Deep neural network and ANN ensemble for slope stability prediction

• Autorzy: Gupta A. , Aggarwal Y. , Aggarwal P.

Identyfikatory

DOI

10.5604/01.3001.0016.0975

• Języki publikacji: EN

Abstrakty

EN

Purpose: Application of deep neural networks (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability with a comparative performance analysis done for all techniques. Design/methodology/approach: 1000 cases with different geotechnical and similar Geometrical properties were collected and analysed using the Limit Equilibrium based Morgenstern-Price Method with input variables as the strength parameters of the soil layers, i.e., Su (Upper Clay), Su (Lower Clay), Su (Peat), angle of internal friction (φ), Su (Embankment) with the factor of safety (FOS) as output. The evaluation and comparison of the performance of predicted models with cross-validation having ten folds were made based on correlation-coefficient (CC), Nash-Sutcliffe-model efficiency-coefficient (NSE), root-mean-square-error (RMSE), mean-absolute-error (MAE) and scattering-index (S.I.). Sensitivity analysis was conducted for the effects of input variables on FOS of soil stability based on their importance. Findings: The results showed that these techniques have great capability and reflect that the proposed model by DNN can enhance performance of the model, surpassing ensemble in prediction. The Sensitivity analysis outcome demonstrated that Su (Lower Clay) significantly affected the factor of safety (FOS), trailed by Su (Peat). Research limitations/implications: This paper sets sight on use of deep neural network (DNN) and ensemble of ANN with bagging for estimating of factor of safety (FOS) of soil stability. The current approach helps to understand the tangled relationship of various inputs to estimate the factor of safety of soil stability using DNN and ensemble of ANN with bagging. Practical implications: A dependable prediction tool is provided, which suggests that model can help scientists and engineers optimise FOS of soil stability. Originality/value: Recently, DNN and ensemble of ANN with bagging have been used in various civil engineering problems as reported by several studies and has also been observed to be outperforming the current prevalent modelling techniques. DNN can signify extremely changing and intricate high-dimensional functions in correlation to conventional neural networks. But on a detailed literature review, the application of these techniques to estimate factor of safety of soil stability has not been observed.

Słowa kluczowe

EN

upper clay; lower clay; peat; angle of internal friction; embankment; factor of safety; slope stability; deep neural network; ensemble

PL

glina górna; glina dolna; torf; kąt tarcia wewnętrznego; nasyp; współczynnik bezpieczeństwa; stabilność zbocza; sieć neuronowa głęboka; zespół

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2022
• Tom: Vol. 116, nr 1
• Strony: 14--27
• Opis fizyczny: Bibliogr. 56 poz.

Twórcy

autor

Gupta A.

• Civil Engineering Department, Punjab Engineering College, Chandigarh, India

autor

Aggarwal Y.

• Civil Engineering Department, National Institute of Technology, Kurukshetra, India

autor

Aggarwal P.

• Civil Engineering Department, National Institute of Technology, Kurukshetra, India

