Nazeer S, Dutta RK. Bearing capacity of E-shaped footing on layered sand. J Achiev Mater Manuf Eng 2021;2:49–60. https://doi.org/10.5604/01.3001.0015.0517.
 Thakur A, Dutta RK. Experimental and numerical studies of skirted hexagonal footings on three sands. SN Appl Sci 2020;2:487. https://doi.org/10.1007/s42452-020-2239-9.
 Gnananandarao T, Khatri VN, Dutta RK. Performance of Multi-Edge Skirted Footings Resting on Sand. Indian Geotech J 2018;48:510–9. https://doi.org/10.1007/s40098-017-0270-6.
 Gnananandarao T, Khatri VN, Dutta RK. Pressure settlement ratio behavior of plus shaped skirted footing on sand. J Civ Eng 2018;46:161–70.
 Gnananandarao T, Dutta RK, Khatri VN. Model studies of plus and double box shaped skirted footings resting on sand. Int J Geo-Engineering 2020;11:2. https://doi.org/10.1186/s40703-020-00109-0.
 Davarci B, Ornek M, Turedi Y. Model studies of multi-edge footings on geogrid-reinforced sand. Eur J Environ Civ Eng 2014;18:190–205. https://doi.org/10.1080/19648189.2013.854726.
 Davarci B, Ornek M, Turedi Y. Analyses of multi-edge footings rested on loose on loose and dense sand. Period Polytech Civ Eng 2014;58:355–70. https://doi.org/10.3311/PPci.2101.
 Ghazavi M, Mokhtari S. Numerical investigation of load-settlement characteristics of multi-edge shallow foundations. 12th Int. Conf. Int. Assoc. Comput. Methods Adv. Geomech. (IACMAG), Goa, India, 2008.
 Ghazavi M, Mirzaeifar H. Bearing capacity of multi-edge shallow foundations on geogrid-reinforced sand. Proc. 4th Int. Conf. Geotech. Eng. Soil Mech., 2010, p. 1–9.
 Khatri VN, Debbarma SP, Dutta RK, Mohanty B. Pressure-settlement behavior of square and rectangular skirted footings resting on sand. Geomech Eng 2017;12:689–705. https://doi.org/10.12989/gae.2017.12.4.689.
 Dutta RK, Dutta K, Jeevanandham S. Prediction of Deviator Stress of Sand Reinforced with Waste Plastic Strips Using Neural Network. Int J Geosynth Gr Eng 2015;1:11. https://doi.org/10.1007/s40891-015-0013-7.
 Boger Z, Guterman H. Knowledge extraction from artificial neural network models. 1997 IEEE Int. Conf. Syst. Man, Cybern. Comput. Cybern. Simul., vol. 4, IEEE; 1997, p. 3030–5.
 Duan K, Cao S, Li J, Xu C. Prediction of Neutralization Depth of R.C. Bridges Using Machine Learning Methods. Crystals 2021;11:210. https://doi.org/10.3390/cryst11020210.
 Das SK. Artificial Neural Networks in Geotechnical Engineering. Metaheuristics Water, Geotech. Transp. Eng., Elsevier; 2013, p. 231–70. https://doi.org/10.1016/B978-0-12-398296-4.00010-6.
 Banimahd M, Yasrobi SS, P.K.Woodward. Artificial neural network for stress–strain behavior of sandy soils: Knowledge based verification. Comput Geotech 2005;32:377–86. https://doi.org/10.1016/j.compgeo.2005.06.002.
 Ikizler SB, Vekli M, Dogan E, Aytekin M, Kocabas F. Prediction of swelling pressures of expansive soils using soft computing methods. Neural Comput Appl 2014;24:473–85. https://doi.org/10.1007/s00521-012-1254-1.
 Yilmaz I, Kaynar O. Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils. Expert Syst Appl 2011;38:5958–66. https://doi.org/10.1016/j.eswa.2010.11.027.
 Ahangar-Asr A, Faramarzi A, Mottaghifard N, Javadi AA. Modeling of permeability and compaction characteristics of soils using evolutionary polynomial regression. Comput Geosci 2011;37:1860–9. https://doi.org/10.1016/j.cageo.2011.04.015.
 Ito Y. Approximation Capability of Layered Neural Networks with Sigmoid Units on Two Layers. Neural Comput 1994;6:1233–43. https://doi.org/10.1162/neco.19126.96.36.1993.
 Dutta RK, Rani R, Rao TG. Prediction of ultimate bearing capacity of skirted footing resting on sand using artificial neural networks. J Soft Comput Civ Eng 2018;2:34–46.
 Rezaei H, Nazir R, Momeni E. Bearing capacity of thin-walled shallow foundations: an experimental and artificial intelligence-based study. J Zhejiang Univ A 2016;17:273–85. https://doi.org/10.1631/jzus.A1500033.
 Nazir R, Momeni E, Marsono K. Prediction of bearing capacity for thin-wall spread foundations using ICA-ANN predictive model. Proc. Int. Conf. Civil, Struct. Transp. Eng. Ottawa, Ontario, 2015.
 Momeni E, Armaghani DJ, Fatemi SA, Nazir R. Prediction of bearing capacity of thin-walled foundation: a simulation approach. Eng Comput 2018;34:319–27.
 Bardhan A, Samui P, Ghosh K, Gandomi AH, Bhattacharyya S. ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions. Appl Soft Comput 2021;110:107595. https://doi.org/10.1016/j.asoc.2021.107595.
 Kardani N, Bardhan A, Roy B, Samui P, Nazem M, Armaghani DJ, et al. A novel improved Harris Hawks optimization algorithm coupled with ELM for predicting permeability of tight carbonates. Eng Comput 2021. https://doi.org/10.1007/s00366-021-01466-9.
 Kardani N, Bardhan A, Samui P, Nazem M, Zhou A, Armaghani DJ. A novel technique based on the improved firefly algorithm coupled with extreme learning machine (ELM-IFF) for predicting the thermal conductivity of soil. Eng Comput 2021. https://doi.org/10.1007/s00366-021-01329-3.
 Kaloop MR, Bardhan A, Kardani N, Samui P, Hu JW, Ramzy A. Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power. Renew Sustain Energy Rev 2021;148:111315. https://doi.org/10.1016/j.rser.2021.111315.
 Salahudeen AB, Ijimdiya TS, Eberemu AO, Osinubi KJ. Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust. Soft Comput Civ Eng 2018;2:50–71. https://doi.org/10.22115/SCCE.2018.128634.1059.
 Kardani N, Bardhan A, Kim D, Samui P, Zhou A. Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO. J Build Eng 2021;35:102105. https://doi.org/10.1016/j.jobe.2020.102105.
 Kumar M, Bardhan A, Samui P, Hu JW, Kaloop MR. Reliability Analysis of Pile Foundation Using Soft Computing Techniques: A Comparative Study. Processes 2021;9:486. https://doi.org/10.3390/pr9030486.
 Zarei F, Baghban A. Phase behavior modelling of asphaltene precipitation utilizing MLP-ANN approach. Pet Sci Technol 2017;35:2009–15. https://doi.org/10.1080/10916466.2017.1377233.
 GHANI S, KUMARI S, BARDHAN A. A novel liquefaction study for fine-grained soil using PCA-based hybrid soft computing models. Sādhanā 2021;46:113. https://doi.org/10.1007/s12046-021-01640-1.
 Kumar R, Singh MP, Roy B, Shahid AH. A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions. Water Resour Manag 2021;35:1927–60. https://doi.org/10.1007/s11269-021-02822-6.