Optimization techniques for identifying soil parameters in geotechnical engineering: Comparative study and enhancement

TLDR

The study reviews and classifies optimization techniques for soil parameter identification in geotechnical engineering, presenting a comparative framework across three categories of methods. The authors aim to enhance identification performance by integrating the Nelder–Mead simplex into a differential evolution algorithm to accelerate convergence while maintaining robust search. They employ an identification methodology comprising an error function, search strategy, and procedure, comparing five common optimizers—GA, PSO, SA, DE, ABC—on synthetic pressuremeter and excavation data, and then augmenting DE with Nelder–Mead for the enhanced algorithm. Results show DE offers the strongest search ability but slowest convergence, all methods achieve small objective errors yet deviate from preset parameters, and the enhanced algorithm successfully identifies Mohr–Coulomb and ANICREEP parameters from synthetic and real pressuremeter tests.

Abstract

Summary A comparative study of optimization techniques for identifying soil parameters in geotechnical engineering was first presented. The identification methodology with its 3 main parts, error function, search strategy, and identification procedure, was introduced and summarized. Then, current optimization methods were reviewed and classified into 3 categories with an introduction to their basic principles and applications in geotechnical engineering. A comparative study on the identification of model parameters from a synthetic pressuremeter and an excavation tests was then performed by using 5 among the mostly common optimization methods, including genetic algorithms, particle swarm optimization, simulated annealing, the differential evolution algorithm and the artificial bee colony algorithm. The results demonstrated that the differential evolution had the strongest search ability but the slowest convergence speed. All the selected methods could reach approximate solutions with very small objective errors, but these solutions were different from the preset parameters. To improve the identification performance, an enhanced algorithm was developed by implementing the Nelder‐Mead simplex method in a differential algorithm to accelerate the convergence speed with strong reliable search ability. The performance of the enhanced optimization algorithm was finally highlighted by identifying the Mohr‐Coulomb parameters from the 2 same synthetic cases and from 2 real pressuremeter tests in sand, and ANICREEP parameters from 2 real pressuremeter tests in soft clay.