Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms
•MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting faulting.•Pavement age, cumulative average precipitation, and elasticity modulus of concrete slab are the most important variables. This study aims...
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| Veröffentlicht in: | Construction & building materials Jg. 364; S. 129948 |
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| Sprache: | Englisch |
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18.01.2023
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| Abstract | •MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting faulting.•Pavement age, cumulative average precipitation, and elasticity modulus of concrete slab are the most important variables.
This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural networks (ANN) and four multi-objective metaheuristic optimization algorithms, namely, the Pareto envelope-based selection algorithm II (PESA-2), the strength Pareto evolutionary algorithm 2 (SPEA-2), multi-objective particle swarm optimization (MPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D showed better performance compared to the other models, which identified 17 input variables affecting faulting failure. In the next step, the classic back-propagation (BP), Biogeography-based optimization (BBO), invasive weed optimization (IWO), and simulated annealing algorithm (SAA) were combined with the ANN to develop three prediction models for faulting failure. Modeling with metaheuristic optimization algorithms showed better performance than the ordinary ANN. The pavement age, cumulative average precipitation, and elasticity modulus of the concrete slab have the most significant impact on the formation and increase of faulting. |
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| AbstractList | •MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting faulting.•Pavement age, cumulative average precipitation, and elasticity modulus of concrete slab are the most important variables.
This study aims to predict faulting failure of jointed plain concrete pavement (JPCP) using different variables. For this purpose, four feature selection methods were developed by combining the artificial neural networks (ANN) and four multi-objective metaheuristic optimization algorithms, namely, the Pareto envelope-based selection algorithm II (PESA-2), the strength Pareto evolutionary algorithm 2 (SPEA-2), multi-objective particle swarm optimization (MPSO), and multi-objective evolutionary algorithm based on decomposition (MOEA/D). The MOEA/D showed better performance compared to the other models, which identified 17 input variables affecting faulting failure. In the next step, the classic back-propagation (BP), Biogeography-based optimization (BBO), invasive weed optimization (IWO), and simulated annealing algorithm (SAA) were combined with the ANN to develop three prediction models for faulting failure. Modeling with metaheuristic optimization algorithms showed better performance than the ordinary ANN. The pavement age, cumulative average precipitation, and elasticity modulus of the concrete slab have the most significant impact on the formation and increase of faulting. |
| ArticleNumber | 129948 |
| Author | Moghadas Nejad, Fereidoon Hamidian, Pouria Hajikarimi, Pouria Ehsani, Mehrdad |
| Author_xml | – sequence: 1 givenname: Mehrdad orcidid: 0000-0003-3413-216X surname: Ehsani fullname: Ehsani, Mehrdad email: mhrehsani@aut.ac.ir organization: Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran – sequence: 2 givenname: Pouria orcidid: 0000-0003-2469-9998 surname: Hamidian fullname: Hamidian, Pouria email: pouria.hamidian@ut.ac.ir organization: Department of Civil Engineering, University of Tehran, Tehran, Iran – sequence: 3 givenname: Pouria orcidid: 0000-0001-5621-7274 surname: Hajikarimi fullname: Hajikarimi, Pouria email: phajikarimi@aut.ac.ir organization: Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran – sequence: 4 givenname: Fereidoon surname: Moghadas Nejad fullname: Moghadas Nejad, Fereidoon email: moghadas@aut.ac.ir organization: Department of Civil & Environmental Engineering, Amirkabir University of Technology (Tehran Polytechnic), Iran |
