Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings

Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC bu...

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Vydáno v:	Neural computing & applications Ročník 28; číslo 8; s. 2005 - 2016
Hlavní autoři:	Chatterjee, Sankhadeep, Sarkar, Sarbartha, Hore, Sirshendu, Dey, Nilanjan, Ashour, Amira S., Balas, Valentina E.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Springer London 01.08.2017 Springer Nature B.V
Témata:	Accuracy Algorithms Artificial Intelligence Artificial neural networks Classifiers Collapse Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Concrete construction Data Mining and Knowledge Discovery Feature extraction Hazards Image Processing and Computer Vision Learning theory Machine learning Multistory buildings Neural networks Original Article Particle swarm optimization Predictions Probability and Statistics in Computer Science Reinforced concrete Root-mean-square errors Structural design Structural failure Multilayer perceptron feed-forward network Structural failure Cross-entropy Scaled conjugate gradient algorithm Artificial neural network Reinforced concrete structures Particle swarm optimization
ISSN:	0941-0643, 1433-3058
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Abstract	Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.
AbstractList	Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure. Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as it leads to human life danger as well as wasting time and property. Using traditional methods for predicting structural failure of the RC buildings will be time-consuming and complex. Recent research proved the artificial neural network (ANN) potentiality in solving various real-life problems. The traditional learning algorithms suffer from being trapped into local optima with a premature convergence. Thus, it is a challenging task to achieve expected accuracy while using traditional learning algorithms to train ANN. To solve this problem, the present work proposed a particle swarm optimization-based approach to train the NN (NN-PSO). The PSO is employed to find a weight vector with minimum root-mean-square error (RMSE) for the NN. The proposed (NN-PSO) classifier is capable to tackle the problem of predicting structural failure of multistoried reinforced concrete buildings via detecting the failure possibility of the multistoried RC building structure in the future. A database of 150 multistoried buildings’ RC structures was employed in the experimental results. The PSO algorithm was involved to select the optimal weights for the NN classifier. Fifteen features have been extracted from the structural design, while nine features have been opted to perform the classification process. Moreover, the NN-PSO model was compared with NN and MLP-FFN (multilayer perceptron feed-forward network) classifier to find its ingenuity. The experimental results established the superiority of the proposed NN-PSO compared to the NN and MLP-FFN classifiers. The NN-PSO achieved 90 % accuracy with 90 % precision, 94.74 % recall and 92.31 % F-Measure.
Author	Hore, Sirshendu Sarkar, Sarbartha Dey, Nilanjan Chatterjee, Sankhadeep Ashour, Amira S. Balas, Valentina E.
Author_xml	– sequence: 1 givenname: Sankhadeep surname: Chatterjee fullname: Chatterjee, Sankhadeep organization: Department of Computer Science and Engineering, University of Calcutta – sequence: 2 givenname: Sarbartha surname: Sarkar fullname: Sarkar, Sarbartha organization: Department of Civil Engineering, Hooghly Engineering and Technology College – sequence: 3 givenname: Sirshendu surname: Hore fullname: Hore, Sirshendu organization: Department of Computer Science and Engineering, Hooghly Engineering and Technology College – sequence: 4 givenname: Nilanjan surname: Dey fullname: Dey, Nilanjan organization: Department of Information Technology, Techno India College of Technology – sequence: 5 givenname: Amira S. surname: Ashour fullname: Ashour, Amira S. email: amirasashour@yahoo.com organization: Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University – sequence: 6 givenname: Valentina E. surname: Balas fullname: Balas, Valentina E. organization: Faculty of Engineering, Aurel Vlaicu University of Arad
