<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The mechanical parameters of the soil that must be introduced into geotechnical calculations, in particular those carried out by the Finite Element Method, are often poorly understood. The search for the numerical values of these parameters so that the models best reflect the observed reality constitutes the inverse analysis approach. In this article, we are interested in the identification of the mechanical parameters of the soil based on the principle of inverse analysis using the two methods of stochastic optimization, the genetic algorithm and the hybrid genetic algorithm with Tabu search. Soil behavior is represented by the constitutive soil Mohr-Coulomb model. The identification relates to the following two parameters: The shear modulus (<italic>G</italic>) and the friction angle (<italic>φ</italic>). The validation of these two stochastic optimization methods is done on the experimental sheet pile wall of Hochstetten in Germany. The results obtained by applying the genetic algorithm method and the hybrid genetic algorithm method for the identification of the two Mohr-Coulomb parameters (<italic>G</italic>, <italic>φ</italic>) show that the hybridization process of the genetic algorithm combined with the Tabu search method accelerated the convergence of the algorithm to the exact solution of the problem whereas the genetic algorithm alone takes a much longer computation time to reach the exact solution of the problem.</p>
</abstract>

The mechanical parameters of the soil that must be introduced into geotechnical calculations, in particular those carried out by the Finite Element Method, are often poorly understood. The search for the numerical values of these parameters so that the models best reflect the observed reality constitutes the inverse analysis approach. In this article, we are interested in the identification of the mechanical parameters of the soil based on the principle of inverse analysis using the two methods of stochastic optimization, the genetic algorithm and the hybrid genetic algorithm with Tabu search. Soil behavior is represented by the constitutive soil Mohr-Coulomb model. The identification relates to the following two parameters: The shear modulus (G) and the friction angle (φ). The validation of these two stochastic optimization methods is done on the experimental sheet pile wall of Hochstetten in Germany. The results obtained by applying the genetic algorithm method and the hybrid genetic algorithm method for the identification of the two Mohr-Coulomb parameters (G, φ) show that the hybridization process of the genetic algorithm combined with the Tabu search method accelerated the convergence of the algorithm to the exact solution of the problem whereas the genetic algorithm alone takes a much longer computation time to reach the exact solution of the problem.

Identification of Soil Mechanical Parameters by Inverse Analysis Using Stochastic Methods

Department of Hydraulics, Faculty of Technology

Department of Civil Engineering, Faculty of Sciences and Applied Sciences

Department of Civil Engineering, Faculty of Sciences and Technology

Selected Scientific Papers - Journal of Civil Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The mechanical parameters of the soil that must be introduced into geotechnical calculations, in particular those carried out by the Finite Element Method, are...

Identification of Soil Mechanical Parameters by Inverse Analysis Using Stochastic Methods

Publicado en línea: 14 ene 2023

Páginas: 1 - 12

DOI: https://doi.org/10.2478/sspjce-2022-0018

Palabras clave
inverse analysis, identification of parameters, stochastic methods, numerical modeling, sheet piling

© 2022 Moussaoui Moufida et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Identification of Soil Mechanical Parameters by Inverse Analysis Using Stochastic Methods

Publicado en línea: 14 ene 2023

Páginas: 1 - 12

DOI: https://doi.org/10.2478/sspjce-2022-0018

Palabras claveinverse analysis, identification of parameters, stochastic methods, numerical modeling, sheet piling

© 2022 Moussaoui Moufida et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
inverse analysis, identification of parameters, stochastic methods, numerical modeling, sheet piling