2018
2
4
6
95
Developing Four Metaheuristic Algorithms for MultipleObjective Management of Groundwater
2
2
Groundwater is one of the important sources of freshwater and accordingly, there is a need for optimizing its usage. In this paper, four multiobjective metaheuristic algorithms with new evolution strategy are introduced and compared for the optimal management of groundwater namely: Multiobjective genetic algorithms (MOGA), multiobjective memetic algorithms (MOMA), multiobjective particle swarm optimization (MOPSO), and multiobjective shuffled frog leaping algorithm (MOSFLA). The suggested evolution process is based on determining a unique solution of the Pareto solutions called the Paretocompromise (PC) solution. The advantages of the current development stem from: 1) The new multiple objectives evolution strategy is inspired from the single objective optimization, where fitness calculations depend on tracking the PC solution only through the search history; 2) a comparison among the performance of the four algorithms is introduced. The development of each algorithm is briefly presented. A comparison study is carried out among the formulation and the results of the four algorithms. The developed four algorithms are tested on two multipleobjective optimization benchmark problems. The four algorithms are then used to optimize twoobjective groundwater management problem. The results prove the ability of the developed algorithms to accurately find the Paretooptimal solutions and thus the potential application on reallife groundwater management problems.
1

1
22


Hamdy
ElGhandour
Irrigation &amp; Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt
Irrigation &amp; Hydraulics Department,
Egypt
hamdy_elghandour@yahoo.com


Emad
Elbeltagi
Structural Engineering Dept., Fac. of Engrg., Mansoura Univ., Mansoura 35516, Egypt
Structural Engineering Dept., Fac. of Engrg.,
Egypt
eelbelta@mans.edu.eg
Genetic Algorithms
Memetic algorithms
Particle swarm
Shuffled frog leaping
Compromise solution
Multiple objectives optimization
Optimum Design of Structures Against earthquake by Simulated Annealing Using Wavelet Transform
2
2
Optimization of earthquakeaffected structures is one of the most widely used methods in structural engineering. In this paper optimum design of structures is achieved by simulated annealing method. The evolutionary algorithm is employed for optimum design of structures. To reduce the computational work, a discrete wavelet transform is used by means of which the number of points in the earthquake record is decreased. The loads are considered as earthquake loads. A time history analysis is carried out for the dynamic analysis. By discrete wavelet transform (DWT) the earthquake record is decomposed into a number of points. Then in the optimization process, the structures are analyzed for these points. To reconstruct the actual responses from these points, a reverse wavelet transform (RWT) was used. A number of space structures are designed for minimum weight and the results are compared with exact dynamic analysis. The result show, DWT and RWT were an effective approach for reducing the computational cost of optimization.
1

23
33


Ali
Heidari
Associate Professor, Department of Civil Engineering, Shahrekord University, Shahrekord, Iran
Associate Professor, Department of Civil
Iran
heidari@eng.sku.ac.ir