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| Cites_doi | 10.3846/13923730.2015.1120770 10.1177/0361198118756881 10.1016/j.ijepes.2015.07.041 10.1109/TEVC.2008.919004 10.1016/j.enconman.2022.115703 10.1109/TEVC.2007.892759 10.1016/0895-7177(93)90204-C 10.1016/j.conbuildmat.2021.125332 10.1007/978-1-4757-2287-1 10.1016/j.compstruc.2011.08.019 10.1126/science.220.4598.671 10.1016/j.tust.2017.07.017 10.1007/s00170-020-05641-y 10.1016/j.jclepro.2021.127053 10.1061/(ASCE)TE.1943-5436.0000446 10.1016/j.jobe.2022.105293 10.1016/j.neucom.2014.01.078 10.1016/j.eswa.2009.05.056 10.1177/0361198119838988 10.1016/j.jestch.2019.06.011 10.1007/BF00940812 10.1063/1.1699114 |
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| Keywords | Feature Selection Artificial Neural Networks (ANN) Faulting Failure Multi-objective Metaheuristic Optimization Algorithms Jointed Plain Concrete Pavement |
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| References | 2008: AASHTO. Yepes (b0045) 2016; 22 Adak (b0135) 2020; 23 Simon (b0140) 2008; 12 1994. 1998. Askari, A., et al. Shahin, M.Y. Wu, C., et al. Chen, Lytton (b0020) 2019; 2673 Owusu-Antwi (b0060) 1997 2001. 2005. Selezneva, O., J. Jiang, and S.D. Tayabji Ehsani, Moghadas Nejad, Hajikarimi (b0075) 2022 Saha (b0125) 2018; 2672 in Fattahi, Bazdar (b0110) 2017; 70 Alavi, Gandomi (b0160) 2011; 89 Officials, T. . Amuso, Enslin (b0085) 2007 Wang, Tsai (b0070) 2013; 6 Elkins (b0105) 2003 Alatoom, Al-Suleiman (b0215) 2022 Zhang, Li (b0090) 2007; 11 Sibtain (b0190) 2022; 263 Ehsani (b0185) 2021; 53 Kirkpatrick, S. Černý (b0165) 1985; 45 Metropolis (b0175) 1953; 21 Corne, D.W., et al. Alidoust (b0210) 2021; 303 Byrum, C.R. and R.W. Perera. Guyon, Elisseeff (b0195) 2003; 3 Titus-Glover, L., et al. Hamidian (b0130) 2022; 61 (4598): p. 671-680. Lajimi, Amraee (b0145) 2016; 76 1993. Sindi, Agbelie (b0040) 2020; 146 Mrzygłód (b0220) 2020; 109 2000. Danesh (b0150) 2022 Huang, Y.H. Saghafi (b0025) 2009; 2 Zhou (b0155) 2015; 151 Optimization by simulated annealing. Science, 1983. Ghafari, Ehsani, Nejad (b0120) 2022; 314 Naseri (b0050) 2021 Schalkoff (b0115) 1997 Rabbani, Bajestani, Khoshkhou (b0095) 2010; 37 Ehsani, M., F.M. Nejad, and P. Hajikarimi Lu, Tolliver (b0100) 2012; 138 Ingber (b0180) 1993; 18 Ingber (10.1016/j.conbuildmat.2022.129948_b0180) 1993; 18 Yepes (10.1016/j.conbuildmat.2022.129948_b0045) 2016; 22 Zhou (10.1016/j.conbuildmat.2022.129948_b0155) 2015; 151 Owusu-Antwi (10.1016/j.conbuildmat.2022.129948_b0060) 1997 10.1016/j.conbuildmat.2022.129948_b0035 Lu (10.1016/j.conbuildmat.2022.129948_b0100) 2012; 138 10.1016/j.conbuildmat.2022.129948_b0015 Elkins (10.1016/j.conbuildmat.2022.129948_b0105) 2003 10.1016/j.conbuildmat.2022.129948_b0065 Alatoom (10.1016/j.conbuildmat.2022.129948_b0215) 2022 Saghafi (10.1016/j.conbuildmat.2022.129948_b0025) 2009; 2 Ehsani (10.1016/j.conbuildmat.2022.129948_b0075) 2022 Lajimi (10.1016/j.conbuildmat.2022.129948_b0145) 2016; 76 Mrzygłód (10.1016/j.conbuildmat.2022.129948_b0220) 2020; 109 Adak (10.1016/j.conbuildmat.2022.129948_b0135) 2020; 23 10.1016/j.conbuildmat.2022.129948_b0080 Alavi (10.1016/j.conbuildmat.2022.129948_b0160) 2011; 89 Simon (10.1016/j.conbuildmat.2022.129948_b0140) 2008; 12 Zhang (10.1016/j.conbuildmat.2022.129948_b0090) 2007; 11 Sindi (10.1016/j.conbuildmat.2022.129948_b0040) 2020; 146 Wang (10.1016/j.conbuildmat.2022.129948_b0070) 2013; 6 Hamidian (10.1016/j.conbuildmat.2022.129948_b0130) 2022; 61 Danesh (10.1016/j.conbuildmat.2022.129948_b0150) 2022 Guyon (10.1016/j.conbuildmat.2022.129948_b0195) 2003; 3 Fattahi (10.1016/j.conbuildmat.2022.129948_b0110) 2017; 70 Schalkoff (10.1016/j.conbuildmat.2022.129948_b0115) 1997 Ghafari (10.1016/j.conbuildmat.2022.129948_b0120) 2022; 314 10.1016/j.conbuildmat.2022.129948_b0205 Amuso (10.1016/j.conbuildmat.2022.129948_b0085) 2007 10.1016/j.conbuildmat.2022.129948_b0200 Alidoust (10.1016/j.conbuildmat.2022.129948_b0210) 2021; 303 10.1016/j.conbuildmat.2022.129948_b0005 Saha (10.1016/j.conbuildmat.2022.129948_b0125) 2018; 2672 10.1016/j.conbuildmat.2022.129948_b0010 Ehsani (10.1016/j.conbuildmat.2022.129948_b0185) 2021; 53 Sibtain (10.1016/j.conbuildmat.2022.129948_b0190) 2022; 263 10.1016/j.conbuildmat.2022.129948_b0055 Chen (10.1016/j.conbuildmat.2022.129948_b0020) 2019; 2673 10.1016/j.conbuildmat.2022.129948_b0170 Metropolis (10.1016/j.conbuildmat.2022.129948_b0175) 1953; 21 10.1016/j.conbuildmat.2022.129948_b0030 Černý (10.1016/j.conbuildmat.2022.129948_b0165) 1985; 45 Naseri (10.1016/j.conbuildmat.2022.129948_b0050) 2021 Rabbani (10.1016/j.conbuildmat.2022.129948_b0095) 2010; 37 |
