Reliability analysis of a shallow foundation on clayey soil based on settlement criteria

Rahul Ray; Pijush Samui; Lal Bahadur Roy

Authors

Rahul Ray Department of Civil Engineering, GLA University Mathura, Mathura, Uttar Pradesh, India, Pin code-281406
Pijush Samui Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India, Pin code-800005
Lal Bahadur Roy Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India, Pin code-800005

Keywords:

adaptive network based fuzzy inference system, anfis, coefficient of variation, cov, functional network, reliability analysis, soil parameters

Abstract

Soil is a heterogeneous medium and the involvement of its many effective attributes in geotechnical behaviour for soil-foundation system makes the prediction of settlement of shallow foundation on soil a complex engineering problem. As the understanding about the soils are improving, the variability in soil attributes is taken into consideration. As result, the present research approach has also shifted from deterministic to probabilistic approach. The present paper describes the application of two probabilistic based soft computing techniques i.e. Adaptive Network based Fuzzy Inference System (ANFIS) and Functional Network (FN) to study the shallow foundation reliability based on settlement criteria. These models are simple and reliable and can be used for routine design practice. In addition, FN and ANFIS were tested to find their adoptability for shallow foundation settlement prediction considering different soil attributes. Models performance was tested based on different fitness parameters i. e. RMSE, VAF, RSR, β, etc. Functional network (FN) model outperformed in terms of all fitness parameters (RMSE=0.0017, VAF=98.512, RSR=0.1416, NS=0.979, RPD=7.062) as compared to ANFIS (RMSE=0.0026, VAF=95.687, RSR=0.2148, NS=0.953, RPD=4.655). The results show that FN approach can be used as a reliable soft computing technique for non-linear problems like settlement of shallow foundations on soils.

References

Abu-Farsakh, M. Y., & Titi, H. H. (2004). Assessment of Direct Cone Penetration Test Methods for Predicting the Ultimate Capacity of Friction Driven Piles. Journal of Geotechnical and Geoenvironmental Engineering, 130(9), 935–944. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:9(935)

Armaghani, D. J., Mirzaei, F., Shariati, M., Trung, N. T., Shariati, M., & Trnavac, D. (2020). Hybrid ann-based techniques in predicting cohesion of sandy-soil combined with fiber. Geomechanics and Engineering, 20(3), 191–205. https://doi.org/10.12989/gae.2020.20.3.191

Arora, K. R. (2004). Soil mechanics and foundation engineering in S.I. units. Retrieved form https://www.scribd.com/document/283580022/Soil-Mechanics-Foundation-Engineering-by-K-R-Arora-6th-Edition

Babu, G. L. S., & Srivastava, A. (2007). Reliability analysis of allowable pressure on shallow foundation using response surface method. Computers and Geotechnics, 34(3), 187–194. https://doi.org/10.1016/j.compgeo.2006.11.002

Baecher, G. B., & Christian, J. T. (2003). Reliability and statistics in geotechnical engineering. Chichester, UK: John Wiley & Sons.

Boumezerane, D. (2022). Recent Tendencies in the Use of Optimization Techniques in Geotechnics: A Review. Geotechnics, 2(1), 114–132. https://doi.org/10.3390/GEOTECHNICS2010005

Castillo, E., Cobo, A., Manuel Gutiérrez, J., & Pruneda, E. (1999). Working with differential, functional and difference equations using functional networks. Applied Mathematical Modelling, 23(2), 89–107. https://doi.org/10.1016/S0307-904X(98)10074-4

Castillo, E., Gutiérrez, J. M., Cobo, A., & Castillo, C. (2000). Some learning methods in functional networks. Computer‐Aided Civil and Infrastructure Engineering, 15(6), 426-438. https://doi.org/10.1111/0885-9507.00205

Chwała, M., & Wengang, Z. (2022). Broken line random failure mechanism method in foundation bearing capacity assessment for spatially variable soil. Computers and Geotechnics, 150, 104903. https://doi.org/10.1016/j.compgeo.2022.104903

Das, S. K., & Basudhar, P. K. (2006). Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33(8), 454–459. https://doi.org/10.1016/j.compgeo.2006.08.006