Jalil
Raeisi
M.Sc., Department of Civil Engineering, Shahrekord University, Shahrekord, Iran
M.Sc., Department of Civil Engineering, Shahrekord
Iran
jalilraeisidehkordi@gmail.com
Simulated Annealing
Discrete Wavelet transform
Reverse Wavelet Transform
Dynamic analysis
[[1] Khademi F, Akbari M, Jamal SM. Prediction of compressive strength of concrete by datadriven models. imanager's Journal on Civil Engineering 2015; 5(2):16.##[2] Behfarnia K, Khademi F. A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neurofuzzy inference system. Iran University of Science & Technology 2017; 7(1):7180.##[3] Salajegheh E, Salajegheh J. Heidari A. Continuousdiscrete optimization of structures using secondorder approximation. International Journal of Engineering 2004; 17:22742.##[4] Salajegheh E, Heidari A. Optimum design of structures against earthquake by adaptive genetic algorithm using wavelet networks. Structural and Multidisciplinary Optimization 2004; 28:27785.##[5] Salajegheh E, Heidari A. Optimum design of structures against earthquake by wavelet neural network and filter banks. Earthquake Engineering & Structural Dynamics 2005; 34:6782.##[6] Salajegheh E, Heidari A, Saryazdi S. Optimum design of structures against earthquake by discrete wavelet transform. International Journal for Numerical Methods in Engineering 2005; 62:217892.##[7] Heidari A. Optimum Design of Structures for Earthquake Induced Loading by Genetic Algorithm Using Wavelet Transform. Advances in Applied Mathematics & Mechanics 2010; 2:10717.##[8] Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by simulated annealing. science. 1983; 220:67180.##[9] Cheng F.Y. D. Li J.Ger, Multiobjective optimization of dynamic structures, ASCE Structures 2000 Conference Proceedings, 2000.##[10] Gholizadeh S, Salajegheh E. Optimal design of structures subjected to time history loading by swarm intelligence and an advanced metamodel. Computer Methods in Applied Mechanics and Engineering 2009; 198:293649.##[11] Kaveh A, Talatahari S. A novel heuristic optimization method: charged system search. Acta Mechanica. 2010; 213:26789.##[12] Kaveh A, Talatahari S. Optimal design of skeletal structures via the charged system search algorithm. Structural and Multidisciplinary Optimization. 2010; 41:893911.##[13] Kaveh A, Talatahari S. A charged system search with a fly to boundary method for discrete optimum design of truss structures. Asian Journal of Civil Engineering 2010; 11:277–93.##[14] Heidari A, Salajegheh, E. Approximate dynamic analysis of structures for earthquake loading using FWT, International Journal of Engineering (IJE) 2007; 20:111.##[15] Heidari A, Salajegheh E. Wavelet analysis for processing of earthquake records. Asian Journal of Civil Engineering. 2008; 9:51324.##[16] Heidari A, Raeisi J, Kamgar R. Application of wavelet theory in determining of strong ground motion parameters. International Journal of Optimization in Civil Engineering. 2018; 8:10315.##[17] Naderpour H, Fakharian PA. Synthesis of peak picking method and wavelet packet transform for structural modal identification. KSCE Journal of Civil Engineering 2016; 20:2859–67.##[18] Roderick JBTM. Wavelets for signal and image processing. Lecture notes, Department of Computing Science, Rijksuniversiteit Groningen, NL, 1993.##[19] Bennage WA, Dhingra AK. Single and multiobjective structural optimization in discrete‐continuous variables using simulated annealing. International Journal for Numerical Methods in Engineering. 1995; 38:275373.##[20] Hasançebi O, Erbatur F. Layout optimization of trusses using simulated annealing. Advances in Engineering Software. 2002; 33:68196.##[21] Chen GS, Bruno RJ, Salama M. Optimal placement of active/passive members in truss structures using simulated annealing. AIAA journal. 1991; 29:132734.##[22] Paz M. Structural dynamics:theory and computation, McGraw Hill, New York, 1997.##[23] Daubechies I. Ten lectures on wavelets. CBMSNSF Conference Series in Applied Mathematics, 1992.##[24] Farge M. Wavelet transforms and their application to turbulence, Annual Reviews Fluid Mechanics 1992; 24:395457.##]
Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks
2
2
The paper presents the prediction of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using the artificial neural network. The input parameters for the artificial neural network model were normalised skirt depth, area of the footing and the friction angle of the sand, while the output was the ultimate bearing capacity. The artificial neural network algorithm uses a back propagation model. The training of artificial neural network model has been conducted and the weights were obtained which described the relationship between the input parameters and output ultimate bearing capacity. Further, the sensitivity analysis has been performed and the parameters affecting the ultimate bearing capacity of different regular shaped skirted footing resting on the sand were identified. The study shows that the prediction accuracy of the ultimate bearing capacity of different regular shaped skirted footing resting on sand using artificial neural network model was quite good.
1