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Keywords	Multilayer perceptron feed-forward network Structural failure Cross-entropy Scaled conjugate gradient algorithm Artificial neural network Reinforced concrete structures Particle swarm optimization
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artificial neural network in fixed offshore structuresIndian J Mar Sci2015443 JoghataieAMojtabaFDynamic analysis of nonlinear frames by Prandtl neural networksJ Eng Mech20081341196196910.1061/(ASCE)0733-9399(2008)134:11(961) ChenBLiuWMobile agent computing paradigm for building a flexible structural health monitoring sensor networkComput Aided Civil Infrastruct Eng201025750451610.1111/j.1467-8667.2010.00656.x GuyonIElisseeffAAn introduction to variable and feature selectionJ Mach Learn Res20033115711821102.68556 CaglarNElmasMYamanZDSaribiyikMNeural networks in 3-dimensional dynamic analysis of reinforced concrete buildingsConstr Build Mater200822578880010.1016/j.conbuildmat.2007.01.029 AwanSMAslamMKhanZASaeedHAn efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecastingNeural Comput Appl2014257–81967197810.1007/s00521-014-1685-y PierceSWordenKMansonGA novel information-gap technique to assess reliability of neural 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AzarATEl-SaidSABalasVEOlariuTLinguistic hedges fuzzy feature selection for differential diagnosis of Erythemato-Squamous diseasesSoft Comput Appl AISC201319548750010.1007/978-3-642-33941-7_43 BagciMNeural network model for moment-curvature relationship of reinforced concrete sectionsMath Comput Appl2010151667805802297 KameliIMiriMRajiAPrediction of target displacement of reinforced concrete frames using artificial neural networksAdv Mater Res20112552345234910.4028/www.scientific.net/AMR.255-260.2345 HanJKamberMData mining: concepts and techniques20052San FranciscoMorgan and Kaufmann285378 MarenAJHarstonCTPapRMHandbook of neural computing applications2014San DiegoAcademic Press0778.68010 MacIntyreJApplications of neural computing in the twenty-first century and 21 years of neural computing and applicationsNeural Comput Appl2013233–465766510.1007/s00521-013-1471-2 StratmanBMahadevanSLiCBiswasGIdentification of critical inspection samples among railroad wheels by similarity-based agglomerative clusteringIntegr Comput Aided Eng2011183203219 RahmanianBPakizehMMansooriSAAEsfandyariMJafariDMaddahHMaskookiAPrediction of MEUF process performance using artificial neural networks and ANFIS approachesJ Taiwan Inst Chem Eng201243455856510.1016/j.jtice.2012.01.002 CoelloCoelloCAPulidoGTMultiobjective structural optimization using a microgenetic algorithmStruct Multidiscip Optim200530538840310.1007/s00158-005-0527-z MaizirHKassimKANeural network application in prediction of axial bearing capacity of driven pilesProc Int Multiconf Eng Comput Sci2013220215155 Standard, Indian (2000) ‘IS-456. 2000’ Plain and Reinforced Concrete-Code of Practice. Bureau of Indian Standards Manak Bhavan. 9 Bahadur Shah Zafar Marg New Delhi 110002 SochaKBlumCAn ant colony optimization algorithm for continuous optimization: application to feed-forward neural network trainingNeural Comput Appl200716323524710.1007/s00521-007-0084-z BaughmanDRLiuYANeural networks in bioprocessing and chemical engineering2014San DiegoAcademic press ChenJ-FDoQHHsiehH-NTraining artificial neural networks by a hybrid PSO-CS algorithmAlgorithms20158292308336640910.3390/a8020292 KarayiannisNVenetsanopoulosANArtificial neural networks: learning algorithms, performance evaluation, and applications2013New YorkSpringer Science & Business Media0817.68121 KausarNPalaniappanSAlGhamdiBSSamirBBDeyNAbdullahASystematic analysis of applied data mining based optimization algorithms in clinical attribute extraction and classification for diagnosis of