areas of interest. This can be useful in determining when fruits start to sprout or when pests and weeds are choking out crops.
UAVs have seen a wide range of applications in a smart city, all of which contributes greatly to the development of any smart city. In [48] the authors pointed out some of the challenges associated with the use of UAVs. Though these works focused on smart city applications, a number of these challenges are also applicable to the Agricultural space. The challenges were broadly classified into business and technical, and include:
Cost: The technology is perceived as expensive as a result of the technical nature of UAVs. Deployment, integration and training can be very expensive [48]. Similarly, in [49], the authors took a project management perspective to deployment of UAV related projects and highlighted cost as a key element that needs to be considered. It was also noted that proper estimations need to be using various technique prior to undertaking any such project.
Licensing and regulation issues: This is still a gray area with respect to UAVs. Regulations are either none-existent or a loose adaptation of aviation laws, which do not perfectly fit in with UAVs. There is therefore the need to draw up legislation to regulate the new possibilities and application areas of UAVs. Countries such as the USA, the UK, Germany and Spain [31] are leading the way in this direction by drafting guidelines for the use of UAVs and areas over which they can be flown. Other countries of the world are however still some way behind.
Business Adoption: From a business perspective, it might not be out rightly easy to justify the adaptation of UAVs into Agriculture. Though one might argue that there might be cost savings in the long run, counter arguments can be put forward regarding the actual acquisition cost of the UAVs, insurance / replacement of crashed UAVs, purchase of high resolution cameras for imagery as well as the accompanying software solutions and other running costs. When all these are added, it makes it a hard case to sell to farmers and Agriculture business owners.
Technical Challenges: These come in the form of system integration - integration of the middleware services with the UAV, high performance systems for data analytics, Net-centric infrastructure which enable any member of a team to control the UAV and retrieve imagery and sensor information in real time and application of machine learning / computation intelligence to identify and retrieve useful insights from the large pool of data.
Ethics and privacy: Some feel that the use of UAVs for monitoring and surveillance would lead to the invasion of their privacy. A lack of standard operational and technological procedures needed for safe performance of the UAVs is a great challenge. There could be GPS-jamming and hacking because of the vulnerabilities in the command and control of UAV operations.
\n
Limited flight time: UAV flight time is largely dependent on battery capacity. In most UAVs, particularly the multi-rotor, batteries can often times only sustain a flight time of between 10 and 30 minutes, and can be less when flown during high wind speeds. For activities such as crop spraying UAVs are only effective on hills, small areas, and in areas where other equipment cannot easily reach, for longer distance/range they are less efficient and even more costly than larger ground-based crop spraying equipment. The same challenge can be seen in the area of NDVI imaging, where farmers obtain NDVI images to assess the plant health. Alternative solutions are airplanes and satellites. While UAVs are the most cost-effective for small areas, they are currently not competitive against planes and satellites for larger areas.
There is the need to improve battery technology and find a way of using batteries with bigger capacity yet small footprint in UAVs. The use of solar photo-voltaic cells to power UAVs, such as [50] or the hybrid fixed-wing might be promising direction to be explored.
Limited payload size: Due to the small size of most UAVs, they are unable to carry a lot at once. This therefore limits their applications to basic aerial photography and observation. Though there are large-size UAVs such as [51], these are still limited in terms of flight time which is even further shortened when they are fully loaded. This limitation is prominent in application of UAVs in crop dusting (spraying pest/weed controlling chemicals or fertilizer on crops). Large gas powered monocopters might be a potential solution to this challenge.
Autonomy of UAVs: The possibilities of UAVs in Agriculture are numerous. However, most are currently being manually operated by humans. This limits their applications to certain times of the day when there is clear visibility. Advancements in computational intelligence specifically in areas of navigation, obstacle avoidance automatic sensing and actuation (performing pre-programmed tasks) can further accelerate the acceptance and usage of UAVs in livestock Agriculture.
Data Processing: Recent research works have shown the importance of data and information in almost all areas of human endeavor. Agriculture is certainly no exception. The use of UAVs as data gathering tools is still very much in its infancy. There is the need to develop effective techniques for data acquisition, data muling and most importantly converting these data to useful information. For instance, by observing the movement and body temperature of cows, farmers might be able to detect possible health related issues early on before they become fatal.