| References_xml | – reference: Huang, Y.H., – reference: . in – volume: 151 start-page: 1227 year: 2015 end-page: 1236 ident: b0155 article-title: A discrete invasive weed optimization algorithm for solving traveling salesman problem publication-title: Neurocomputing – reference: Corne, D.W., et al. – reference: Officials, T., – volume: 109 start-page: 1385 year: 2020 end-page: 1395 ident: b0220 article-title: Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel publication-title: Int. J. Adv. Manuf. Technol. – volume: 263 year: 2022 ident: b0190 article-title: A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network publication-title: Energ. Conver. Manage. – volume: 303 year: 2021 ident: b0210 article-title: Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques publication-title: J. Clean. Prod. – volume: 138 start-page: 1297 year: 2012 end-page: 1302 ident: b0100 article-title: Pavement treatment short-term effectiveness in IRI change using long-term pavement program data publication-title: J. Transp. Eng. – volume: 45 start-page: 41 year: 1985 end-page: 51 ident: b0165 article-title: Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm publication-title: J. Optim. Theory Appl. – start-page: 1 year: 2021 end-page: 18 ident: b0050 article-title: Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm publication-title: Int. J. Pavement Eng. – reference: . 1993. – volume: 2 start-page: 20 year: 2009 end-page: 25 ident: b0025 article-title: Artificial neural networks and regression analysis for predicting faulting in jointed concrete pavements considering base condition publication-title: Int. J. Pavement Res. Technol. – start-page: 1 year: 2022 end-page: 16 ident: b0075 article-title: Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods publication-title: Int. J. Pavement Eng. – volume: 3 start-page: 1157 year: 2003 end-page: 1182 ident: b0195 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – year: 2007 ident: b0085 article-title: The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi-mission waveform design publication-title: In – volume: 2673 start-page: 407 year: 2019 end-page: 417 ident: b0020 article-title: Development of a new faulting model in jointed concrete pavement using LTPP data publication-title: Transp. Res. Rec. – volume: 89 start-page: 2176 year: 2011 end-page: 2194 ident: b0160 article-title: Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing publication-title: Comput. Struct. – volume: 22 start-page: 540 year: 2016 end-page: 550 ident: b0045 article-title: Optimal pavement maintenance programs based on a hybrid greedy randomized adaptive search procedure algorithm publication-title: J. Civ. Eng. Manag. – volume: 53 start-page: 1 year: 2021 ident: b0185 article-title: Compressive strength prediction of ordinary concrete, fly ash concrete, and slag concrete by novel techniques and presenting their optimal mixtures publication-title: Amirkabir Journal of Civil Engineering – reference: Titus-Glover, L., et al., – start-page: 1 year: 2022 end-page: 16 ident: b0215 article-title: Development of pavement roughness models using Artificial Neural Network (ANN) publication-title: Int. J. Pavement Eng. – start-page: 1 year: 2022 end-page: 14 ident: b0150 article-title: Prediction model of crash severity in imbalanced dataset using data leveling methods and metaheuristic optimization algorithms publication-title: Int. J. Crashworthiness – reference: Askari, A., et al., – reference: . 2005. – reference: Kirkpatrick, S., – volume: 146 start-page: 04020008 year: 2020 ident: b0040 article-title: Assignments of pavement treatment options: genetic algorithms versus mixed-integer programming publication-title: Journal of Transportation Engineering, Part B: Pavements – reference: 1993. – reference: . 