cardiac patientsAppl Intell Optim Biol Med Ser Intell Syst Ref Libr201596217231 GuptaRKewalramaniMGoelAPrediction of concrete strength using neural-expert systemJ Mater Civ Eng200618346246610.1061/(ASCE)0899-1561(2006)18:3(462) RojasRNeural networks: a systematic introduction2013BerlinSpringer Science & Business Media0861.68072 CiancioCAmbrogioG GagliardiFMusmannoRHeuristic techniques to optimize neural network architecture in manufacturing applicationsNeural Comput Appl2015 Chakraborty S, Samanta S, Mukherjee A, Dey N, Chaudhuri SS (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. In: 2013 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, 26–28 Dec 2013 ParkHSAdeliHDistributed neural dynamics algorithms for optimization of large steel structuresJ Struct Eng1997123788088810.1061/(ASCE)0733-9445(1997)123:7(880) GrafWFreitagSKaliskeMSickertJURecurrent neural networks for uncertain time-dependent structural behaviorComput Aided Civ Infrastruct Eng201025532233310.1111/j.1467-8667.2009.00645.x SiddiqueeMSAHossainMMADevelopment of a sequential artificial neural network for predicting river water levels based on Brahmaputra and Ganges water levelsNeural Comput Appl20152681979199010.1007/s00521-015-1871-6 GaoSNingBDongHAdaptive neural control with intercepted adaptation for time-delay saturated nonlinear systemsNeural Comput Appl20152681849185710.1007/s00521-015-1855-6 MukherjeeADespandeJMModeling initial design process using artificial neural networksJ Comput Civ Eng19959319420010.1061/(ASCE)0887-3801(1995)9:3(194) ArslanMHCeylanMKoyuncuTAn ANN approaches on estimating earthquake performances of existing RC buildingsNeural Netw World201222544310.14311/NNW.2012.22.027 MirjaliliSZSaremiSMirjaliliSMDesigning evolutionary feedforward neural networks using social spider optimization algorithmNeural Comput Appl20152681919192810.1007/s00521-015-1847-6 KiaASensoySClassification of earthquake-induced damage for R/C slab column frames using multiclass SVM and its combination with MLP neural networkMath Probl Eng2014201411410.1155/2014/734072 ZhangTOn the consistency of feature selection using greedy least squares regressionJ Mach Learn Res20091055556824917491235.62096 McEntireDADisaster response and recovery: strategies and tactics for resilience2014HobokenJohn Wiley & Sons Dash RN, Subudhi B, Das S. (2010) A comparison between MLP NN and RBF NN techniques for the detection of stator inter-turn fault of an induction motor. In: 2010 International conference on industrial electronics, control and robotics (IECR), pp 251–256 ErdemHPrediction of moment capacity of reinforced concrete slabs in fire using artificial neural networksAdv Eng Softw201041227027610.1016/j.advengsoft.2009.07.0061180.80060 CaoZChengLZhouCGuNWangXTanMSpiking neural network-based target tracking control for autonomous mobile robotsNeural Comput Appl20152681839184710.1007/s00521-015-1848-5 DA McEntire (2190_CR1) 2014 K Socha (2190_CR41) 2007; 16 A Kia (2190_CR57) 2014; 2014 R Rojas (2190_CR33) 2013 P Hajela (2190_CR13) 1991; 41 NT Nguyen (2190_CR42) 2009; 20 VL Berardi (2190_CR37) 2005; 14 B Chen (2190_CR4) 2010; 25 N Caglar (2190_CR6) 2008; 22 W Graf (2190_CR21) 2010; 25 MH Arslan (2190_CR58) 2015; 9 I Kameli (2190_CR36) 2011; 255 J MacIntyre (2190_CR44) 2013; 23 J-F Chen (2190_CR9) 2015; 8 AM Elazouni (2190_CR18) 1997; 11 DR Baughman (2190_CR32) 2014 A Joghataie (2190_CR28) 2008; 134 DÖ Faruk (2190_CR12) 2010; 23 X Jiang (2190_CR3) 2007; 71 ND Lagaros (2190_CR25) 2012; 44 S Gao (2190_CR51) 2015; 26 SJ Nanda (2190_CR10) 2014; 16 I Guyon (2190_CR39) 2003; 3 N Dey (2190_CR46) 2013; 5 MSA Siddiquee (2190_CR49) 2015; 26 MNS Hadi (2190_CR19) 2003; 81 M Uddi (2190_CR27) 2015; 44 MH Arslan (2190_CR56) 2012; 22 M Bagci (2190_CR23) 2010; 15 CA CoelloCoello (2190_CR35) 2005; 30 AT Azar (2190_CR45) 2013; 195 MH Arslan (2190_CR55) 2010; 32 HS Park (2190_CR17) 1997; 123 N Kausar (2190_CR52) 2015; 96 2190_CR47 MM Fayyadh (2190_CR2) 2011; 6 J Han (2190_CR7) 2005 H Adeli (2190_CR14) 1995; 57 H Maizir (2190_CR26) 2013; 2202 R Gupta (2190_CR20) 2006; 18 M Jakubek (2190_CR24) 2012; 19 B Stratman (2190_CR5) 2011; 18 Z Cao (2190_CR50) 2015; 26 T Zhang (2190_CR38) 2009; 10 2190_CR34 N Karayiannis (2190_CR40) 2013 S Pierce (2190_CR8) 2006; 293 C Ciancio (2190_CR53) 2015 2190_CR30 B Rahmanian (2190_CR11) 2012; 43 V Plevris (2190_CR29) 2011; 26 H Adeli (2190_CR16) 1997; 123 A Mukherjee (2190_CR15) 1995; 9 SZ Mirjalili (2190_CR54) 2015; 26 AJ Maren (2190_CR31) 2014 H Erdem (2190_CR22) 2010; 41 S Dehuri (2190_CR43) 2010; 19 SM Awan (2190_CR48) 2014; 25
