A novel approach for resilient modulus prediction using extreme learning machine-equilibrium optimiser techniques

References

• AASHTO, 1993. AASHTO guide for design of pavement structures. In: Sustainable waste management and recycling: construction demolition waste: proceedings of the international conference organised by the concrete and masonry research group.
   Google Scholar
• AASHTO, 2003. AASHTO: T307-99 standard method of test for determining the resilient modulus of soils and aggregate materials. Washington: American Association of State Highway and Transportation Officials.
   Google Scholar
• AASHTO, 2008. Mechanistic empirical pavement design guide: a manual practice. Washington: American Association of State Highway and Transportation Officials.
   Google Scholar
• AASHTO, 2017. Standard method of test for resilient modulus of subgrade soils and untreated base/subbase materials. Test Procedure T307, AASHTO, Washington, D.C. doi:10.1155/2014/372838.
   Google Scholar
• Abambres, Miguel, and Ferreira, Adelino, 2017. Application of ANN in pavement engineering: state-of-art. SSRN Electronic Journal, hal-02066889v2f. doi:10.2139/ssrn.3351973.
   Google Scholar
• Abdullah, Shafika Sultan, et al., 2015. Extreme learning machines: a new approach for prediction of reference evapotranspiration. Journal of Hydrology, 527 (August), 184–195. doi:10.1016/j.jhydrol.2015.04.073.
   Google Scholar
• Adeyemo, Josiah, and Stretch, Derek, 2018. Review of hybrid evolutionary algorithms for optimizing a reservoir. South African Journal of Chemical Engineering, 25 (June), 22–31. doi:10.1016/j.sajce.2017.11.004.
   Google Scholar
• Alharbi, Fawaz, and Smadi, Omar, 2019. Predicting pavement performance utilizing artificial neural network (ANN). International Journal of Advanced Engineering, Management and Science, 5 (8), 504–508. doi:10.22161/ijaems.58.4.
   Google Scholar
• Andrei, Dragos, Matthew W. Witczak, and William N. Houston. 2009. Resilient modulus predictive model for unbound pavement materials. In: Contemporary topics in ground modification, problem soils, and geo-support. Reston, VA: American Society of Civil Engineers, 401–408. doi:10.1061/41023(337)51.
   Google Scholar
• Arisha, M., et al., 2018. Performance evaluation of construction and demolition waste materials for pavement construction in Egypt. Journal of Materials in Civil Engineering, 30 (2), 04017270. doi:10.1061/(ASCE)MT.1943-5533.0002127.
   Web of Science ®Google Scholar
• Chan, Arthur, Keoleian, Gregory, and Gabler, Eric, 2008. Evaluation of life-cycle cost analysis practices used by the Michigan department of transportation. Journal of Transportation Engineering, 134 (6), 236–245. doi:10.1061/(ASCE)0733-947X(2008)134:6(236).
   Web of Science ®Google Scholar
• Du, Dawei, Simon, Dan, and Ergezer, Mehmet, 2009. Biogeography-based optimization combined with evolutionary strategy and immigration refusal. In: 2009 IEEE International Conference on Systems, Man and Cybernetics, 997–1002. IEEE. doi:10.1109/ICSMC.2009.5346055.
   Google Scholar
• ECP, 2008. Egyptian code of practice for urban and rural roads, edition 1: road materials and their tests (part four). The Ministry of Housing, Utilities and Urban Communities. doi:10.4236/oalib.1100711.
   Google Scholar
• El-Ashwah, Ahmed S., et al., 2019. A new approach for developing resilient modulus master surface to characterize granular pavement materials and subgrade soils. Construction and Building Materials, 194 (January), 372–385. doi:10.1016/j.conbuildmat.2018.10.212.
   Google Scholar
• El-Ashwah, Ahmed S., et al., 2020. Advanced characterization of unbound granular materials for pavement structural design in Egypt. International Journal of Pavement Engineering, 1–13. doi:10.1080/10298436.2020.1754416.
   Web of Science ®Google Scholar
• Fan, Junsong, et al., 2014. Improved biogeography-based optimization approach to secondary protein prediction. In: 2014 International joint conference on neural networks (IJCNN), 4223–4228. IEEE. doi:10.1109/IJCNN.2014.6889417.
   Google Scholar
• Faramarzi, Afshin, et al., 2020. Equilibrium optimizer: a novel optimization algorithm. Knowledge-Based Systems, 191 (March), 105190. doi:10.1016/j.knosys.2019.105190.
   Google Scholar
• Gabr, A. R., and Cameron, D. A., 2012. Properties of recycled concrete aggregate for unbound pavement construction. Journal of Materials in Civil Engineering, 24 (6), 754–764. doi:10.1061/(ASCE)MT.1943-5533.0000447.