2008: AASHTO. – volume: 61 year: 2022 ident: b0130 article-title: Introduction of a novel evolutionary neural network for evaluating the compressive strength of concretes: A case of Rice Husk Ash concrete publication-title: Journal of Building Engineering – reference: . 1998. – volume: 6 start-page: 651 year: 2013 ident: b0070 article-title: Back-propagation network modeling for concrete pavement faulting using LTPP data publication-title: Int. J. Pavement Res. Technol. – reference: Ehsani, M., F.M. Nejad, and P. Hajikarimi, – reference: Byrum, C.R. and R.W. Perera. – year: 2003 ident: b0105 article-title: Long-term pavement performance information management system: Pavement performance database user reference guide – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: b0090 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – reference: Selezneva, O., J. Jiang, and S.D. Tayabji, – reference: . 2001. – volume: 2672 start-page: 23 year: 2018 end-page: 33 ident: b0125 article-title: Use of an artificial neural network approach for the prediction of resilient modulus for unbound granular material publication-title: Transp. Res. Rec. – reference: (4598): p. 671-680. – volume: 70 start-page: 114 year: 2017 end-page: 124 ident: b0110 article-title: Applying improved artificial neural network models to evaluate drilling rate index publication-title: Tunn. Undergr. Space Technol. – reference: Wu, C., et al. – volume: 23 start-page: 463 year: 2020 end-page: 469 ident: b0135 article-title: Classification of alcohols obtained by QCM sensors with different characteristics using ABC based neural network publication-title: Engineering Science and Technology, an International Journal – volume: 12 start-page: 702 year: 2008 end-page: 713 ident: b0140 article-title: Biogeography-based optimization publication-title: IEEE Trans. Evol. Comput. – volume: 21 start-page: 1087 year: 1953 end-page: 1092 ident: b0175 article-title: Equation of state calculations by fast computing machines publication-title: J. Chem. Phys. – year: 1997 ident: b0115 article-title: Artificial neural networks – volume: 314 year: 2022 ident: b0120 article-title: Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach publication-title: Constr. Build. Mater. – volume: 37 start-page: 315 year: 2010 end-page: 321 ident: b0095 article-title: A multi-objective particle swarm optimization for project selection problem publication-title: Expert Syst. Appl. – reference: . – reference: Optimization by simulated annealing. Science, 1983. – reference: . 2000. – year: 1997 ident: b0060 article-title: Development and calibration of mechanistic-empirical distress models for cost allocation – reference: . 1994. – reference: Shahin, M.Y., – volume: 18 start-page: 29 year: 1993 end-page: 57 ident: b0180 article-title: Simulated annealing: Practice versus theory publication-title: Math. Comput. Model. – volume: 76 start-page: 82 year: 2016 end-page: 89 ident: b0145 article-title: A two stage model for rotor angle transient stability constrained optimal power flow publication-title: Int. J. Electr. Power Energy Syst. – start-page: 1 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0075 article-title: Developing an optimized faulting prediction model in Jointed Plain Concrete Pavement using artificial neural networks and random forest methods publication-title: Int. J. Pavement Eng. – volume: 22 start-page: 540 issue: 4 year: 2016 ident: 10.1016/j.conbuildmat.2022.129948_b0045 article-title: Optimal pavement maintenance programs based on a hybrid greedy randomized adaptive search procedure algorithm publication-title: J. Civ. Eng. Manag. doi: 10.3846/13923730.2015.1120770 – ident: 10.1016/j.conbuildmat.2022.129948_b0005 – ident: 10.1016/j.conbuildmat.2022.129948_b0030 – volume: 2672 start-page: 23 issue: 52 year: 2018 ident: 10.1016/j.conbuildmat.2022.129948_b0125 article-title: Use of an artificial neural network approach for the prediction of resilient modulus for unbound granular material publication-title: Transp. Res. Rec. doi: 10.1177/0361198118756881 – volume: 76 