Parameter identification for elasto-plastic modelling of unsaturated soils from pressuremeter tests by parallel modified particle swarm optimization

Zhang, Youliang; Gallipoli, Domenico; Augarde, Charles E.

doi:10.1016/j.compgeo.2012.08.004

Cited by 42 publications

(24 citation statements)

References 27 publications

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“…Therefore, the procedure should be presented before conducting the optimization. Calvello and Finno gave a 3‐step procedure for a general identification of soil parameters; Zentar and Hicher presented a simplified procedure to combine the finite element code CESAR‐LCPC and the SiDoLo optimization tool to identify the modified Cam‐clay parameters from PMTs; Finno and Calvello presented a relatively complex procedure to combine the computer code UCODE and the finite element code PLAXIS for identifying the hardening soil model parameters from excavation tests; Obrzud et al presented a procedure using a 2‐level neural network tool to conduct the parameter identification; Zhang et al presented a procedure involving the finite element code MUSEFEM and the particle swarm optimization (PSO) for identifying the parameters of an unsaturated soil model from PMTs; Zhao et al presented an optimization procedure involving a differential evolution (DE) algorithm and the finite element code ABAQUS for identifying the modified Cam‐clay parameters from an excavation test.…”

Section: Methodology Of Identificationmentioning

confidence: 99%

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

Yin

Jin

Shen

et al. 2017

Num Anal Meth Geomechanics

235

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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.

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Section: Methodology Of Identificationmentioning

confidence: 99%

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

Yin

Jin

Shen

et al. 2017

Num Anal Meth Geomechanics

235

View full text Add to dashboard Cite

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“…Its main feature is its socio-psychological characteristics derived from swarm intelligence and offers comprehension of social behavior and thus it is governed by two drivers and these are group and individual knowledge. Particle swarm optimization method has been applied successfully to predict foreign exchange rates (Sermpinis et al, 2013), design bacterial foraging in power system stabilizers (Abd-Elazim and Ali, 2013), model elasto-plastic of unsaturated soils (Zhang et al, 2013a), plan robot paths (Zhang et al, 2013b) and design electronic enclosure (Scriven et al, 2013). Particle Swarm Optimization is used to model a correlation model.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Introduction to Artificial Intelligence based Decision Making

Marwala¹

2015

Causality, Correlation and Artificial Intelligence for Rational Decision Making

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In this chapter, decision making which is based on causality, correlation and artificial intelligence is introduced. The concept of correlation is introduced and is applied to build a correlation function. Furthermore, it relates causality to correlation and uses it to build a causal function. Artificial intelligence methods are introduced and these include neural networks, particle swarm optimization, genetic algorithm and simulated annealing. It describes decision making and outlines the rest of the book. 1 Causality, Correlation and Artificial Intelligence for Rational Decision Making Downloaded from www.worldscientific.com by KAINAN UNIVERSITY on 02/04/15. For personal use only.

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“…The genetic algorithm (GA) or methods that combine the GA with other optimization algorithms are frequently used for parameter identification . However, although a comprehensive field monitoring system has been implemented and various types of field measurements have been obtained, most of the inverse analysis methods utilize only 1 type of field observation to back‐calculate the physical‐mechanical parameters . Only 1 type of field measurement is not sufficient to consider the important characteristics of the field performance of a deep excavation .…”

Section: Introductionmentioning

confidence: 99%

“…[23][24][25][26][27][28] However, although a comprehensive field monitoring system has been implemented and various types of field measurements have been obtained, most of the inverse analysis methods utilize only 1 type of field observation to back-calculate the physical-mechanical parameters. [29][30][31] Only 1 type of field measurement is not sufficient to consider the important characteristics of the field performance of a deep excavation. 32 Therefore, this treatment may cause the results of inverse analysis to be inaccurate.…”

Section: Introductionmentioning

confidence: 99%

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Jiang

Sun

Yi³

et al. 2018

Num Anal Meth Geomechanics

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Summary An inverse analysis method that combines the back propagation neural network (BPNN) and vector evaluated genetic algorithm (VEGA) was proposed to identify mechanical geomaterial parameters for a more accurate prediction of deformation. The BPNN is used to replace the time‐consuming numerical calculations, thus enhancing the efficiency of the inverse analysis. The VEGA is used to find the Pareto‐optimal solutions to multiobjective functions. Unlike traditional back‐analysis methods which are based on only 1 type of field measurement and a single objective function, this proposed method can consider multiple field observations simultaneously. The proposed method was applied to the Shapingba foundation pit excavation located in Chongqing city, China. Two types of measurements are considered in the method simultaneously: the displacements in the x‐direction (north orientation) and those in the y‐direction (east orientation). Five deformation modulus parameters for artificial backfill soil, silty clay, siltstone, sandstone, and mudstone were selected as the inversion parameters. Compared with the weighted sum approach, the proposed method was demonstrated as an efficient multi‐objective optimization tool for back calculating undetermined parameters. After performing a forward‐calculation using the optimized parameters obtained by the inverse analysis, the predicted results were well consistent with the practical deformation in magnitude and trend.

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Parameter identification for elasto-plastic modelling of unsaturated soils from pressuremeter tests by parallel modified particle swarm optimization

Cited by 42 publications

References 27 publications

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

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

Introduction to Artificial Intelligence based Decision Making

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

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