Abstract
Soil being a heterogeneous medium, predicting the stability of soil slope is a complex engineering problem due to the involvement of multiple effective attributes of soil in geotechnical behaviour. However, as comprehension of soil variability improves, deterministic methods have been replaced by probabilistic ones. This paper examines the application of two soft computing techniques, Group Method of Data Handling (GMDH) and Random Forests Classifier (RFC), to the study of reliability analysis of clayey soil slope stability. In addition, the applicability of GMDH and RFC in predicting stability of Soil Slope based on distinct soil attributes was evaluated, and model performance was evaluated using various fitness parameters such as RMSE, LMI, Bias Factor, etc. The results indicate that the GMDH model outperformed all fitness parameters, suggesting that the GMDH approach can be used as a reliable soft computing method for addressing non-linear problems, such as the stability of soil slope.
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References
Phoon KK (2002) Potential application of reliability-based design to geotechnical engineering. In: Proceedings of 4th Colombian geotechnical seminar, Medellin, pp 1–22
Yadav P, Shah K (2021) Quinolines, a perpetual, multipurpose scaffold in medicinal chemistry. Bioorg Chem 109
Sharma H, Jalal AS (2021) Visual question answering model based on graph neural network and contextual attention. Image Vis Comput
Verma P, Agrawal P, Amorim I, Prodan R (2021) WELFake: word embedding over linguistic features for fake news detection. IEEE Trans Comput Soc Syst 8:881–893
Ghosh S, Singh D, Kumar R, Maharaj S (2021) Phase transition of AdS black holes in 4D EGB gravity coupled to nonlinear electrodynamics. Ann Phys 424
Praveen K, Roy LB (2022) Assessment of groundwater quality using water quality indices: a case study of Paliganj Distributary, Bihar, India. ETASR 12:8199–8203
Christian JT, Ladd CC, Baecher GB (1994) Reliability applied to slope stability analysis. J Geotech Eng 120:2180–2207. https://doi.org/10.1061/(ASCE)0733-9410(1994)120:12(2180)
Khandelwal M, Singh TN (2013) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222. https://doi.org/10.1016/j.ijrmms.2009.03.004
Ray R, Roy LB (2021) Reliability analysis of soil slope stability using Ann, Anfis, PSO-Ann soft computing techniques. NVEO-Nat Volatiles Essent Oils 8:3478–3491
Ray R, Choudhary SS, Roy LB (2021) Reliability analysis of layered soil slope stability using ANFIS and MARS soft computing techniques. Int J Performability Eng 17:647. https://doi.org/10.23940/ijpe.21.07.p9.647656
Ray R, Choudhary SS, Roy LB (2021) Reliability analysis of soil slope stability using MARS, GPR and FN soft computing techniques. Model Earth Syst Environ 2021:1–11. https://doi.org/10.1007/S40808-021-01238-W
Dang V, Hoang N, Nguyen L, Bui D, Forests PS (2020) A novel GIS-based random forest machine algorithm for the spatial prediction of shallow landslide susceptibility. For MDPI 11:1–20. https://doi.org/10.3390/f11010118
Ray R, Choudhary SS, Roy LB, Kaloop MR, Samui P, Kurup PU, Ahn J, Hu JW (2023) Reliability analysis of reinforced soil slope stability using GA-ANFIS, RFC, and GMDH soft computing techniques. Case Stud Constr Mater 18:e01898. https://doi.org/10.1016/J.CSCM.2023.E01898
Ray R, Samui P, Roy LB (2023) Reliability analysis of a shallow foundation on clayey soil based on settlement criteria. J Curr Sci Technol 13
Ivakhnenko A, Krotov G, Visotsky V (1979) Identification of the mathematical model of a complex system by the self-organization method. Theor Syst Ecol Adv Case Stud
