Soft computing based prediction of friction angle of clay

• Autorzy: Dutta R. K. , Gnananandarao T. , Ladol S.

Identyfikatory

DOI

10.5604/01.3001.0014.4895

• Języki publikacji: EN

Abstrakty

EN

Purpose: This article uses soft computing-based techniques to elaborate a study on the prediction of the friction angle of clay. Design/methodology/approach: A total of 30 data points were collected from the literature to predict the friction angle of the clay. To achieve the friction angle, the independent parameters sand content, silt content, plastic limit and liquid limit were used in the soft computing techniques such as artificial neural networks, M5P model tree and multi regression analysis. Findings: The major findings from this study are that the artificial neural networks are predicting the friction angle of the clay accurately than the M5P model and multi regression analysis. The sensitivity analysis reveals that the clay content is the major influencing independent parameter to predict the friction angle of the clay followed by sand content, liquid limit and plastic limit. Research limitations/implications: The proposed expressions can used to predict the friction angle of the clay accurately but can be further improved using large data for a wider range of applications. Practical implications: The proposed equations can be used to calculate the friction angle of the clay based on sand content, silt content, plastic limit and liquid limit. Originality/value: There is no such expression available in the literature based on soft computing techniques to calculate the friction angle of the clay.

Słowa kluczowe

EN

artificial neural network; sensitivity analysis; M5P model tree; multiregression analysis; friction angle of clay

PL

sztuczna sieć neuronowa; analiza wrażliwości; drzewo modelu M5P; analiza wielokrotnej regresji

• Wydawca: International OCSCO World Press
• Czasopismo: Archives of Materials Science and Engineering
• Rocznik: 2020
• Tom: Vol. 104, nr 2
• Strony: 58--68
• Opis fizyczny: Bibliogr. 57 poz.

Twórcy

autor

Dutta R. K.

• Department of Civil Engineering, National Institute of Technology Hamirpur, Himachal Pradesh, India

autor

Gnananandarao T.

• Department of Civil Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

autor

Ladol S.

• Department of Civil Engineering, National Institute of Technology Hamirpur, Himachal Pradesh, India