34
46


Rakesh
Dutta
Type IV Special
Type IV Special
India
rakeshkdutta@yahoo.com


Radha
Rani
PG Student
PG Student
India
r2.radha@gmail.com


Tammineni
Rao
Ph.D. Scholar
Ph.D. Scholar
India
anandrcwing@gmail.com
Different regular shaped skirted footings
Ultimate bearing capacity
Feed forward backpropagation algorithm
Artificial neural network and Multiple regression analysis
Prediction of Concrete and Steel Materials Contained by Cantilever Retaining Wall by Modeling the Artificial Neural Networks
2
2
In this study, the Artificial Neural Network (ANN) application is implemented for predicting the required concrete volume and amount of the steel reinforcement within the inversedTshaped and stemstepped reinforced concrete (RC) walls. For this aim, sevendifferent RC wall designs were approached differentiated within the wall heights and various internal friction angles of backfill materials. Each RC wall is proportionally designed and subjected to active lateral earth pressure defined with the MononobeOkabe approach foreseen by Turkish Specification for Building to be Built in Seismic Zones (TSC2007)[14]. Following the stability analysis of the RC retaining walls, the structural and reinforced concrete analyses are performed according to the Turkish Standard on Requirements for Design and Construction in Reinforced Concrete Structures (TS5002000)[15]. Input parameters such as concrete volumes, weights of the steel bars, soil and wall material properties are subjected to the ANN modeling. The prediction of the concrete volume and amount of the steel bars are achieved with the implementation of the ANN model trained with the Artificial Bee Colony (ABC) algorithm. As a result of this study, it is revealed that ANN models are useful for verifying the existing RC retaining wall designs or performing preliminary designs for the Lshaped and stemstepped cantilever retaining walls.
1

47
61


Umit
Gokkus
Civil Engineering Department, Engineering Faculty, Manisa Celal Bayar University, Manisa/TURKEY
Civil Engineering Department, Engineering
Turkey
umit.gokkus@cbu.edu.tr


Mehmet
Yildirim
Civil Eng Dept., Engineering Faculty, Manisa Celal Bayar University, Manisa/TURKEY
Civil Eng Dept., Engineering Faculty, Manisa
Turkey
mehmetsinan.yildirim@cbu.edu.tr


Arif
Yilmazoglu
Civil Eng.Dept.Institute of Natural and Applied Sciences, Manisa Celal Bayar University, Manisa/TURKEY
Civil Eng.Dept.Institute of Natural and Applied
Turkey
arifyilmazoglu@hotmail.com
Inverse TShaped Retaining Walls
StemStepped Walls
ReinforcedConcrete Walls
Application of Neural Network
Artificial Bee ColonyBased Preliminary Wall Design
Scale Effect and Anisotropic Analysis of Rock Joint Roughness Coefficient Neutrosophic Interval Statistical Numbers Based on Neutrosophic Statistics
2
2
In rock mechanics, mechanical properties of rock masses in nature imply complexity and diversity. The shear strength of rock mass is a key factor for affecting the stability of the rock mass. Then, the joint roughness coefficient (JRC) of rock indicates an important parameter in the shear strength and stability analysis of rock mass. Since the nature of the rock mass is indeterminate and incomplete to some extent, we cannot always express rock JRC by a certain/exact number. Therefore, this paper introduces neutrosophic interval statistical numbers (NISNs) based on the concepts of neutrosophic numbers and neutrosophic interval probability to express JRC data of the rock mass in indeterminate setting. Then we present the calculational method of the neutrosophic average value and standard deviation of NISNs based on neutrosophic statistics. Next, by an actual case, the neutrosophic average value and standard deviation of the rock JRC NISNs are used to analyze the scale effect and anisotropy of the rock body corresponding to different sample lengths and measuring directions. Lastly, the analysis method of the scale effect and anisotropy for JRC NISNs shows its effectiveness and rationality in the actual case study.
1

62
71


Wenzhong
Jiang
School of Civil Engineering, Shaoxing University, Shaoxing, Zhejiang 312000, China
School of Civil Engineering, Shaoxing University,
China
17306565715@163.com