start-page: 82 year: 2016 ident: 10.1016/j.conbuildmat.2022.129948_b0145 article-title: A two stage model for rotor angle transient stability constrained optimal power flow publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2015.07.041 – volume: 12 start-page: 702 issue: 6 year: 2008 ident: 10.1016/j.conbuildmat.2022.129948_b0140 article-title: Biogeography-based optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2008.919004 – volume: 263 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0190 article-title: A multivariate ultra-short-term wind speed forecasting model by employing multistage signal decomposition approaches and a deep learning network publication-title: Energ. Conver. Manage. doi: 10.1016/j.enconman.2022.115703 – ident: 10.1016/j.conbuildmat.2022.129948_b0055 – ident: 10.1016/j.conbuildmat.2022.129948_b0200 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.conbuildmat.2022.129948_b0090 article-title: MOEA/D: A multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 18 start-page: 29 issue: 11 year: 1993 ident: 10.1016/j.conbuildmat.2022.129948_b0180 article-title: Simulated annealing: Practice versus theory publication-title: Math. Comput. Model. doi: 10.1016/0895-7177(93)90204-C – volume: 314 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0120 article-title: Prediction of low-temperature fracture resistance curves of unmodified and crumb rubber modified hot mix asphalt mixtures using a machine learning approach publication-title: Constr. Build. Mater. doi: 10.1016/j.conbuildmat.2021.125332 – ident: 10.1016/j.conbuildmat.2022.129948_b0015 doi: 10.1007/978-1-4757-2287-1 – volume: 89 start-page: 2176 issue: 23–24 year: 2011 ident: 10.1016/j.conbuildmat.2022.129948_b0160 article-title: Prediction of principal ground-motion parameters using a hybrid method coupling artificial neural networks and simulated annealing publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2011.08.019 – start-page: 1 year: 2021 ident: 10.1016/j.conbuildmat.2022.129948_b0050 article-title: Sustainable pavement maintenance and rehabilitation planning using differential evolutionary programming and coyote optimisation algorithm publication-title: Int. J. Pavement Eng. – ident: 10.1016/j.conbuildmat.2022.129948_b0170 doi: 10.1126/science.220.4598.671 – volume: 70 start-page: 114 year: 2017 ident: 10.1016/j.conbuildmat.2022.129948_b0110 article-title: Applying improved artificial neural network models to evaluate drilling rate index publication-title: Tunn. Undergr. Space Technol. doi: 10.1016/j.tust.2017.07.017 – volume: 6 start-page: 651 issue: 5 year: 2013 ident: 10.1016/j.conbuildmat.2022.129948_b0070 article-title: Back-propagation network modeling for concrete pavement faulting using LTPP data publication-title: Int. J. Pavement Res. Technol. – volume: 109 start-page: 1385 issue: 5 year: 2020 ident: 10.1016/j.conbuildmat.2022.129948_b0220 article-title: Sensitivity analysis of the artificial neural networks in a system for durability prediction of forging tools to forgings made of C45 steel publication-title: Int. J. Adv. Manuf. Technol. doi: 10.1007/s00170-020-05641-y – volume: 303 year: 2021 ident: 10.1016/j.conbuildmat.2022.129948_b0210 article-title: Prediction of the shear modulus of municipal solid waste (MSW): An application of machine learning techniques publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2021.127053 – start-page: 1 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0150 article-title: Prediction model of crash severity in imbalanced dataset using data leveling methods and metaheuristic optimization algorithms publication-title: Int. J. Crashworthiness – ident: 10.1016/j.conbuildmat.2022.129948_b0010 – volume: 138 start-page: 1297 issue: 11 year: 2012 ident: 10.1016/j.conbuildmat.2022.129948_b0100 article-title: Pavement treatment short-term effectiveness in IRI change using long-term pavement program data publication-title: J. Transp. Eng. doi: 10.1061/(ASCE)TE.1943-5436.0000446 – volume: 61 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0130 article-title: Introduction of a novel evolutionary neural network for evaluating the compressive strength of concretes: A