Abstract

Today, the back analysis methods are known as reliable and effective approaches for estimating the soil strength parameters in the site of project. The back analysis can be performed by genetic algorithm and particle swarm optimization in the form of an optimization process. In this paper, the back analysis is carried out using genetic algorithm and particle swarm optimization in order to determine the soil strength parameters in an excavation project in Tehran city. The process is automatically accomplished by linking between MATLAB and Abaqus software using Python programming language. To assess the results of numerical method, this method is initially compared with the results of numerical studies by Babu and Singh. After the verification of numerical results, the values of the three parameters of elastic modulus, cohesion and friction angle (parameters of the Mohr–Coulomb model) of the soil are determined and optimized for three soil layers of the project site using genetic algorithm and particle swarm optimization. The results optimized by genetic algorithm and particle swarm optimization show a decrease of 72.1% and 62.4% in displacement differences in the results of project monitoring and numerical analysis, respectively. This research shows the better performance of genetic algorithm than particle swarm optimization in minimization of error and faster success in achieving termination conditions.

Highlights

  • Excavation operations increase the probability of occurring problems like collapse of the buildings, large deformations at the ground level and especially unpredicted damages

  • After the back analysis using GA and particle swarm optimization (PSO) under similar conditions, the genetic algorithm satisfies the convergence criterion and reduces the error to less than 5% by 254 iterations of finite element analysis and checking objective function

  • The PSO algorithm cannot reduce the amount of error function to less than 5% by 254 iterations, unlike GA; the back analysis should continue by PSO algorithm or the ranges must be more limited

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Summary

Introduction

Excavation operations increase the probability of occurring problems like collapse of the buildings, large deformations at the ground level and especially unpredicted damages. This importance is due to the concerns related to ground movements around deep excavations. For determination of the soil strength parameters one could take advantage of various the back analysis methods. The back analysis method was first used by Peck in 1980 to estimate the soil parameters based on project monitoring. Strength parameters of the soil can be determined by minimizing the difference. The direct method is based on optimization and the indirect method is based on mathematical formulation [2]

Direct and Indirect Displacement Back Analysis
Encoding Individuals and Populations
Generating an Initial Population
Updating the Position and Velocity of All Particles
Error Function
Convergence Criterion
Implementation of Genetic Algorithm and Particle Swarm Optimization in MATLAB
Verification
Introduction of Case Study
Analysis of Case Study and Implementation of Python Programming Language
10. Effective Parameters of Genetic Algorithm and Particle Swarm Optimization
11. Feasibility of Back Analysis
13. Back Analysis Results
14. Conclusion
16. References
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