Jun
Ye
Department of Electrical and Information Engineering, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang Province 312000, P.R. China
Department of Electrical and Information
China
yehjun@aliyun.com


Wenhua
Cui
Department of Electrical Engineering and Automation, Shaoxing University, Shaoxing, Zhejiang 312000, China
Department of Electrical Engineering and
China
wenhuacui@usx.edu.cn
Joint roughness coefficient (JRC)
Neutrosophic interval probability
Neutrosophic interval statistical number
Neutrosophic statistics
Scale effect
Anisotropy
Application of adaptive Neurofuzzy inference system to estimate alongshore sediment transport rate (A real case study: southern shorelines of Caspian Sea)
2
2
Many empirical models have been introduced by scientists during the recent decades for estimating longshore sediment transport rate, but these approaches have been calibrated and applied under limited conditions of the bed profile and specific range of the bed sediment size. The existing empirical relations are linear or exponential regressions based on the observation and measurements data and there’s a great potential to build more accurate models to predict sediment transport phenomena by means of soft computation approach. This paper presents a novel case study application of the adaptive Neurofuzzy inference system (ANFIS) as a superior modeling technique for estimation of the longshore sediment transport rate in the southern shorelines of the Caspian Sea. The results will be compared with top three popular existing empirical equations. Daily grab samples from four stations were collected in the period of March 2012 through June 2012. The trained ANFIS model outperformed the existing regressiontype empirical equations for the estimation of the alongshore sediment transport rate due to the adaptive structure of the ANFIS model to better fit complex systems.
1

72
85


Tayeb
Sadeghifar
Faculty of Marine Sciences, Tarbiat Modares University, Tehran, Iran
Faculty of Marine Sciences, Tarbiat Modares
Iran
t.sadeghifar@modares.ac.ir


Reza
Barati
Faculty of Civil and Environmental Engineering, Tarbiat Modares University, Tehran
Faculty of Civil and Environmental Engineering,
Iran
r88barati@gmail.com
Alongshore sediment transports rate
Semiempirical formula
adaptive neurofuzzy inference system
Caspian Sea
An Equation to Determine the Ultimate Flexural Load of RC Beams Strengthened with CFRP Laminates
2
2
In this paper, a new relationship is presented for determining the ultimate flexural load of reinforced concrete beams strengthened with CFRP laminates. An artificial neural network with a suitable performance was used to estimate this equation. First, a collection of laboratory results including 83 data was collected from valid references. This database was then divided into three groups of 51, 16, and 16, which were used to train, validation, and test the proposed equation, respectively. The final model had eleven inputs including concrete compressive strength, width of beam, effective depth, area of tension reinforcement, area of compression reinforcement, yield strength of steel, modulus of elasticity of steel, modulus of elasticity of CFRP sheet, width of CFRP sheet, total thickness of CFRP sheets and, length of CFRP sheet, which were applied to the network to determine the ultimate flexural load as the output of the model. The obtained results from the proposed relationship showed that it was able to use as a predictive equation for the considered target.
1

86
95


Reza
Farahnaki
The University of Wollongong, Wollongong, Australia
The University of Wollongong, Wollongong,
Australia
rf847@uowmail.edu.au