References_xml	– reference: CaglarNElmasMYamanZDSaribiyikMNeural networks in 3-dimensional dynamic analysis of reinforced concrete buildingsConstr Build Mater200822578880010.1016/j.conbuildmat.2007.01.029 – reference: StratmanBMahadevanSLiCBiswasGIdentification of critical inspection samples among railroad wheels by similarity-based agglomerative clusteringIntegr Comput Aided Eng2011183203219 – reference: ChenJ-FDoQHHsiehH-NTraining artificial neural networks by a hybrid PSO-CS algorithmAlgorithms20158292308336640910.3390/a8020292 – reference: GuyonIElisseeffAAn introduction to variable and feature selectionJ Mach Learn Res20033115711821102.68556 – reference: AdeliHParkHSA neural dynamics model for structural optimization—theoryComput Struct1995573383390134595210.1016/0045-7949(95)00048-L0900.73502 – reference: ZhangTOn the consistency of feature selection using greedy least squares regressionJ Mach Learn Res20091055556824917491235.62096 – reference: Standard, Indian (2000) ‘IS-456. 2000’ Plain and Reinforced Concrete-Code of Practice. Bureau of Indian Standards Manak Bhavan. 9 Bahadur Shah Zafar Marg New Delhi 110002 – reference: ElazouniAMNosairIAMohieldinYAMohamedAGEstimating resource requirements at conceptual stage using neural networksJ Comput Civ Eng199711421722310.1061/(ASCE)0887-3801(1997)11:4(217) – reference: ArslanMHCeylanMKoyuncuTAn ANN approaches on estimating earthquake performances of existing RC buildingsNeural Netw World201222544310.14311/NNW.2012.22.027 – reference: ArslanMHAn evaluation of effective design parameters on earthquake performance of RC buildings using neural networksEng Struct20103271888189810.1016/j.engstruct.2010.03.010 – reference: NandaSJPandaGA survey on nature inspired metaheuristic algorithms for partitional clusteringSwarm Evolut Comput20141611810.1016/j.swevo.2013.11.003 – reference: GaoSNingBDongHAdaptive neural control with intercepted adaptation for time-delay saturated nonlinear systemsNeural Comput Appl20152681849185710.1007/s00521-015-1855-6 – reference: MukherjeeADespandeJMModeling initial design process using artificial neural networksJ Comput Civ Eng19959319420010.1061/(ASCE)0887-3801(1995)9:3(194) – reference: MarenAJHarstonCTPapRMHandbook of neural computing applications2014San DiegoAcademic Press0778.68010 – reference: Dash RN, Subudhi B, Das S. (2010) A comparison between MLP NN and RBF NN techniques for the detection of stator inter-turn fault of an induction motor. In: 2010 International conference on industrial electronics, control and robotics (IECR), pp 251–256 – reference: RahmanianBPakizehMMansooriSAAEsfandyariMJafariDMaddahHMaskookiAPrediction of MEUF process performance using artificial neural networks and ANFIS approachesJ Taiwan Inst Chem Eng201243455856510.1016/j.jtice.2012.01.002 – reference: CaoZChengLZhouCGuNWangXTanMSpiking neural network-based target tracking control for autonomous mobile robotsNeural Comput Appl20152681839184710.1007/s00521-015-1848-5 – reference: HajelaPBerkeLNeurobiological computational models in structural analysis and designComput Struct199141465766710.1016/0045-7949(91)90178-O0752.73053 – reference: KarayiannisNVenetsanopoulosANArtificial neural networks: learning algorithms, performance evaluation, and applications2013New YorkSpringer Science & Business Media0817.68121 – reference: SiddiqueeMSAHossainMMADevelopment of a sequential artificial neural network for predicting river water levels based on Brahmaputra and Ganges water levelsNeural Comput