☆ 4.7 Article

Identification of constitutive parameters of soil using an optimization strategy and statistical analysis

COMPUTERS AND GEOTECHNICS (2013)

期刊

COMPUTERS AND GEOTECHNICS

卷 49, 期 -, 页码 143-157

出版社

ELSEVIER SCI LTD

DOI: 10.1016/j.compgeo.2012.10.002

关键词

Constitutive soil models; Clay; Parameter identification; Particle swarm optimization; Sensitivity analysis; Assessing model fit

类别

Computer Science, Interdisciplinary Applications Engineering, Geological Geosciences, Multidisciplinary

资金

Bulgarian National Science Fund [DSAB 02/6]

向作者/读者索取更多资源

Protocol

Reagent

摘要

The paper at hand investigates a strategy to calibrate different constitutive models for soils via back analysis. The efficiency and reliability of the parameter identification for soil models is worked out. In order to demonstrate of how to utilise identification procedures, results from optimisation against conventional oedometer and drained triaxial compression tests on natural Pappadai clay are presented and discussed. The aim of geotechnical optimisation problems is to obtain a set of model parameter values that provide the best match between soil model simulations and appropriate measurements. For the parameter identification process, a constrained population-based algorithm is chosen, namely the Particle Swarm Optimiser. The identification is carried out in an initial step separately on each test and then simultaneously on oedometer and triaxial tests. The evaluation is performed employing three different constitutive models of varying complexity and number of constitutive parameters. A subsequent residual analysis and the computation of confidence intervals for the parameters provide valuable results to assess the quality of the identified parameters in correlation with the evaluated data. Therefore, criteria of the utility and reliability of the mathematical models for further prognosis computations can be estimated. (C) 2012 Elsevier Ltd. All rights reserved.

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Predicting peak shear strength of rock fractures using tree-based models and convolutional neural network

Jinfan Chen, Zhihong Zhao, Jintong Zhang

Summary: This study develops data-driven criteria to estimate the peak shear strength (PSS) of rock fractures, considering the effects of surface roughness features. A high-quality dataset is created using particle-based discrete element method and diamond-square algorithm. Tree-based models and convolutional neural network are trained to predict the PSS of rock fractures, and their reliability is verified using experimental data.

COMPUTERS AND GEOTECHNICS (2024)

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