case of Rice Husk Ash concrete publication-title: Journal of Building Engineering doi: 10.1016/j.jobe.2022.105293 – volume: 151 start-page: 1227 year: 2015 ident: 10.1016/j.conbuildmat.2022.129948_b0155 article-title: A discrete invasive weed optimization algorithm for solving traveling salesman problem publication-title: Neurocomputing doi: 10.1016/j.neucom.2014.01.078 – volume: 37 start-page: 315 issue: 1 year: 2010 ident: 10.1016/j.conbuildmat.2022.129948_b0095 article-title: A multi-objective particle swarm optimization for project selection problem publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2009.05.056 – ident: 10.1016/j.conbuildmat.2022.129948_b0035 – year: 2003 ident: 10.1016/j.conbuildmat.2022.129948_b0105 – volume: 146 start-page: 04020008 issue: 2 year: 2020 ident: 10.1016/j.conbuildmat.2022.129948_b0040 article-title: Assignments of pavement treatment options: genetic algorithms versus mixed-integer programming publication-title: Journal of Transportation Engineering, Part B: Pavements – year: 1997 ident: 10.1016/j.conbuildmat.2022.129948_b0115 – ident: 10.1016/j.conbuildmat.2022.129948_b0205 – volume: 2673 start-page: 407 issue: 5 year: 2019 ident: 10.1016/j.conbuildmat.2022.129948_b0020 article-title: Development of a new faulting model in jointed concrete pavement using LTPP data publication-title: Transp. Res. Rec. doi: 10.1177/0361198119838988 – ident: 10.1016/j.conbuildmat.2022.129948_b0065 – year: 1997 ident: 10.1016/j.conbuildmat.2022.129948_b0060 – volume: 23 start-page: 463 issue: 3 year: 2020 ident: 10.1016/j.conbuildmat.2022.129948_b0135 article-title: Classification of alcohols obtained by QCM sensors with different characteristics using ABC based neural network publication-title: Engineering Science and Technology, an International Journal doi: 10.1016/j.jestch.2019.06.011 – volume: 45 start-page: 41 issue: 1 year: 1985 ident: 10.1016/j.conbuildmat.2022.129948_b0165 article-title: Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm publication-title: J. Optim. Theory Appl. doi: 10.1007/BF00940812 – volume: 53 start-page: 1 issue: 10 year: 2021 ident: 10.1016/j.conbuildmat.2022.129948_b0185 article-title: Compressive strength prediction of ordinary concrete, fly ash concrete, and slag concrete by novel techniques and presenting their optimal mixtures publication-title: Amirkabir Journal of Civil Engineering – volume: 3 start-page: 1157 issue: Mar year: 2003 ident: 10.1016/j.conbuildmat.2022.129948_b0195 article-title: An introduction to variable and feature selection publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 20 issue: 1 year: 2009 ident: 10.1016/j.conbuildmat.2022.129948_b0025 article-title: Artificial neural networks and regression analysis for predicting faulting in jointed concrete pavements considering base condition publication-title: Int. J. Pavement Res. Technol. – start-page: 1 year: 2022 ident: 10.1016/j.conbuildmat.2022.129948_b0215 article-title: Development of pavement roughness models using Artificial Neural Network (ANN) publication-title: Int. J. Pavement Eng. – ident: 10.1016/j.conbuildmat.2022.129948_b0080 – year: 2007 ident: 10.1016/j.conbuildmat.2022.129948_b0085 article-title: The Strength Pareto Evolutionary Algorithm 2 (SPEA2) applied to simultaneous multi-mission waveform design – volume: 21 start-page: 1087 issue: 6 year: 1953 ident: 10.1016/j.conbuildmat.2022.129948_b0175 article-title: Equation of state calculations by fast computing machines publication-title: J. Chem. Phys. doi: 10.1063/1.1699114 |
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| Snippet | •MOEA/D method has the best performance to select 17 features affecting faulting.•ANN- SAA with R2 value of 0.976 has been the best model for predicting... |
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| SubjectTerms | Artificial Neural Networks (ANN) Faulting Failure Feature Selection Jointed Plain Concrete Pavement Multi-objective Metaheuristic Optimization Algorithms |
| Title | Optimized prediction models for faulting failure of Jointed Plain concrete pavement using the metaheuristic optimization algorithms |
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