   Web of Science ®Google Scholar
• Gabr, A. R., Mills, K. G., and Cameron, D. A., 2013. Repeated load triaxial testing of recycled concrete aggregate for pavement base construction. Geotechnical and Geological Engineering, 31 (1), 119–132. doi:10.1007/s10706-012-9572-8.
   Google Scholar
• Gopalakrishnan, Kasthurirangan, 2010. Neural network–Swarm intelligence hybrid nonlinear optimization algorithm for pavement moduli back-calculation. Journal of Transportation Engineering, 136 (6), 528–536. doi:10.1061/(ASCE)TE.1943-5436.0000128.
   Web of Science ®Google Scholar
• Grosan, Crina, and Abraham, Ajith, 2007. Hybrid evolutionary algorithms: methodologies, architectures, and reviews. Studies in Computational Intelligence, 1–17. doi:10.1007/978-3-540-73297-6_1.
   Google Scholar
• Hicks, RG, and Monismith, CL, 1971. Factors influencing the resilient response of granular materials. Highw Res Rec.
   Google Scholar
• Hintze, Jerry L, and Nelson, Ray D, 1998. Violin plots: a box plot-density trace synergism statistical computing and graphics violin plots: a box plot-density trace synergism. Source: The American Statistician.
   Google Scholar
• Huang, Gao, et al., 2015. Trends in extreme learning machines: a review. Neural Networks, 61 (January), 32–48. doi:10.1016/j.neunet.2014.10.001.
   PubMedGoogle Scholar
• Huang, Guang-Bin, 2014. An insight into extreme learning machines: random neurons, random features and kernels. Cognitive Computation, 6 (3), 376–390. doi:10.1007/s12559-014-9255-2.
   Web of Science ®Google Scholar
• Inkoom, Sylvester, et al., 2019. Prediction of the crack condition of highway pavements using machine learning models. Structure and Infrastructure Engineering, 15 (7), 940–953. doi:10.1080/15732479.2019.1581230.
   Web of Science ®Google Scholar
• Jaafari, Abolfazl, et al., 2019. Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility. Catena, 175, 430–445. doi:10.1016/j.catena.2018.12.033.
   Web of Science ®Google Scholar
• Jafar-Zanjani, Samad, Inampudi, Sandeep, and Mosallaei, Hossein, 2018. Adaptive genetic algorithm for optical metasurfaces design. Scientific Reports, 8 (1), 11040. doi:10.1038/s41598-018-29275-z.
   PubMedGoogle Scholar
• Jahed, Danial, et al., 2020. A novel approach for forecasting of ground vibrations resulting from blasting: modified particle swarm optimization coupled extreme learning machine. Engineering with Computers. doi:10.1007/s00366-020-00997-x.
   Web of Science ®Google Scholar
• Kaloop, Mosbeh R., et al., 2019a. Predicting resilient modulus of recycled concrete and clay masonry blends for pavement applications using soft computing techniques. Frontiers of Structural and Civil Engineering, 13 (6), 1379–1392. doi:10.1007/s11709-019-0562-2.
   Web of Science ®Google Scholar
• Kaloop, Mosbeh R., et al., 2019b. Hybrid wavelet and principal component analyses approach for extracting dynamic motion characteristics from displacement series derived from multipath-affected high-rate GNSS observations. Remote Sensing, 12 (1), 79. doi:10.3390/rs12010079.
   Web of Science ®Google Scholar
• Kaloop, Mosbeh R., et al., 2020. A hybrid wavelet-optimally-pruned extreme learning machine model for the estimation of international roughness index of rigid pavements. International Journal of Pavement Engineering, 1–15. doi:10.1080/10298436.2020.1776281.
   Web of Science ®Google Scholar
• Kaloop, Mosbeh R., et al., 2019c. Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases. Applied Sciences, 9 (16), 3221. doi:10.3390/app9163221.
   Google Scholar
• Kim, Sung-Hee, Yang, Jidong, and Jeong, Jin-Hoon, 2014. Prediction of subgrade resilient modulus using artificial neural network. KSCE Journal of Civil Engineering, 18 (5), 1372–1379. doi:10.1007/s12205-014-0316-6.
   Web of Science ®Google Scholar
• Kvasov, Dmitri E., and Sergeyev, Yaroslav D., 2015. Deterministic approaches for solving practical black-box global optimization problems. Advances in Engineering Software, 80 (February), 58–66. doi:10.1016/j.advengsoft.2014.09.014.
   Google Scholar
• Lekarp, Fredrick, Isacsson, Ulf, and Dawson, Andrew, 2000. State of the art. I: resilient response of unbound aggregates. Journal of Transportation Engineering, 126 (1), 66–75. doi:10.1061/(ASCE)0733-947X(2000)126:1(66).