Empowering Farmers: In an article titled “on Drone technology as a tool for improving agricultural productivity”, in [2] the authors identified empowering farmers as a vital process in improving agriculture. They concluded that it’s one thing to have the technology and have the ability to gather billions of data for analysis, however all these are of no use, if they cannot be properly integrated and applied into agricultural business processes; where it can bring the much needed improvement. This can only be done by empowering the farmers themselves – either through formal class room education or informal practical demonstrations.
Cost: The ideal UAV for agriculture applications is one that has a good balance of durability, long flight time, stability and optional ability to fly autonomously. Such a device would cost much more than an average farmer might be able to afford. Most especially for farmers in developing countries. For those in much developed countries, there might also be the challenge of justifying how the purchase of such expensive devices can directly translate to measureable profit. To this end Farmers are still largely depend on manual ways of carrying out their farm operations.
Safety: There are also safety concerns with the use of UAV in Agriculture. One such is the UAVs’ inability to recognize and avoid other airborne aircrafts and objects within the same airspace. This could result in collisions. Though obstacle avoidance is not too far-fetched, incorporating such features into basic UAVs would further drive up the already expensive cost of the UAVs.
Availability: There is also the problem of manufacturing, and meeting up with the demands for UAVs by farmers. This is largely expected since the industry is still exploring and testing Agricultural use cases. Manufacturing is being done on a small scale and the fixed costs remain high. In [22], it was pointed out that despite the numerous potential advantages of thermal remote sensing has over the optical RS in crop and soil monitory, there are a number of practical difficulties in its use. These include but not limited to atmospheric attenuation and absorption, calibration, climate conditions, crop growth stages as well as complex soil and plant interaction that have thus far limited its use in the agricultural sector.
Unmanned Aerial Vehicles or UAVs are essentially flying robots. Though initially designed for military use, they have are now widely used in various areas, from recreational sports, fire-fighting, flight simulations / trainings to toys for children. In this chapter we presented an application of UAVs to commercial Agriculture. We presented four major types of UAVs, and though the multi-rotor UAV with its ability to hover on spot and take-off and landing vertical may seem well suited for agriculture, its limited flight time is a major limitation. The hybrid-fixed-wing-motor-rotor might be a better fit. A detailed insight into the applications of UAVs in crop production and livestock farming was also presented. A prominent requirement for most UAV application in Agriculture is an integrated camera, as it allows images to be taken. Images are used in weed identification and control, soil analysis, animal monitoring, animal head counts, geo-fencing, mustering among others. Like most machines, UAVs have the advantage of doing repetitive and monotonous works better and more efficiently when compared to humans. Some advantages of applying UAVs in Agriculture were presented, some of which include limited path constraints, time saving and reduction in manual labor. However, there are a number of challenges limiting UAVs, most prominent among which is cost. UAVs that are well suited for Agriculture use are expensive. Operation and maintenance also come at a cost. It is therefore often difficult to convince farmers and Agriculture related stakeholders to integrate UAVs into their business. Beyond cost, battery limitations, safety and legal related issues are still major hurdles that need to be scaled before UAVs can find a strong foothold in agriculture.
\nconvolutional neural network
Mearly season site-specific weed management
geographic information system
global positioning system
leaf area index
long range wide area network
livestock production systems
normalized difference vegetation index
object-based image analysis
codequick response code
radio frequency identification
scale-invariant feature transformation
unmanned aerial vehicle
vertical take off and landing
Supporting women in scientific research and encouraging more women to pursue careers in STEM fields has been an issue on the global agenda for many years. But there is still much to be done. And IntechOpen wants to help.
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\\n\\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
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\\n\\nAdvantages of Publishing with IntechOpen
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\n\nWe aim to publish 100 books in our Women in Science program over the next three years. We are looking for books written, edited, or co-edited by women. Contributing chapters by men are welcome. As always, the quality of the research we publish is paramount.
\n\nAll project proposals go through a two-stage peer review process and are selected based on the following criteria:
\n\nPlus, we want this project to have an impact beyond scientific circles. We will publicize the research in the Women in Science program for a wider general audience through:
\n\nInterested? If you have an idea for an edited volume or a monograph, we’d love to hear from you! Contact Ana Pantar at book.idea@intechopen.com.
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