Alla
Azimi
Department of Civil Engineering, University of Birmingham, Birmingham, United Kingdom
Department of Civil Engineering, University
United Kingdom
alla.azimi@gmail.com
concrete beam
Flexural load
FRP
strengthening
[[1] Seo SY, Choi KB, Kwon YS. Retrofit Capacity of NearSurfaceMounted RC Beam by using FRP Plate. J Korea Inst Struct Maint Insp 2012;16:18–26. doi:10.11112/jksmi.2012.16.1.018.##[2] Dolan CW, Swanson D. Development of flexural capacity of a FRP prestressed beam with vertically distributed tendons. Compos Part B Eng 2002;33:1–6. doi:10.1016/S13598368(01)000531.##[3] Kheyroddin A, Mirrashid M, Arshadi H. An Investigation on the Behavior of Concrete Cores in Suspended Tall Buildings. Iran J Sci Technol Trans Civ Eng 2017;41:383–8. doi:10.1007/s409960170075y.##[4] Qu HC, Wu CQ, Chen LL. Numerical Analysis on the LoadCarrying Capacity for the FRP Reinforced FourPoint Bending Concrete Beam. Adv Mater Res 2011;287–290:1130–4. doi:10.4028/www.scientific.net/AMR.287290.1130.##[5] Xu X. Calculation Method and Analysis of Bearing Capacity of FRP Rebar Concrete Beam. ICTE 2011, Reston, VA: American Society of Civil Engineers; 2011, p. 1572–7. doi:10.1061/41184(419)260.##[6] Zatloukal J, Konvalinka P. Moment Capacity of FRP Reinforced Concrete Beam Assessment Based on Centerline Geometry. Appl Mech Mater 2013;486:211–6. doi:10.4028/www.scientific.net/AMM.486.211.##[7] Jafari M, Mirrashid M, Vahidnia A. Prediction of chloride penetration in the concrete containing magnetite aggregates by Adaptive Neural Fuzzy Inference System (ANFIS). 7th Internatinal Symp. Adv. Sci. Technol. (5thsastech), Bandare Abbas, Iran, 2013.##[8] Mirrashid M. Earthquake magnitude prediction by adaptive neurofuzzy inference system (ANFIS) based on fuzzy Cmeans algorithm. Nat Hazards 2014;74:1577–93. doi:10.1007/s1106901412647.##[9] Mirrashid M. Comparison Study of Soft Computing Approaches for Estimation of the NonDuctile RC Joint Shear Strength. Soft Comput Civ Eng 2017;1:12–28. doi:10.22115/scce.2017.46318.##[10] Mirrashid M, Bigdeli S. Genetic Algorithm for Prediction the Compressive Strength of Mortar Containing Wollastonite. 1st Natl. Congr. Counstruction Eng. Proj. Assessment, Gorgan, Iran, 2014.##[11] Mirrashid M, Givehchi M, Miri M, Madandoust R. Performance investigation of neurofuzzy system for earthquake prediction. Asian J Civ Eng 2016;17:213–23.##[12] Mirrashid M, Jafari M, Akhlaghi A, Vahidnia A. Prediction of compressive strength of concrete containing magnetite aggregates using Adaptive Neural Fuzzy Inference System (ANFIS) n.d.##[13] Naderpour H, Mirrashid M. Application of Soft Computing to Reinforced Concrete Beams Strengthened with Fibre Reinforced Polymers: A StateoftheArt Review. Comput Tech Civ Struct Eng n.d.:305–23.##[14] Naderpour H, Mirrashid M. Compressive Strength of Mortars Admixed with Wollastonite and Microsilica. Mater Sci Forum 2017;890:415–8. doi:10.4028/www.scientific.net/MSF.890.415.##[15] Naderpour H, Mirrashid M. Ultimate Capacity Prediction of Concrete Slabs Reinforced with FRP Bars. 3rd Int. 7th Natl. Conf. Mod. Mater. Struct. Civ. Eng. BuAli Sina Univ. Hamedan, IRAN, 2018.##[16] Naderpour H, Mirrashid M. Application of group method of data handling to Estimate the Shear Strength of RC Beams Reinforced with FRP Bars. 3rd Int. 7th Natl. Conf. Mod. Mater. Struct. Civ. Eng. BuAli Sina Univ. Hamedan, IRAN, 2018.