Bibliografia

• [1] S.K. Das, P.K. Basudhar, Prediction of residual friction angle of clay using ANN, Engineering Geology 100/3-4 (2008) 142-145. DOI: https://doi.org/10.1016/j.enggeo.2008.03.001
• [2] C. Arvanitidis, E. Steiakakis, Z. Agioutantis, Peak friction angle of soils as function of grain size, Geotechnical and Geological Engineering 37 (2019) 1155-1167. DOI: https://doi.org/10.1007/s10706-018- 0675-8
• [3] S.A. AI-Hamed, M.F. Wahby, A.M. Aboukarima, Artificial Neural Network for soil cohesion and soil internal friction angle prediction from soil physical properties, International Research Journal of Agricultural Science and Soil Science 4/5 (2014) 85¬94. DOI: http:/dx.doi.org/10.14303/irjas.2014.035
• [4] H. Farshbaf, Aghajani, H. Salehzadeh, H. Shahnazari, Application of ANN for calculating anisotropic friction angle of sands and effect on slope stability, Journal of Central South University 22 (2015) 1878-1891. DOI: https://doi.org/10.1007/s11771-015-2707-3
• [5] V.K. Varghese, S.S. Babu, R. Bijukumar, S. Cyrus, B.M. Abraham, Artificial neural network: A solution to the Ambiguity in prediction of engineering properties of fine grained soils, Geotechnical and Geological Engineering 31/4 (2013) 1187-1205. DOI: https://doi.org/10.1007/s10706-013-9643-5
• [6] P. Tizpa, R.J. Chenari, M. Karimpour Fard, S.L. Machado, ANN prediction of some geotechnical properties of soil from their index parameters, Arabian Journal of Geoscience 8 (2015) 2911-2920. DOI: https://doi.org/10.1007/s12517-014-1304-3
• [7] A. Tenpe, S. Kaur, Artificial neural network modelling for predicting compaction parameters based on index properties of soil, International Journal of Science and Research 4/7 (2015) 1198-1202.
• [8] G.R. Khanlari, M. Heidari, A.A. Momeni, Y. Abdilor, Prediction of shear strength parameters of soils using ANN and multivariate regression methods, Engineering Geology 131-132 (2012) 11-18. DOI: https://doi.org/10.1016/j.enggeo.2011.12.006
• [9] C. Venkatasubramanian, G. Dhinakaran, ANN model for predicting CBR from index properties of soils, International Journal of Civil and Structural Engineering 2/2 (2011) 614-620.
• [10] P.G. Rakaraddi, V. Gomarsi, Establishing relationship between CBR with different soil properties, International Journal of Research in Engineering and Technology 4/2 (2015) 182-188.
• [11] J. Jayan, N. Sankar, Prediction of soils compaction parameter using ANN, Asian Journal of Engineering and Technology 3/4 (2015) 368-375.
• [12] A.H. Alavi, A.H. Gondomi, A. Mollahassani, A.A. Heshmati, A. Rashed, Modelling of maximum dry density and optimum moisture content of stabilized soil using ANN, Journal Plant and Nutrition and Soil Science 173/3 (2010) 368-379. DOI: https://doi.org/10.1002/jpln.200800233
• [13] R.K. Dutta, K. Dutta, S. Jeevanandham, Prediction of deviator stress of sand reinforced with waste plastic strips using ANN, International Journal of Geo- synthetics and Ground Engineering 1 (2015) 11. DOI: https://doi.org/10.1007/s40891-015-0013-7
• [14] R.K. Dutta, V.N. Khatri, T. Gnananandarao, Appli-cation of soft computing techniques in predicting the ultimate bearing capacity of shallow footings resting on rock masses, International Journal of Geological and Geotechnical Engineering 5/2 (2019) 1-14.
• [15] R.K. Dutta, T. Gnananandarao, V.N. Khatri, Application of Soft Computing Techniques in Predicting the Ultimate Bearing Capacity of Strip Footing Subjected to Eccentric Inclined Load and Resting on Sand, Journal of Soft Computing in Civil Engineering 3/2 (2019) 30-40. DOI: http://dx.doi.org/10.22115/SCCE.2019.144535.1088
• [16] R.K. Dutta, R. Rani, T. Gnananandarao, Prediction of Ultimate Bearing Capacity of Skirted Footing Resting on Sand Using Artificial Neural Networks, Journal of Soft Computing in Civil Engineering 2/4 (2018) 34-46. DOI: http://dx.doi.org/10.22115/SCCE.2018.133742.1066
• [17] T. Gnananandarao, V.N. Khatri, R.K. Dutta, Prediction of Bearing Capacity of H Plan Shaped Skirted footing on Sand using Soft Computing Techniques, Archives of Material Science and Engineering 103/2 (2020) 62-74. DOI: https://doi.org/10.5604/01.3001.0014.3356
• [18] T. Gnananandarao, R.K. Dutta, V.N. Khatri, Appli-cation of Artificial Neural Network to Predict the Settlement of Shallow Foundations on Cohesionless Soils, in: I.V. Anirudhan, V.B. Maji (eds), Geotechnical Applications, Lecture Notes in Civil Engineering, vol 13, Springer, Singapore, 2019, 51-58. DOI: https://doi.org/10.1007/978-981-13-0368-5 6
