Engineering Geology

Engineering Geology

Prediction of shear strength parameters of Bandar Abbas soils using artificial neural network

Authors
Mohammad Fathollahy 1
Habib Rahimi menbar 2
Gholamreza shoaei 2
1 university of Kurdistan
2 Tarbiat modares university
10.22034/JEG.2022.16.3.1015261
Abstract
Shear strength parameters are important for assessing the stability of structures, and are costly to calculate using conventional methods. In this research, simple geotechnical techniques and artificial intelligence were used to calculate the angle of internal friction and soil cohesion without the need for more complex testing. To this end, intact samples from 14 boreholes in Bandar Abbas, which had undergone primary geotechnical testing and direct cutting, were selected and used to train neural networks. 195 networks were trained in in this research. To achieve the best performance, feedforward neural networks were first trained in single and double layer modes with a low number of neurons in the middle layer, and the TRAIN BR function was selected due to the high ratio of R (0.97). Then, by incorporating additional layers, the Median model was trained using configurations of 3, 4, and 5 layers, each with varying numbers of neurons in the intermediate layer (50, 40, 30, 20, and 10). The results show that the four-layer MLP network gives the best results, for this mode R training 1, the test R is 0.90 and the total R is 0.98. Finally, to validate the neural network, 15 samples were selected and the input parameters of the network were trained in the optimal states of 2, 3, and 4 layers, then the output of the network was evaluated. For cohesion prediction, the neural network in 4-layer mode (R2=0.99) and 2, 3 and 4-layer networks (R2=0.99) have the best output for the friction angle.
Keywords
Bandar Abbas
Geotechnical parameters prediction
Neural Network
Abu -Farsakh, M.Y., 2004. Evaluation of consolidation characteristics of cohesive soils from piezocone penetration tests (No. FHWA/LA. 03/386). Louisiana Transportation Research Center.
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), pp.935 -944.
Baziar, M.H. , Ghorbani, A., 2005. Evaluation of lateral spreading using artificial neural networks. Soil Dynamics and Earthquake Engineering, 25(1), pp.1 -9.
Cal, Y., 1995. Soil classification by neural network. Advances in Engineering software, 22(2), pp.95 -97.
Chan, W.T., Chow, Y.K. , Liu, L.F., 1995. Neural network: an alternative to pile driving formulas. Computers and geotechnics, 17(2), pp.13 5 -156.
Choi, J., Oh, H.J., Lee, H.J., Lee, C. and Lee, S., 2012. Combining landslide susceptibility maps obtained from frequency ratio, logistic regression, and artificial neural network models using ASTER images and GIS. Engineering geology, 124, pp.12 -23 .
Churchland, P. S., 1992. The computational brain, Cambridge, MA: MIT press.
Das, M., Dey, A.K., 2018. Prediction of bearing capacity of stone columns placed in soft clay using ANN model. Geotechnical and Geological Engineering, 36, pp.1845 -1861.
Das, S.K . , Basudhar, P.K., 2006. Undrained lateral load capacity of piles in clay using artificial neural network. Computers and Geotechnics, 33(8), pp.454 -459.
Dayhoff, J., 1994. Neural network architectures: An introduction VNR , USA.
Ellis, G.W., Yao, C., Zhao, R., Penumadu, D., 1995. Stress -strain modeling of sands using artificial neural networks. J. Geotech. Eng. ASCE 121 (5), 429 –435.
Goh, A.T.C., 1995. Empirical design in geotechnics using neural networks. Geotechnique, 45(4), pp.709 -714.
Hagan M .T., 1995. Neural network design, PWS, USA.
Kalinli, A., Acar, M.C. , Gündüz, Z., 2011. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology, 117(1 -2), pp.29 -38.
Kiefa, M.A., 1998. General regression neural networks for driven piles in cohesionless soils. Journal of Geotechnical and Geoenvironmental Engineering, 124(12), pp.1177 -1185.
Khaboushan, E.A., Emami, H., Mosaddeghi, M.R., Astaraei, A.R., 2018. Estimation of unsaturated shear strength parameters using easily -available soil properties. Soil and Tillage Research, 184, pp.118 -127.
Khanlari, G.R., Heidari, M., Momeni, A.A. , Abdilor, Y., 2012. Prediction of shear strength parameters of soils using artificial neural networks and multivariate regression methods. Engineering Geology, 131, pp.11 -18.
Lee, I.M. , Lee, J.H., 1996. Prediction of pile bearing capacity using artificial neural networks. Computers and geotechnics, 18(3), pp.189 -200.
Lee, S.J., Lee, S.R. , Kim, Y.S., 2003. An approach to estimate unsaturated shear strength using artificial neural network and hyperbolic formulation. Computers and Geotechnics, 30(6), pp.489 -503.
Mahmoud, M.A.A.N., 2013. Reliability of using standard penetration test (SPT) in predicting properties of silty clay with sand soil. International Journal of Civil & Structural Engineering, 3(3), pp.545 -556. MATLAB help center, https://ch.mathworks.com/
Shahin, M.A., Maier, H.R. , Jaksa, M.B., 2004. Data division for developing neural networks applied to geotechnical engineering. journal of Computing in Civil Engineering, 18(2), pp.105 -114.
Mohammadi, M., Fatemi Aghda, S.M., Talkhablou, M. , Cheshomi, A., 2022. Prediction of the shear strength parameters from easily -available soil properties by means of multivariate regression and artificial neural network methods. Geomechanics and Geoengineering, 17(2), pp.442 -454.
Ni, S.H., Lu, P.C. , Juang, C.H., 1996. A fuzzy neural network approach to evaluation of slope failure potential. Computer ‐Aided Civil and Infrastructure Engineering, 11(1), pp.59 -66.
Romero, S. , Pamukcu, S., 1996. Characterization of granular material by low strain dynamic excitation and ANN. In Uncertainty in the Geologic Environment: from Theory to Practice (pp. 1134 -1148).
ASCE. Rosenblatt, F., 1958. The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6), p.386.
Rumelhart, D.E., Hinton, G.E. , Williams, R.J., 1985. Learning internal representations by error propagation.
Shahin, M.A. and Jaksa, M.B., 2006. Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks. Canadian Geotechnical Journal, 43(6), pp.626 -637.
Sivakugan, N., Eckersley, J. , Li, H., 1998. Settlement predictions using neural networks. Australian Civil Engineering Transactions, 40, pp.49 -52.
Teh, C.I., Wong, K.S., Goh, A.T.C. , Jaritngam, S., 1997. Prediction of pile capacity using neural networks. Journal of computing in civil engineering, 11(2), pp.129 -138.
Kim, Y., Satyanaga, A., Rahardjo, H., Park, H. , Sham, A.W.L., 2021. Estimation of effective cohesion using artificial neural networks based on index soil properties: A Singapore case. Engineering Geology, 289, p.106163.
Zhang, C., Wang, X., Zou, X., Tian, J., Liu, B., Li, J., Kang, L., Chen, H. , Wu, Y., 2018. Estimation of surface shear strength of undisturbed soils in the eastern part of northern China’s wind erosion area. Soil and Tillage Research, 178, pp.1 -10.
Zhang, C., Wang, X., Zou, X., Tian, J., Liu, B., Li, J., Kang, L., Chen, H. , Wu, Y., 2018. Estimation of surface shear strength of undisturbed soils in the eastern part of northern China’s wind erosion area. Soil and Tillage Research, 178, pp.1 -10.