Nguyen TN, Lee S, Nguyen-Xuan H, Lee J (2019) A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling. Comput Methods Appl Mech Eng 354:506–526. https://doi.org/10.1016/J.CMA.2019.05.052
Griffiths DV, Huang J, Fenton GA (2012) Risk assessment in geotechnical engineering: stability analysis of highly variable soils. In: GeoCongress 2012. https://doi.org/10.1061/9780784412138.0004
Ray R, Kumar D, Samui P, Roy LB, Goh ATC, Zhang W (2020) Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. Geosci Front 12:375–383. https://doi.org/10.1016/j.gsf.2020.05.003
Jain SK, Sudheer KP (2008) Fitting of hydrologic models: a close look at the Nash-Sutcliffe index. J Hydrol Eng 13:981–986. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:10(981)
Kisi O, Shiri J, Tombul M (2013) Modeling rainfall-runoff process using soft computing techniques. Comput Geosci 51:108–117. https://doi.org/10.1016/j.cageo.2012.07.001
Babu GLS, Srivastava A (2007) Reliability analysis of allowable pressure on shallow foundation using response surface method. Comput Geotech 34:187–194. https://doi.org/10.1016/j.compgeo.2006.11.002
Kung GT, Juang CH, Hsiao EC, Hashash YM (2007) Simplified model for wall deflection and ground-surface settlement caused by braced excavation in clays. J Geotech Geoenviron Eng 133:731–747. https://doi.org/10.1061/(ASCE)1090-0241(2007)133:6(731)
Prasomphan S, Machine SM (2013) Generating prediction map for geostatistical data based on an adaptive neural network using only nearest neighbors. Int J Mach Learn Comput 3
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900. https://doi.org/10.13031/2013.23153
Armstrong JS, Collopy F (1992) Error measures for generalizing about forecasting methods: empirical comparisons. Int J Forecast 8:69–80. https://doi.org/10.1016/0169-2070(92)90008-W
Viscarra Rossel RA, McGlynn RN, McBratney AB (2006) Determining the composition of mineral-organic mixes using UV–vis–NIR diffuse reflectance spectroscopy. Geoderma 137:70–82. https://doi.org/10.1016/j.geoderma.2006.07.004
Raventos-Duran T, Camredon M, Valorso R, Mouchel-Vallon C, Aumont B (2010) Structure-activity relationships to estimate the effective Henry’s law constants of organics of atmospheric interest. Atmos Chem Phys 10:7643–7654. https://doi.org/10.5194/acp-10-7643-2010
Matérn B (1960) Spatial variation, volume 36 of lecture notes in statistics, 2nd edn. Springer-Verlag, New York (First edn. Publ. by Meddelandan fran Statens …, 1960)
Lal Bahadur R, Praveen P (2022) Study of soil erosion by using remote sensing and GIS techniques in Sone command area in Bihar, India. Mater Today Proc 62:1664–1670. https://doi.org/10.1016/J.MATPR.2022.04.739
Stone RJ (1993) Improved statistical procedure for the evaluation of solar radiation estimation models. Sol Energy 51:289–291. https://doi.org/10.1016/0038-092X(93)90124-7
USACE (1997) Risk-based analysis in geotechnical engineering for support of planning studies, engineering and design. Dept. Army, USACE Washington, DC
Taylor KE (2001) Summarizing multiple aspects of model performance in a single diagram. J Geophys Res Atmos 106:7183–7192. https://doi.org/10.1029/2000JD900719
Mann HB, Whitney DR (1947) On a test of whether one of two random variables is stochastically larger than the other. Ann Math Stat 18:50–60. https://doi.org/10.1214/aoms/1177730491
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Ray, R. (2024). Reliability Analysis of Clayey Soil Slope Stability Using GMDH and RFC Soft Computing Techniques. In: Sreekeshava, K.S., Kolathayar, S., Vinod Chandra Menon, N. (eds) Civil Engineering for Multi-Hazard Risk Reduction. IACESD 2023. Lecture Notes in Civil Engineering, vol 457. Springer, Singapore. https://doi.org/10.1007/978-981-99-9610-0_11
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