Application of Machine Learning Techniques in Predicting the Bearing Capacity of E-shaped Footing on Layered Sand

Document Type : Regular Article

Authors

1 M.Tech. Student, Faculty of Civil Engineering, National Institute of Technology, Hamirpur, India

2 Professor, Faculty of Civil Engineering, National Institute of Technology, Hamirpur, India

Abstract

The paper presents the prediction of bearing capacity equation of E-shaped footing subjected to a vertical concentric load and resting on layered sand using machine learning techniques and the data used in the analysis has been extracted from finite element modelling of the same footing. The input variables used in the developed neural network model were the bearing capacity of square footing, thickness ratio, friction angle ratio and the output were the bearing capacity of E-shaped footing on layered sand. Multiple layer perceptron (MLP) and multiple linear regression (MLR) prediction models were used for the determination of error metrics and the ultimate bearing capacity of E-shaped footing resting on layered sand. Finally, for the ANN model development, a model equation was developed with the assistance of weights and biases, based on the MLP and MLR model using open-source WEKA and Anaconda software respectively. Sensitivity analysis has been performed on the data sets which correlates the various input variables with the output variable of both the models. The coefficient of determination (R2) comes out to be 0.99 and 0.98 for the MLP and MLR models respectively indicating that both the models were able to predict the bearing capacity for the E shaped footing with acceptable accuracy.

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[1]     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.
[2]     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.
[3]     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.
[4]     Gnananandarao T, Khatri VN, Dutta RK. Pressure settlement ratio behavior of plus shaped skirted footing on sand. J Civ Eng 2018;46:161–70.
[5]     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.
[6]     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.
[7]     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.
[8]     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.
[9]     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.
[10]   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.
[11]   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.
[12]   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.
[13]   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.
[14]   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.
[15]   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.
[16]   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.
[17]   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.
[18]   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.
[19]   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.1994.6.6.1233.
[20]   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.
[21]   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.
[22]   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.
[23]   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.
[24]   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.
[25]   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.
[26]   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.
[27]   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.
[28]   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.
[29]   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.
[30]   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.
[31]   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.
[32]   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.
[33]   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.