• [19] R.K. Dutta, P. Kumar, T. Gnananandarao, Neural network based prediction of shear wave velocity for soils, International Journal of Geological and Geo-technical Engineering 5/2 (2019) 23-34.
• [20] R.K. Dutta, A. Singh, T. Gnananandarao, Prediction of Free Swell Index for the Expansive Soil Using Artificial Neural Networks, Journal of Soft Computing in Civil Engineering 3/1 (2019) 47-62. DOI: http://dx.doi.org/10.22115/SCCE
• [21] A.T.C. Goh, Empirical design in geotechnics using neural networks, Geotechnique 45/4 (1995) 709-714. DOI: https://doi.org/10.1680/geot.1995.45.4.709
• [22] A.T.C. Goh, Pile driving records reanalyzed using neural networks, Journal of Geotechnical Engineering 122/6 (1996) 492-495. DOI: https://doi.org/10.1061/(ASCE)0733-9410(1996)122:6(492)
• [23] I.M. Lee, J.H. Lee, Prediction of pile bearing capacity using artificial neural networks, Computers and Geotechnics 18/3 (1996) 189-200. DOI: https://doi.org/10.1016/0266-352X(95)00027-8
• [24] C.H. Juang, T. Jiang, A. Christopher, Three- dimensional site characterisation: Neural network approach, Geotechnique 51/9 (2001) 799-809. DOI: https://doi.org/10.1680/geot2001.51.9.799
• [25] M.S. Rahman, J. Wang, W. Deng, J.P. Carter, A neural network model for the uplift capacity of suction caissons, Computers and Geotechnics 28/4 (2001) 269-287. DOI: https://doi.org/10.1016/S0266- 352X(00)00033-1
• [26] A.A. Basma, N. Kallas, Modeling soil collapse by artificial neural networks, Geotechnical and Geological Engineering 22/3 (2004) 427-438. DOI: https://doi.org/10.1023/B:GEGE.0000025044.72718.db
• [27] S. Celik, O. Tan, Determination of preconsolidation pressure with artificial neural network, Civil Engineer-ing and Environmental Systems 22/4 (2005) 217-231. DOI: https://doi.org/10.1080/10286600500383923
• [28] M. Moosavi, M.J. Yazdanpanah, R. Doostmohammadi, Modeling the cyclic swelling pressure of mudrock using artificial neural networks, Engineering Geology 87/3-4 (2006) 178-194. DOI: https://doi.org/10.1016/j.enggeo.2006.07.001
• [29] S.K. Das, P.K. Basudhar, Undrained lateral load capacity of piles in clay using artificial neural network, Computers and Geotechnics 33/8 (2006) 454-459. DOI: https://doi.org/10.1016/j.compgeo.2006.08.006
• [30] C. Kayadelen, Estimation of effective stress parameter of unsaturated soils by using artificial neural networks, International Journal for Numerical and Analytical Methods in Geomechanics 32/9 (2008) 1087-1106. DOI: https://doi.org/10.1002/nag.660
• [31] K. Rezaei, B. Guest, A. Friedrich, F. Fayazi, M. Nakhaei, A. Beitollahi, S.M.F. Aghda, Feed forward neural network and interpolation function models to predict the soil and subsurface sediments distribution in Bam, Iran, Acta Geophysica 57/2 (2009) 271-293. DOI: https://doi.org/10.2478/s11600-008-0073-3
• [32] S.E. Cho, Probabilistic stability analyses of slopes using the ANN-based response surface, Computers and Geotechnics 36/5 (2009) 787-797. DOI: https://doi.org/10.1016/j.compgeo.2009.01.003
• [33] S.B. Ikizler, M. Aytekin, M. Vekli, F. Kocabaę, Prediction of swelling pressures of expansive soils using artificial neural networks, Advances in Engineering Software 41/4 (2010) 647-655. DOI: https7/doi.OTg/10J.1Q16j.advengsoft.2009J.1.2005
• [34] H.I. Park, S.R. Lee, Evaluation of the compression index of soils using an artificial neural network, Computers and Geotechnics 38/4 (2001) 472-481. DOI: https://doi.org/10.1016/j.compgeo.2011.02.011
• [35] H.I. Park, Y.T. Kim, Prediction of strength of reinforced lightweight soil using an artificial neural network, Engineering Computations: International Journal for Computer-Aided Engineering and Software 28/5 (2011) 600-615. DOI: https://doi.org/10.1108/02644401111141037
• [36] H.I. Park, Development of neural network model to estimate the permeability coefficient of soils, Marine Georesources and Geotechnology 29/4 (2011) 267-278. DOI: https://doi.org/10.1080/1064119X.2011.554963
• [37] J.D. Olden, D.A. Jackson, Illuminating the “black box” a randomization approach for understanding variable contributions in artificial neural networks, Ecological Modelling 154 (1998) 135-150. DOI: https://doi.org/10.1016/S0304-3800(02)00064-9
• [38] P. Sihag, F. Esmaeilbeiki, B. Singh, I. Ebtehaj, H. Bonakdari, Modeling unsaturated hydraulic conductivity by hybrid soft computing techniques, Soft Computing 23 (2019) 12897-12910. DOI: https://doi.org/10.1007/s00500-019-03847-1