Deo, R. C., Samui, P., & Kim, D. (2016). Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environmental Research and Risk Assessment, 30(6), 1769–1784. https://doi.org/10.1007/s00477-015-1153-y

Dindarloo, S. R. (2015). Prediction of blast-induced ground vibrations via genetic programming. International Journal of Mining Science and Technology, 25(6). https://doi.org/10.1016/j.ijmst.2015.09.020

Dodigović, F., Ivandić, K., Kovačević, M. S., & Soldo, B. (2021). Error Evaluation and Suitability Assessment of Common Reliability Methods in the Case of Shallow Foundations. Applied Sciences, 11(2), 795. https://doi.org/10.3390/APP11020795

Duc Nguyen, M., NguyenHai, H., Al-Ansari, N., Amiri, M., Ly, H. B., Prakash, I., & Pham, B. T. (2022). Hybridization of differential evolution and adaptive-network-based fuzzy inference systemin estimation of compression coefficient of plastic clay soil. CMES - Computer Modeling in Engineering and Sciences, 130(1), 149–166. https://doi.org/10.32604/CMES.2022.017355

Fatolahzadeh, S., & Mehdizadeh, R. (2021). Reliability Assessment of Shallow Foundation Stability Under Eccentric Load Using Monte Carlo and First Order Second Moment Method. Geotechnical and Geological Engineering, 39(8), 5651–5664. https://doi.org/10.1007/S10706-021-01852-6/TABLES/7

Ghosh, S., Singh, D., Kumar, R., & Maharaj, S. (2021). Phase transition of AdS black holes in 4D EGB gravity coupled to nonlinear electrodynamics. Annals of Physics, 424. https://doi.org/10.1016/j.aop.2020.168347

Gokceoglu, C. (2002). A fuzzy triangular chart to predict the uniaxial compressive strength of Ankara agglomerates from their petrographic composition. Engineering Geology, 66(1–2), 39–51. https://doi.org/10.1016/S0013-7952(02)00023-6

Griffiths, D. V., & Fenton, G. A. (Eds.). (2007). Probabilistic methods in geotechnical engineering (Vol. 491). Berlin, Germany: Springer Science & Business Media. https://doi.org/10.1007/978-3-211-73366-0

Hajihassani, M., Jahed Armaghani, D., Marto, A., & Tonnizam Mohamad, E. (2015). Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm. Bulletin of Engineering Geology and the Environment, 74(3), 873-886. https://doi.org/10.1007/s10064-014-0657-x

Homaei, F., & Najafzadeh, M. (2020). A reliability-based probabilistic evaluation of the wave-induced scour depth around marine structure piles. Ocean Engineering, 196, 106818. https://doi.org/10.1016/j.oceaneng.2019.106818

Jain, S. K., & Sudheer, K. P. (2008). Fitting of Hydrologic Models: A Close Look at the Nash–Sutcliffe Index. Journal of Hydrologic Engineering, 13(10), 981–986. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:10(981)

Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685. DOI: 10.1109/21.256541

Jones, A. L., Kramer, S. L., & Arduino, P. (2002). Estimation of uncertainty in geotechnical properties for performance-based earthquake engineering. Pacific Earthquake Engineering Research Center, College of Engineering, University of California.

Karimi, I. (2003). Application of Neuro-Fuzzy systems in estimating the response of sediment-filled valleys. In International Fuzzy Systems Association Congress. Retrived form https://www.researchgate.net/publication/320555422_Application_of_Neuro-Fuzzy_systems_in_estimating_the_response_of_sediment-filled_valleys

Khan, S. Z., Suman, S., Pavani, M., & Das, S. K. (2016). Prediction of the residual strength of clay using functional networks. Geoscience Frontiers, 7(1), 67–74. https://doi.org/10.1016/j.gsf.2014.12.008

Kisi, O., Shiri, J., & Tombul, M. (2013). Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51, 108–117. https://doi.org/10.1016/j.cageo.2012.07.001

Krizek, R. J., Corotis, R. B., & El-Moursi, H. H. (1977). Probabilistic analysis of predicted and measured settlements. Canadian Geotechnical Journal, 14(1), 17–33. https://doi.org/10.1139/t77-002