• https://orcid.org/0000-0003-1201-2757

Bibliografia

• [1] L. Jing, J.A. Hudson, Numerical methods in rock mechanics, International Journal of Rock Mechanics and Mining Sciences 39/4 (2002) 409-427. DOI: https://doi.org/10.1016/S1365-1609(02)00065-5
• [2] A. Thakur, R.K. Dutta, Study of bearing capacity of skirted irregular pentagonal footings on different sands, Journal of Achievements in Materials and Manufacturing Engineering 105/1 (2021) 5-17. DOI: https://doi.org/10.5604/01.3001.0014.8741
• [3] T. Gnananandaro, V.N. Khatri, R.K. Dutta, Prediction of bearing capacity of H-shaped skirted footings on sand using soft computing techniques, Archives of Materials Science and Engineering 103/2 (2020) 62-74. DOI: https://doi.org/10.5604/01.3001.0014.3356
• [4] L. Jing, A review of techniques, advances and outstanding issues in numerical modelling for rock mechanics and rock engineering, International Journal of Rock Mechanics and Mining Sciences 40/3 (2003) 283-353. DOI: https://doi.org/10.1016/S1365-1609(03)00013-3
• [5] S.K. Sarma, Seismic stability of earth dams and embankments, Geotechnique 25/4 (1975) 743-761. DOI: https://doi.org/10.1680/geot.1975.25.4.743
• [6] A.J. Li, M.J. Cassidy, Y. Wang, R.S. Merifeld, A.V. Lyamin, Parametric Monte Carlo studies of rock slopes based on the Hoek–Brown failure criterion, Computers and Geotechnics 45 (2012) 11-18. DOI: https://doi.org/10.1016/j.compgeo.2012.05.010
• [7] Y. Aggarwal, P. Aggarwal, P. Sihag, M. Pal, A. Kumar, Estimation of Punching Shear Capacity of Concrete Slabs Using Data Mining Techniques, International Journal of Engineering 32/7 (2019) 908-914. DOI: https://doi.org/10.5829/ije.2019.32.07a.02
• [8] P. Sihag, M.R. Sadikhani, V. Vambol, S. Vambol, A.K. Prabhakar, N. Sharma, Comparative study for deriving stage-discharge–sediment concentration relationships using soft computing techniques, Journal of Achievements in Materials and Manufacturing Engineering 104/2 (2021) 57-76. DOI: https://doi.org/10.5604/01.3001.0014.8489
• [9] P. Aggarwal, Y. Aggarwal, R. Siddique, S. Gupta, H. Garg, Fuzzy Logic Modelling of Compressive Strength of High Strength Concrete (HSC) with Supplementary Cementitious Material, Journal of Sustainable Cement- Based Materials 2/2 (2013) 128-143. DOI: https://doi.org/10.1080/21650373.2013.801800
• [10] A. Aradhana, B. Singh, P. Sihag, Predictive models for estimation of labyrinth weir aeration efficiency, Journal of Achievements in Materials and Manufacturing Engineering 105/1 (2021) 18-32. DOI: https://doi.org/10.5604/01.3001.0014.8742
• [11] R. Siddique, P. Aggarwal, Y. Aggarwal, Prediction of Compressive Strength of Self-Compacting Concrete containing bottom ash using artificial neural network, Advances in Engineering Software 42/10 (2011) 780- 786. DOI: https://doi.org/10.1016/j.advengsoft.2011.05.016
• [12] S.S. Nain, R. Sai, P. Sihag, S. Vambol, V. Vambol, Use of machine learning algorithm for the better prediction of S.R. peculiarities of WEDM of Nimonic-90 super-alloy, Archives of Materials Science and Engineering 95/1 (2019) 12-19. DOI: https://doi.org/10.5604/01.3001.0013.1422
• [13] R. Siddique, P. Aggarwal, Y. Aggarwal, S.M. Gupta, Modeling Properties of Self-Compacting Concrete: Support Vector Machines Approach, Computers and Concrete 5/5 (2008) 461-473. DOI: https://doi.org/10.12989/cac.2008.5.5.461
• [14] M. Rahul, S. Baldev, Prediction of scour depth around bridge piers in tandem arrangement using M5 and ANN regression models Archives of Materials Science and Engineering 102/2 (2020) 49-58. DOI: https://doi.org/10.5604/01.3001.0014.1524
• [15] R. Honysz, Prediction optimization of mechanical properties of ferrite stainless steels after forging treatment with use of genetic algorithms, Archives of Materials Science and Engineering 100/1-2 (2019) 13- 20. DOI: https://doi.org/10.5604/01.3001.0013.5998
• [16] D. Subhash, S.M. Gupta, S. Setia, V. Pavlykivskyi, Estimating the compressive strength of concrete, using vacuum dewatering technique, Archives of Materials Science and Engineering 99/1-2 (2019) 30-41. DOI: https://doi.org/10.5604/01.3001.0013.5880
• [17] P. Aggarwal, R. Siddique, Y. Aggarwal, S.M. Gupta, Modeling the Properties of Self-Compacting Concrete: An M5 Model Tree Based Approach, Proceedings of the 5th International RILEM Symposium on Self- Compacting Concrete, Ghent, Belgium, 2007, 49-54.