Appl20152681979199010.1007/s00521-015-1871-6 – reference: RojasRNeural networks: a systematic introduction2013BerlinSpringer Science & Business Media0861.68072 – reference: ArslanMHCeylanMKoyuncuTDetermining earthquake performances of existing reinforced concrete buildings by using ANNWorld Acad Sci Eng Technol Int J Civ Environ Struct Constr Archit Eng201598921925 – reference: UddiMJameelMAbdul RazakHApplication of artificial neural network in fixed offshore structuresIndian J Mar Sci2015443 – reference: HanJKamberMData mining: concepts and techniques20052San FranciscoMorgan and Kaufmann285378 – reference: AwanSMAslamMKhanZASaeedHAn efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecastingNeural Comput Appl2014257–81967197810.1007/s00521-014-1685-y – reference: MirjaliliSZSaremiSMirjaliliSMDesigning evolutionary feedforward neural networks using social spider optimization algorithmNeural Comput Appl20152681919192810.1007/s00521-015-1847-6 – reference: GrafWFreitagSKaliskeMSickertJURecurrent neural networks for uncertain time-dependent structural behaviorComput Aided Civ Infrastruct Eng201025532233310.1111/j.1467-8667.2009.00645.x – reference: DehuriSChoSBA hybrid genetic based functional link artificial neural network with a statistical comparison of classifiers over multiple datasetsNeural Comput Appl201019231732810.1007/s00521-009-0310-y – reference: AzarATEl-SaidSABalasVEOlariuTLinguistic hedges fuzzy feature selection for differential diagnosis of Erythemato-Squamous diseasesSoft Comput Appl AISC201319548750010.1007/978-3-642-33941-7_43 – reference: BaughmanDRLiuYANeural networks in bioprocessing and chemical engineering2014San DiegoAcademic press – reference: DeyNSamantaSYangX-SChaudhriSSDasAOptimization of scaling factors in electrocardiogram signal watermarking using cuckoo searchInt J Bio Inspired Comput (IJBIC)20135531532610.1504/IJBIC.2013.057193 – reference: Chakraborty S, Samanta S, Mukherjee A, Dey N, Chaudhuri SS (2013) Particle swarm optimization based parameter optimization technique in medical information hiding. 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Eng Mech20081341196196910.1061/(ASCE)0733-9399(2008)134:11(961) – reference: FarukDÖA hybrid neural network and ARIMA model for water quality time series predictionEng Appl Artif Intell2010234586594272916010.1016/j.engappai.2009.09.015 – reference: BerardiVLKlineDMRevisiting squared-error and cross-entropy functions for training neural network classifiersNeural Comput Appl200514431031810.1007/s00521-005-0467-y – reference: HadiMNSNeural network applications in concrete structuresComput Struct200381637338110.1016/S0045-7949(02)00451-0 – reference: AdeliHKarimANeural network model for optimization of cold-formed steel beamsJ Struct Eng1997123111535154310.1061/(ASCE)0733-9445(1997)123:11(1535) – reference: LagarosNDPapadrakakisMNeural network based prediction schemes of the non-linear seismic response of 3D buildingsAdv Eng Softw20124419211510.1016/j.advengsoft.2011.05.033 – reference: McEntireDADisaster response and recovery: strategies and tactics for resilience2014HobokenJohn Wiley & 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Snippet	Faulty structural design may cause multistory reinforced concrete (RC) buildings to collapse suddenly. All attempts are directed to avoid structural failure as...
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SubjectTerms	Accuracy Algorithms Artificial Intelligence Artificial neural networks Classifiers Collapse Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Concrete construction Data Mining and Knowledge Discovery Feature extraction Hazards Image Processing and Computer Vision Learning theory Machine learning Multistory buildings Neural networks Original Article Particle swarm optimization Predictions Probability and Statistics in Computer Science Reinforced concrete Root-mean-square errors Structural design Structural failure
Title	Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings
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