Legates, D. R., & McCabe, G. J. (2013). A refined index of model performance: a rejoinder. International Journal of Climatology, 33(4), 1053–1056. https://doi.org/10.1002/joc.3487

Luat, N. V., Lee, K., & Thai, D. K. (2020). Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils. Geomechanics and Engineering, 20(5), 385–397. https://doi.org/10.12989/gae.2020.20.5.385

Mikaeil, R., Haghshenas, S. S., Ozcelik, Y., & Gharehgheshlagh, H. H. (2018). Performance Evaluation of Adaptive Neuro-Fuzzy Inference System and Group Method of Data Handling-Type Neural Network for Estimating Wear Rate of Diamond Wire Saw. Geotechnical and Geological Engineering, 36(6), 3779–3791. https://doi.org/10.1007/S10706-018-0571-2

Mikaeil, R., Piri, M., Shaffiee Haghshenas, S., Careddu, N., & Hashemolhosseini, H. (2022). An Experimental-Intelligent Method to Predict Noise Value of Drilling in Dimension Stone Industry. Journal of Mining and Environment, 13(3), 693–713. https://doi.org/10.22044/JME.2022.12092.2206

Momeni, E., Nazir, R., Armaghani, D. J., & Maizir, H. (2015). Application of artificial neural network for predicting shaft and tip resistances of concrete piles. Earth Sciences Research Journal, 19(1), 85–93. https://doi.org/10.15446/esrj.v19n1.38712

Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Transactions of the ASABE, 50(3), 885–900. https://doi.org/10.13031/2013.23153

Mustafa, R., Samui, P., & Kumari, S. (2022). Reliability Analysis of Gravity Retaining Wall Using Hybrid ANFIS. Infrastructures, 7(9), 121. https://doi.org/10.3390/INFRASTRUCTURES7090121

Najafzadeh, M., Homaei, F., & Farhadi, H. (2021). Reliability assessment of water quality index based on guidelines of national sanitation foundation in natural streams: integration of remote sensing and data-driven models. Artificial Intelligence Review, 54(6), 4619–4651. https://doi.org/10.1007/S10462-021-10007-1/FIGURES/6

Najafzadeh, M., Homaei, F., & Mohamadi, S. (2022). Reliability evaluation of groundwater quality index using data-driven models. Environmental Science and Pollution Research, 29(6), 8174–8190. https://doi.org/10.1007/S11356-021-16158-6

Nazeeh, K. M., & Sivakumar Babu, G. L. (2022). Reliability-based design of geogrid reinforced soil foundation using kriging surrogates. Geosynthetics International. https://doi.org/10.1680/JGEIN.21.00068

Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences | BibSonomy. Ph.D. Dissertation, Harvard University, Cambridge. Retrived form https://www.bibsonomy.org/bibtex/2b0644d7aa84be0df0f198d586d341843/schaul

Phoon, K. K. (2002). Potential application of reliability-based design to geotechnical engineering. In Proceedings of 4th Colombian Geotechnical Seminar, Medellin. https://scholar.googleusercontent.com/scholar.bib?q=info:QM5z871ibZUJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAW_v5-Wzp-k_66xleNzjzo49Dfsq0PFXI&scisf=4&ct=citation&cd=-1&hl=en

Pramanik, R., Baidya, D. K., & Dhang, N. (2021). Reliability assessment of three-dimensional bearing capacity of shallow foundation using fuzzy set theory. Frontiers of Structural and Civil Engineering, 15(2), 478–489. https://doi.org/10.1007/S11709-021-0698-8

Prasomphan, S., & Machine, S. M. (2013). Generating prediction map for geostatistical data based on an adaptive neural network using only nearest neighbors. International Journal of Machine Learning and Computing, 3(1), 38.

Praveen, K., & Roy, L. B. (2021). Study Of Reference Evapotranspiration Based Deficit Irrigation In The Sone Command Area In Bihar, India–A Case Study. Nveo-Natural Volatiles & Essential Oils Journal| (NVEO), 8(6), 1242-1258.