• [18] C. Qi, X. Tang, Slope stability prediction using integrated metaheuristic and machine learning approaches: A comparative study, Computers and Industrial Engineering 118 (2018) 112-122. DOI: https://doi.org/10.1016/j.cie.2018.02.028
• [19] R.K. Dutta, T. Gnanandarao, S. Ladol, Soft computing based prediction of friction angle of clay, Archives of Materials Science and Engineering 104/2 (2020) 58-68. DOI: https://doi.org/10.5604/01.3001.0014.4895
• [20] S.H. Ni, P.C. Lu, C.H. Juang, A Fuzzy Neural Network Approach to Evaluation of Slope. Failure Potential, Microcomputers in Civil Engineering 11/1 (1996) 59-66. DOI: https://doi.org/10.1111/j.1467- 8667.1996.tb00309.x
• [21] M.A. Shahin, M.B. Jaksa, H.R. Maier, Artificial neural network applications in geotechnical engineering, Australian Geomechanics 36/1 (2001) 49-62.
• [22] S.J. Lee, S.R. Lee, Y.S. Kim, An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation, Computers and Geotechnics 30/6 (2003) 489-503. DOI: https://doi.org/10.1016/S0266-352X(03)00058-2
• [23] M.G. Sakellariou, M.D. Ferentinoua, A study of slope stability prediction using neural networks, Geotechnical and Geological Engineering 23 (2005) 419-445. DOI: https://doi.org/10.1007/s10706-004-8680-5
• [24] D. Ramakrishnan, T.N. Singh, N. Purwar, K.S. Barde, A. Gulati, S. Gupta, Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India, Computational Geosciences 12/4 (2008) 491-501. DOI: https://doi.org/10.1007/s10596-008-9088-8
• [25] A.J. Choobbasti, F. Farrokhzad, A. Barari, Prediction of slope stability using artificial neural network (case study: Noabad, Mazandaran, Iran), Arabian Journal of Geosciences 2 (2009) 311-319. DOI https://doi.org/10.1007/s12517-009-0035-3
• [26] Y.L. Kuo, M.B. Jaksa, A.V. Lyamin, W.S. Kaggwa, ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil, Computers and Geotechnics 36/3 (2009) 503-516. DOI: https://doi.org/10.1016/j.compgeo.2008.07.002
• [27] Z.G. Qian, A.J. Li, W.C. Chen, A.V. Lyamin, J.C. Jiang, An artificial neural network approach to inhomogeneous soil slope stability predictions based on limit analysis methods, Soils and Foundations 59/2 (2019) 556-569. DOI: https://doi.org/10.1016/j.sandf.2018.10.008
• [28] F. Huang, J. Huang, S. Jiang, C. Zhou, Landslide displacement prediction based on multivariate chaotic model and extreme learning machine, Engineering Geology 218 (2017) 173-186. DOI: https://doi.org/10.1016/j.enggeo.2017.01.016
• [29] Y. Erzin, T. Cetin, The prediction of the critical factor of safety of homogeneous finite slopes using neural networks and multiple regressions, Computers and Geosciences 51 (2013) 305-313. DOI: https://doi.org/10.1016/j.cageo.2012.09.003
• [30] B. Gordan, D.J. Armaghani, M. Hajihassani, M. Monjezi, Prediction of seismic slope stability through combination of particle swarm optimization and neural network, Engineering with Computers 32 (2015) 85-97. DOI: https://doi.org/10.1007/s00366-015-0400-7
• [31] H. Gandomi, A.R. Kashani, M. Mousaviand, M. Jalalvandi, Slope stability analysis using evolutionary optimization techniques, International Journal for Numerical and Analytical Methods in Geomechanics 41/2 (2017) 251-264. DOI: https://doi.org/10.1002/nag.2554
• [32] D.T. Bui, H. Moayedi, M. Gör, A. Jaafari, L.K. Foong, Predicting Slope Stability Failure through Machine Learning Paradigms, ISPRS International Journal of Geo-Information 8/9 (2019) 395. DOI: https://doi.org/10.3390/ijgi8090395
• [33] Z. Liao, Z. Liao, Slope stability evaluation using backpropagation neural networks and multivariate adaptive regression splines, Open Geosciences 12/1 (2020) 1263-1273. DOI: https://doi.org/10.1515/geo- 2020-0198
• [34] N. Kardani, A. Zhou, M. Nazem, S.L. Shen, Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data, Journal of Rock Mechanics and Geotechnical Engineering 13/1 (2021) 188-201. DOI: https://doi.org/10.1016/j.jrmge.2020.05.011
• [35] A. Mahmoodzadeh, M. Mohammadi, H.H. Ibrahim, K.M. Gharrib Noori, S.N. Abdulhamid, H.F.H. Ali, Forecasting sidewall displacement of underground caverns using machine learning techniques. Automation in Construction 123 (2021) 103530. DOI: https://doi.org/10.1016/j.autcon.2020.103530
• [36] A. Mahmoodzadeh, M. Mohammadi, K.M. Gharrib Noori, M. Khishe, H.H. Ibrahim, H.F.H. Ali, S.N. Abdulhamid, Presenting the best prediction model of water inflow into drill and blast tunnels among several machine learning techniques, Automation in Construction 127 (2021) 103719. DOI: https://doi.org/10.1016/j.autcon.2021.103719