##[17] Naderpour H, Mirrashid M. Shear Strength Prediction of RC Beams Using Adaptive NeuroFuzzy Inference System. Sci Iran 2018:0–0. doi:10.24200/sci.2018.50308.1624.##[18] Naderpour H, Mirrashid M. An innovative approach for compressive strength estimation of mortars having calcium inosilicate minerals. J Build Eng 2018;19:205–15. doi:10.1016/j.jobe.2018.05.012.##[19] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.##[20] Naderpour H, Vahdani R, Mirrashid M. Soft Computing Research in Structural Control by Mass Damper (A review paper). 4st Int. Conf. Struct. Eng. Tehran, Iran, 2018.##[21] HAJELA P, BERKE L. Neurobiological Computational Models in Structural Analysis and Design. 31st Struct. Struct. Dyn. Mater. Conf., Reston, Virigina: American Institute of Aeronautics and Astronautics; 1990. doi:10.2514/6.19901133.##[22] Saadatmanesh H, Ehsani MR. RC Beams Strengthened with GFRP Plates. I: Experimental Study. J Struct Eng 1991;117:3417–33. doi:10.1061/(ASCE)07339445(1991)117:11(3417).##[23] C. Allen Ross Joseph W. Tedesco, and Mary L. Hughes DMJ. Strengthening of Reinforced Concrete Beams with Externally Bonded Composite Laminates. Struct J n.d.;96. doi:10.14359/612.##[24] Ni HG, Wang JZ. Prediction of compressive strength of concrete by neural networks. Cem Concr Res 2000;30:1245–50. doi:10.1016/S00088846(00)003458.##[25] Du Béton FI. Externally bonded FRP reinforcement for RC structures. Bulletin 2001;14:138.##[26] Sanad A, Saka MP. Prediction of Ultimate Shear Strength of ReinforcedConcrete Deep Beams Using Neural Networks. J Struct Eng 2001;127:818–28. doi:10.1061/(ASCE)07339445(2001)127:7(818).##[27] Dong Y, Zhao M, Ansari F. Failure characteristics of reinforced concrete beams repaired with CFRP composites. Strain 2002;304:12–7.##[28] Dai JG, Ueda T, Sato Y, Ito T. Flexural strengthening of RC beams using externally bonded FRP sheets through flexible adhesive bonding. Int. Symp. Bond Behav. FRP Struct. (BBFS 2005), Hong Kong, 2005, p. 7–9.##[29] Ebead UA, Marzouk H. Tensionstiffening model for FRPstrenghened RC concrete twoway slabs. Mater Struct 2005;38:193–200. doi:10.1007/BF02479344.##[30] Kotynia R. Debonding failures of RC beams strengthened with externally bonded strips. Proc. Int. Symp. Bond Behav. FRP Struct. (BBFS 2005), 2005.##[31] Lundqvist J, Nordin H, Täljsten B, Olofsson T. Numerical analysis of concrete beams strengthened with CFRP : a study of anchorage lengths. Int Symp Bond Behav FRP Struct 07/12/2005  09/12/2005 2005:239–46.##[32] Maalej M, Leong KS. Effect of beam size and FRP thickness on interfacial shear stress concentration and failure mode of FRPstrengthened beams. Compos Sci Technol 2005;65:1148–58. doi:10.1016/j.compscitech.2004.11.010.##[33] Coronado CA, Lopez MM. Sensitivity analysis of reinforced concrete beams strengthened with FRP laminates. Cem Concr Compos 2006;28:102–14. doi:10.1016/j.cemconcomp.2005.07.005.##[34] Reeve BZ. Effect of adhesive stiffness and CFRP geometry on the behavior of externally bonded CFRP retrofit measures subject to monotonic loads 2006.##[35] Abdalla JA, Elsanosi A, Abdelwahab A. Modeling and simulation of shear resistance of R/C beams using artificial neural network. J Franklin Inst 2007;344:741–56. doi:10.1016/j.jfranklin.2005.12.005.##[36] Esfahani MR, Kianoush MR, Tajari AR. Flexural behaviour of reinforced concrete beams strengthened by CFRP sheets. Eng Struct 2007;29:2428–44. doi:10.1016/j.engstruct.2006.12.008.##[37] Neagoe CA. Concrete beams reinforced with CFRP laminates 2011.##]