Determination of Multilayer Soil Strength Parameters Using Genetic Algorithm

Seyyed Mohammad Hashemi, Iraj Rahmani


This paper employs a back analysis method to determine soil strength parameters of the Mohr-Coulomb model from in situ geotechnical measurements. The lateral displacement of a soil nailed wall retaining an excavation in Tehran city used as a criterion for the back analysis. For this purpose, a genetic algorithm is applied as an optimization algorithm to minimize the error function, which can perform the back analysis process. When the accuracy of modeling is verified, the back analysis is performed automatically by creating a link between genetic algorithm in MATLAB and Abaqus software using Python programming language. This paper demonstrated that the genetic algorithm is a particularly suitable tool to determine 9 soil strength parameters simultaneously for 3 soil layers of the project site to decrease the difference of lateral displacement between the results of project monitoring and numerical analysis. The soil strength parameters have increased, with the most changes in Young's modulus of the first to third layers as the most effective parameter, 49.45%, 61.67% and 64.35% respectively. The results can be used in advanced engineering analyses and professional works.


Excavation; Back Analysis; Parameters Determination; Mohr-Coulomb Model; Genetic Algorithm; Python Programming Language.


Levasseur, S., Y. Malécot, M. Boulon, and E. Flavigny. “Soil Parameter Identification Using a Genetic Algorithm.” International Journal for Numerical and Analytical Methods in Geomechanics 32, no. 2 (2008): 189–213. doi:10.1002/nag.614.

Sakurai, Shunsuke, Shinichi Akutagawa, Kunifumi Takeuchi, Masato Shinji, and Norikazu Shimizu. “Back Analysis for Tunnel Engineering as a Modern Observational Method.” Tunnelling and Underground Space Technology 18, no. 2–3 (April 2003): 185–196. doi:10.1016/s0886-7798(03)00026-9.

Gao, Wei, Dongliang Chen, and Xu Wang. “Elastic–plastic Model Identification for Rock Surrounding an Underground Excavation Based on Immunized Genetic Algorithm.” SpringerPlus 5, no. 1 (July 11, 2016). doi:10.1186/s40064-016-2726-z.

Jin, Yin-Fu, Zhen-Yu Yin, Shui-Long Shen, and Dong-Mei Zhang. “A New Hybrid Real-Coded Genetic Algorithm and Its Application to Parameters Identification of Soils.” Inverse Problems in Science and Engineering 25, no. 9 (November 24, 2016): 1343–1366. doi:10.1080/17415977.2016.1259315.

Vahdati, Pooya, Séverine Levasseur, Hans Mattsson, and Sven Knutsson. "Inverse soil parameter identification of an earth and rockfill dam by genetic algorithm optimization." In ICOLD European Club Symposium: 10/04/2013-12/04/2013. (2013).

Hui, Wang, and Wang Qing-biao. “Dynamic Optimization Research on the Section Morphology of Large-Span Tunnels in Shallow Rock Mass.” The Open Construction and Building Technology Journal 9, no. 1 (September 10, 2015): 210–213. doi:10.2174/1874836801509010210.

Bartlewska-Urban, M., and T. Strzelecki. “Application of Genetic Algorithms for the Estimation of Hydraulic Conductivity.” Studia Geotechnica et Mechanica 0, no. 0 (August 4, 2018). doi:10.2478/sgem-2018-0013.

Zhao, B. D., L. L. Zhang, D. S. Jeng, J. H. Wang, and J. J. Chen. “Inverse Analysis of Deep Excavation Using Differential Evolution Algorithm.” International Journal for Numerical and Analytical Methods in Geomechanics 39, no. 2 (May 9, 2014): 115–134. doi:10.1002/nag.2287.

Sun, Yang, Qinghui Jiang, Tao Yin, and Chuangbing Zhou. “A Back-Analysis Method Using an Intelligent Multi-Objective Optimization for Predicting Slope Deformation Induced by Excavation.” Engineering Geology 239 (May 2018): 214–228. doi:10.1016/j.enggeo.2018.03.019.

Rechea, C., S. Levasseur, and R. Finno. “Inverse Analysis Techniques for Parameter Identification in Simulation of Excavation Support Systems.” Computers and Geotechnics 35, no. 3 (May 2008): 331–345. doi:10.1016/j.compgeo.2007.08.008.

Tikhonov A, V. Arsenine. Methodes de Resolution des Problemes mal Poses, Translation of Russian by V. Kotliar, Mir ed., Moscow, (1976).

Haupt RL, Haupt SE. "Practical Genetic Algorithms". Wiley: New York, 1998.

Gallagher, Kerry, and Malcolm Sambridge. “Genetic Algorithms: A Powerful Tool for Large-Scale Nonlinear Optimization Problems.” Computers & Geosciences 20, no. 7–8 (August 1994): 1229–1236. doi:10.1016/0098-3004(94)90072-8.

Holland JH. "Adaptation in Natural and Artificial Systems". University of Michigan Press: Ann Arbor, MI, (1975).

Renders JM. Algorithmes Genetiques et Reseaux de Neurones. Hermes; Paris, (1995).

Goldberg, DE, editor. Genetic algorithms in search, optimization and machine learning. Adisson-Wesley; (1989).

Pal, Surajit, G. Wije Wathugala, and Sukhamay Kundu. “Calibration of a Constitutive Model Using Genetic Algorithms.” Computers and Geotechnics 19, no. 4 (January 1996): 325–348. doi:10.1016/s0266-352x(96)00006-7.

Tang, Yu-Geng, and Gordon Tung-Chin Kung. “Application of Nonlinear Optimization Technique to Back Analyses of Deep Excavation.” Computers and Geotechnics 36, no. 1–2 (January 2009): 276–290. doi:10.1016/j.compgeo.2008.02.004.

Singh, Vikas Pratap, and G. L. Sivakumar Babu. “2D Numerical Simulations of Soil Nail Walls.” Geotechnical and Geological Engineering 28, no. 4 (December 25, 2009): 299–309. doi:10.1007/s10706-009-9292-x.

Razavi, Seyyed Kazem, and Masoud Hajialilue Bonab. “Study of Soil Nailed Wall Under Service Loading Condition.” Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 170, no. 2 (April 2017): 161–174. doi:10.1680/jgeen.16.00006.

Dehghan, A., F. Rezaee, and A. Ghanbari, “Back analysis of Karaj metro tunnel to determine geomechanical parameters of the encompassing soil.” Geological Engineering Journal, No.2 (2009). coi: JR_JEG-3-2_001.html.

Hashemi, S. M. “Back analysis of soil strength parameters by genetic algorithms”, Master thesis (2017).

Full Text: PDF

DOI: 10.28991/cej-03091167


  • There are currently no refbacks.

Copyright (c) 2018 Seyyed Mohammad Hashemi, Iraj Rahmani

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.