• [37] A. Mahmoodzadeh, M. Mohammadi, H.H. Ibrahim, S.N. Abdulhamid, H.F.H. Ali, A. Hasan, M. Khishe, H. Mahmud, Machine learning forecasting models of disc cutters life of tunnel boring machine, Automation in Construction 128 (2021) 103779. DOI: https://doi.org/10.1016/j.autcon.2021.103779
• [38] A. Mahmoodzadeh, M. Mohammadi, S.N. Abdulhamid, H.H. Ibrahim, H.F.H. Ali, S.G. Salim, Dynamic reduction of time and cost uncertainties in tunneling projects, Tunnelling and Underground Space Technology 109 (2021) 103774. DOI: https://doi.org/10.1016/j.tust.2020.103774
• [39] A. Mahmoodzadeh, M. Mohammadi, H.F.H. Ali, H.H. Ibrahim, S.N. Abdulhamid, H.R. Nejati, Prediction of safety factors for slope stability: comparison of machine learning techniques, Natural Hazards 111 (2022) 1771-1799. DOI: https://doi.org/10.1007/s11069-021-05115-8
• [40] A. Mahmoodzadeh, M. Mohammadi, S.G. Salim, H.F.H. Ali, H.H. Ibrahim, S.N. Abdulhamid, H.R. Nejati, S. Rashidi, Machine Learning Techniques to Predict Rock Strength Parameters, Rock Mechanics and Rock Engineering 55 (2022) 1721-1741. DOI: https://doi.org/10.1007/s00603-021-02747-x
• [41] F. Deng, Y. He, S. Zhou, Y. Yu, H. Cheng, X. Wu, Compressive strength prediction of recycled concrete based on deep learning, Construction and Building Materials 175 (2018) 562-569. DOI: https://doi.org/10.1016/j.conbuildmat.2018.04.169
• [42] S.S. Kumar, D.M. Abraham, A deep learning based automated structural defect detection system for sewer pipelines, Proceedings of the ASCE International Conference on Computing in Civil Engineering, Atlanta, Georgia, 2019, 226-233. DOI: https://doi.org/10.1061/9780784482445.029
• [43] K. Dick, L. Russell, Y. Souley Dosso, F. Kwamena, J.R. Green, Deep learning for critical infrastructure resilience, Journal of Infrastructure System 25/2 (2019) 05019003. DOI: https://doi.org/10.1061/(ASCE)IS.1943-555X.0000477
• [44] F. Ding, Z. Zhang, Y. Zhou, X. Chen, B. Ran, Large-scale full-coverage traffic speed estimation under extreme traffic conditions using a big data and deep learning approach: case study in China, Journal of Transportation Engineering Part A System 145/5 (2019) 05019001. DOI: https://doi.org/10.1061/JTEPBS.0000230
• [45] T. Nguyen, A. Kashani, T. Ngo, S. Bordas, Deep neural network with high-order neuron for the prediction of foamed concrete strength, Computer Aided Civil Infrastructure Engineering 34/4 (2019) 316-332. DOI: https://doi.org/10.1111/mice.12422
• [46] P. Yu, X. Yan, Stock price prediction based on deep neural networks, Neural Computing and Applications 32 (2019) 1609-1628. DOI: https://doi.org/10.1007/s00521-019-04212-x
• [47] Y. Zhou, L. Zhang, Z. Yi, Predicting movie box-office revenues using deep neural networks, Neural Computing and Applications 31 (2019) 1855-1865. DOI: https://doi.org/10.1007/s00521-017-3162-x
• [48] G. Singh, M. Pal, Y. Yadav, T. Singla, Deep neural network-based predictive modeling of road accidents, Neural Computing and Applications 32 (2020) 12417- 12426. DOI: https://doi.org/10.1007/s00521-019- 04695-8
• [49] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 436-444. DOI: https://doi.org/10.1038/nature14539
• [50] I. Goodfellow, Y. Bengio, A. Courville, Deep learning, MIT Press, USA, 2016.
• [51] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feed forward neural networks, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics “AISTATS”, Sardinia, Italy, 2010, 249-256.
• [52] D.P. Kingma, J. Ba, Adam: a method for stochastic Optimization, Proceedings of the 3rd International Conference for Learning Representations, San Diego, 2015. DOI: https://arxiv.org/abs/1412.6980
• [53] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, Dropout: a simple way to prevent neural networks from overfitting, Journal of Machine Learning and Research 15/1 (2014) 1929-1958.
• [54] L. Breiman, Bagging predictors, Machine Learning 24/2 (1996) 123-140. DOI: https://doi.org/10.1007/BF00058655
• [55] Z.M. Yaseen, R.C. Deo, A. Hilal, A.M. Abd, L.C. Bueno, S. Salcedo-Sanz, M.L. Nehdi, Predicting compressive strength of lightweight foamed concrete using extreme learning machine model, Advances in Engineering Software 115 (2018) 112-125. DOI: https://doi.org/10.1016/j.advengsoft.2017.09.004
• [56] J.-S. Chou, A.-D. Pham, Enhanced artificial intelligence for ensemble approach to predicting high performance concrete compressive strength, Construction and Building Materials 49 (2013) 554-563. DOI: https://doi.org/10.1016/j.conbuildmat.2013.08.078

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).