Praveen, K., & Roy, L. B. (2022). Assessment of Groundwater Quality Using Water Quality Indices: A Case Study of Paliganj Distributary, Bihar, India. Etasr, 12(1), 8199–8203. https://orcid.org/0000-0003-4209-6007

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. Atmospheric Chemistry and Physics, 10(16), 7643–7654. https://doi.org/10.5194/acp-10-7643-2010

Ray, R., Choudhary, S. S., & Roy, L. B. (2021a). Reliability Analysis of Layered Soil Slope Stability using ANFIS and MARS Soft Computing Techniques. International Journal of Performability Engineering, 17(7), 647. https://doi.org/10.23940/IJPE.21.07.P9.647656

Ray, R., Choudhary, S. S., & Roy, L. B. (2021b). Reliability analysis of soil slope stability using MARS, GPR and FN soft computing techniques. Modeling Earth Systems and Environment, 1–11. https://doi.org/10.1007/S40808-021-01238-W

Ray, R., Kumar, D., Samui, P., Roy, L. B., Goh, A. T. C., & Zhang, W. (2021c). Application of soft computing techniques for shallow foundation reliability in geotechnical engineering. Geoscience Frontiers, 12(1), 375–383. https://doi.org/10.1016/j.gsf.2020.05.003

Ray, R., & Roy, L. B. (2021). Reliability Analysis Of Soil Slope Stability Using Ann, Anfis, Pso-Ann Soft Computing Techniques. NVEO-Natural Volatiles & Essential Oils, 8(6), 3478–3491. https://www.nveo.org/index.php/journal/article/view/4100

Saadat, M., & Bayat, M. (2022). Prediction of the unconfined compressive strength of stabilised soil by Adaptive Neuro Fuzzy Inference System (ANFIS) and Non-Linear Regression (NLR). Geomechanics and Geoengineering, 17(1), 80–91. https://doi.org/10.1080/17486025.2019.1699668

Saseendran, R., & Dodagoudar, G. R. (2020). Reliability analysis of slopes stabilised with piles using response surface method. Geomechanics and Engineering, 21(6), 513–525. https://doi.org/10.12989/gae.2020.21.6.513

Sharma, H., & Jalal, A. S. (2021). Visual question answering model based on graph neural network and contextual attention. Image and Vision Computing, 110, 104165. https://doi.org/10.1016/j.imavis.2021.104165

Simões, J. T., Neves, L. C., Antão, A. N., & Guerra, N. M. C. (2020). Reliability assessment of shallow foundations on undrained soils considering soil spatial variability. Computers and Geotechnics, 119, 103369. https://doi.org/10.1016/J.COMPGEO.2019.103369

Stone, R. J. (1993). Improved statistical procedure for the evaluation of solar radiation estimation models. Solar Energy, 51(4), 289–291. https://doi.org/10.1016/0038-092X(93)90124-7

Sultana, P., Dey, A. K., & Kumar, D. (2022). Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques. Results in Engineering, 15, 100489. https://doi.org/10.1016/J.RINENG.2022.100489

USACE. (1997). Risk-based analysis in geotechnical engineering for support of planning studies, engineering and design. Dept. of Army, USACE Washington, DC. https://scholar.googleusercontent.com/scholar.bib?q=info:bv3fN-6CN7oJ:scholar.google.com/&output=citation&scisig=AAGBfm0AAAAAW_zeF2dtDZUsFebZOFPhdvOSEBPk8WFM&scisf=4&ct=citation&cd=-1&hl=en

Varghese, P. C. (2005). Foundation engineering. Retrived form https://books.google.com/books?hl=en&lr=&id=3_VSLiXA7w0C&oi=fnd&pg=PT19&dq=foundation+engineering+varghese&ots=5uZ1lPxbWh&sig=70QUyF2dqcaRPusQcyupC8uIVEA

Verma, P., Agrawal, P., Amorim, I., & Prodan, R. (2021). WELFake: word embedding over linguistic features for fake news detection. IEEE Transactions on Computational Social Systems, 8(4), 881–893. https://ieeexplore.ieee.org/abstract/document/9395133/

Yadav, P., & Shah, K. (2021). Quinolines, a perpetual, multipurpose scaffold in medicinal chemistry. Bioorganic Chemistry, 109, 104639. https://doi.org/10.1016/j.bioorg.2021.104639

Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision processes. IEEE Transactions on systems, Man, and Cybernetics, 3(1), 28-44. DOI: 10.1109/TSMC.1973.5408575