• [39] B. Singh, P. Sihag, A. Tomar, A. Sehgad, Estimation of compressive strength of high-strength concrete by ran-dom forest and M5P model tree approaches, Journal of Materials and Engineering Structures 6 (2019) 583-592.
• [40] P. Sihag, M. Kumar, B. Singh, Assessment of infiltration models developed using soft computing techniques, Geology, Ecology, and Landscapes (2020) 1-11. DOI: https://doi.org/10.1080/24749508.2020.1720475
• [41] A. Sepahvand, B. Singh, P. Sihag, A.N. Samani, H. Ahmadi, S.F. Nia, Assessment of the various soft computing techniques to predict sodium absorption ratio (SAR), ISH Journal of Hydraulic Engineering (2019) 1-12. DOI: https://doi.org/10.1080/09715010.2019.1595185
• [42] B. Singh, P. Sihag, K. Singh, Comparison of infiltration models in NIT Kurukshetra campus, Applied Water Science 8 (2018) 63. DOI: https://doi.org/10.1007/s13201-018-0708-8
• [43] B. Singh, K. Singh, R. Kumar, P. Sihag, Future prediction and trend analysis of temperature of Haryana, Journal of Indian Water Resources Society 38/2 (2018) 24-27.
• [44] K. Singh, Dharmendra, Power density analysis by using soft computing techniques for microbial fuel cell, Journal of Environmental Treatment Techniques (2019) 1068-1073.
• [45] B. Singh, P. Sihag, K. Singh, Modelling of impact of water quality on infiltration rate of soil by random forest regression, Modeling Earth Systems and Environment 3 (2017) 999-1004. DOI: https://doi.org/10.1007/s40808-017-0347-3
• [46] B. Singh, P. Sihag, K. Singh, S. Kumar, Estimation of trapping efficiency of a vortex tube silt ejector, International Journal of River Basin Management (2018) (published online). DOI: https://doi.org/10.1080/15715124.2018.1476367
• [47] K. Singh, Dharmendra, Performance of a dual chamber microbial fuel cell using sodium chloride as catholyte, Pollution 6/1 (2020) 79-86. DOI: https://doi.org/10.22059/poll.2019.285668.651
• [48] H. Rahardjo, A. Satyanaga, E.-C. Leong, Y.S. Ng, H.T.C. Pang, Variability of residual soil properties, Engineering Geology 141-142 (2012) 124-140. DOI: https://doi.org/10.1016/tenggeo.2012.05.009
• [49] P.A.G. Viran, A. Binal, Effects of repeated freeze thaw cycles on physio mechanical properties of cohesive soils, Arabian Journal of Geosciences 11 (2018) 250. DOI: https://doi.org/10.1007/s12517-018-3592-5
• [50] R. Nithiaraj, W.H. Ting, A.S. Balasubramaniam, Strength parameters of residual soils and application to stability analysis of anchored slopes, Geotechnical Engineering Journal 27/2 (1996) 55-82.
• [51] M.O. Karkush, T.A.A. AL-Taher, Geotechnical evaluation of clayey soil contaminated with industrial waste water, Archives of Civil Engineering 63/1 (2017) 47-62. DOI: https://doi.org/10.1515/ace-2017-0004
• [52] S.K. Singh, R.K. Srivastava, S. John, Studies on soil contamination due to used oil and its remediation, Canadian Geotechnical Journal 46/9 (2009) 1077-1083. DOI: https://doi.org/10.1139/T09-047
• [53] S.A. Naeini, S.M. Sadjadi, Effect of waste polymer materials on shear strength of unsaturated clays, Electronic Journal of Geotechnical Engineering 13 (2008) 1-12.
• [54] M.M.E. Zumrawi, L.A.D. Mohammed, Correlation of placement and soil intrinsic properties with shear strength of cohesive soils, Proceedings of the 7th Annu- al Conference for Postgraduate Studies and Scientific Research Basic Sciences and Engineering Studies on Scientific Research and Innovation for Sustainable Development in Africa, Friendship Hall, University of Khartoum, Khartoum, Sudan, 2016, Vol. 6, 234-239.
• [55] R. Alias, A. Kasa and M.R. Taha, Effective shear strength parameters for remoulded granite residual soil in direct shear and triaxial test, Electronic Journal of Geotechnical Engineering 19 (2014) 4559-4569.
• [56] M.A. Shahin, M.B. Jaksa and H.R. Maier, Artificial neural network-based settlement prediction formula for shallow foundations on granular soils, Australian Geomechanics: Journal and News of the Australian Geomechanics Society 37/4 (2002) 45-52.
• [57] K.K. Sorensen, N. Okkels, Correlation between drained shear strength and plasticity index of undisturbed over consolidated clay, Proceedings of the 18th International Conference on Soil Mechanics and Geotechnical Engineering, Paris, 2013, Vol. 1, 423-428.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)