   Web of Science ®Google Scholar
• Li, S. T., et al., 2019. Back-analysis of pavement thickness based on PSO-GA hybrid algorithms. IOP Conference Series: Earth and Environmental Science, 252 (July), 052066. doi:10.1088/1755-1315/252/5/052066.
   Google Scholar
• Li, Yaohui, et al., 2017. A kriging-based constrained global optimization algorithm for expensive black-box functions with infeasible initial points. Journal of Global Optimization, 67 (1–2), 343–366. doi:10.1007/s10898-016-0455-z.
   Web of Science ®Google Scholar
• Liu, Hai, et al., 2015. Extreme learning machine based on improved genetic algorithm. In: Proceedings of the 5th international conference on information engineering for mechanics and materials. Paris: Atlantis Press. doi:10.2991/icimm-15.2015.38.
   Google Scholar
• Mousa, Rabah, et al., 2017. Resilient modulus for unbound granular materials and subgrade soils in Egypt. In: A. Shanableh, M. Maalej, S. Barakat, M. Omar, S. Al-Toubat, R. Al-Ruzouq, and K. Hamad. MATEC web of conferences 120 (August): 06009. doi:10.1051/matecconf/201712006009.
   Google Scholar
• Mousa, Eman, El-Badawy, Sherif, and Azam, Abdelhalim, 2020. Evaluation of reclaimed asphalt pavement as base/subbase material in Egypt. Transportation Geotechnics, 100414. doi:10.1016/j.trgeo.2020.100414.
   Web of Science ®Google Scholar
• Murlidhar, Bhatawdekar Ramesh, et al., 2020. A novel intelligent ELM-BBO technique for predicting distance of mine blasting-induced flyrock. Natural Resources Research. doi:10.1007/s11053-020-09676-6.
   Web of Science ®Google Scholar
• Nachar, Nadim, 2008. The Mann-Whitney U: A test for assessing whether two independent samples come from the same distribution. Tutorials in Quantitative Methods for Psychology, 4 (1), 13–20. doi:10.20982/tqmp.04.1.p013.
   Google Scholar
• Pal, Mahesh, and Deswal, Surinder, 2014. Extreme learning machine based modeling of resilient modulus of subgrade soils. Geotechnical and Geological Engineering, 32 (2), 287–296. doi:10.1007/s10706-013-9710-y.
   Google Scholar
• Pourtahmasb, Mohammad Saeed, Karim, Mohamed Rehan, and Shamshirband, Shahaboddin, 2015. Resilient modulus prediction of asphalt mixtures containing recycled concrete aggregate using an adaptive neuro-fuzzy methodology. Construction and Building Materials, 82 (May), 257–263. doi:10.1016/j.conbuildmat.2015.02.030.
   Google Scholar
• Purnomo, D. M. J., et al., 2015. Genetic algorithm optimization for extreme learning machine based microalgal growth forecasting of Chlamydomonas Sp. In: 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), 243–248. IEEE. doi:10.1109/ICACSIS.2015.7415189.
   Google Scholar
• Reza, A., and Rahrovan, M., 2016. Application of artifitial neural network to predict the resilient modulus of stabilized base subjected to wet dry cycles. Computations and Materials in Civil Engineering, 1 (1), 37–47.
   Google Scholar
• Santos, João, Ferreira, Adelino, and Flintsch, Gerardo, 2019. An adaptive hybrid genetic algorithm for pavement management. International Journal of Pavement Engineering, 20 (3), 266–286. doi:10.1080/10298436.2017.1293260.
   Web of Science ®Google Scholar
• Shiha, Mohamed, El-Badawy, Sherif, and Gabr, Alaa, 2020. Modeling and performance evaluation of asphalt mixtures and aggregate bases containing steel slag. Construction and Building Materials, 248, 118710. doi:10.1016/j.conbuildmat.2020.118710.
   Web of Science ®Google Scholar
• Simon, Dan, 2008. Biogeography-based optimization. IEEE Transactions on Evolutionary Computation, 12 (6), 702–713. doi:10.1109/TEVC.2008.919004.
   Web of Science ®Google Scholar
• Solanki, Pranshoo, Zaman, Musharraf, and Ebrahimi, Ali, 2009. Regression and artificial neural network modeling of resilient modulus of subgrade soils for pavement design applications. In: Studies in computational intelligence, 269–304. doi:10.1007/978-3-642-04586-8_10.
   Google Scholar
• Taylor, Karl E., 2005. Taylor diagram primer. Work. Pap. doi:10.1029/2000JD900719.
   Google Scholar
• Wei, Yan, et al., 2019. Application of extreme learning machine for predicting chlorophyll-a concentration inartificial upwelling processes. Mathematical Problems in Engineering, 2019 (May), 1–11. doi:10.1155/2019/8719387.
   Google Scholar