Multi-objective optimization-based updating of predictions during excavation

Zhou

et al. 2019

Summary Current studies have focused on selecting constitutive models using optimization methods or selecting simple formulas or models using Bayesian methods. In contrast, this paper deals with the challenge to propose an effective Bayesian‐based selection method for advanced soil models accounting for the soil uncertainty. Four representative critical state‐based advanced sand models are chosen as database of constitutive model. Triaxial tests on Hostun sand are selected as training and testing data. The Bayesian method is enhanced based on transitional Markov chain Monte Carlo method, whereby the generalization ability for each model is simultaneously evaluated, for the model selection. The most plausible/suitable model in terms of predictive ability, generalization ability, and model complexity is selected using training data. The performance of the method is then validated by testing data. Finally, a series of drained triaxial tests on Karlsruhe sand is used for further evaluating the performance.

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian model selection for sand with generalization ability evaluation

Zhou

et al. 2019

“…Benefitted from the sustained development of computing power, numerical simulations have become standard methods in geomechanics and its related fields. Among those numerical methods, the finite element method (FEM) features prominently in engineering practices 1,3,[7][8][9][10][11][12] . For FEM, however, excessive deformation of a mesh can result in numerical inaccuracies, even to the point of making calculation impossible for large deformation problems.…”

Section: Introductionmentioning

confidence: 99%

An edge‐based strain smoothing particle finite element method for large deformation problems in geotechnical engineering

Yuan

et al. 2020

Summary To solve large deformation geotechnical problems, a novel strain‐smoothed particle finite element method (SPFEM) is proposed that incorporates a simple and effective edge‐based strain smoothing method within the framework of original PFEM. Compared with the original PFEM, the proposed novel SPFEM can solve the volumetric locking problem like previously developed node‐based smoothed PFEM when lower‐order triangular element is used. Compared with the node‐based smoothed PFEM known as “overly soft” or underestimation property, the proposed SPFEM offers super‐convergent and very accurate solutions due to the implementation of edge‐based strain smoothing method. To guarantee the computational stability, the proposed SPFEM uses an explicit time integration scheme and adopts an adaptive updating time step. Performance of the proposed SPFEM for geotechnical problems is first examined by four benchmark numerical examples: (a) bar vibrations, (b) large settlement of strip footing, (c) collapse of aluminium bars column, and (d) failure of a homogeneous soil slope. Finally, the progressive failure of slope of sensitive clay is simulated using the proposed SPFEM to show its outstanding performance in solving large deformation geotechnical problems. All results demonstrate that the novel SPFEM is a powerful and easily extensible numerical method for analysing large deformation problems in geotechnical engineering.

“…Yin et al distinguished three approaches: analytical methods, empirical correlations, and optimisation methods to determine soil parameters based on experimental data. Among these techniques, the inverse analysis by optimisation has been successfully used in the geotechnical area because it produces a relatively objective determination of the parameters for an adopted soil model, even for those that have no direct physical meaning. The existing optimisation techniques can be divided into two categories: (a) deterministic optimisation techniques; and (b) stochastic optimisation techniques.…”

Section: Introductionmentioning

confidence: 99%

Enhancement of backtracking search algorithm for identifying soil parameters

2020

In this paper, an enhanced backtracking search algorithm (so-called MBSA-LS) for parameter identification is proposed with two modifications: (a) modifying the mutation of original backtracking search algorithm (BSA)considering the contribution of current best individual for accelerating convergence speed and (b) novelly incorporating an efficient differential evolution (DE) as local search for improving the quality of population. The proposed MBSA-LS is first validated with better performance than the original BSA and some other typical state-of-the-art optimization algorithms on a benchmark of soil parameter identification in terms of effectiveness, efficiency, and robustness. Then, the efficiency of the MBSA-LS is further illustrated by two representative cases: identifying soil parameters from both laboratory tests and field measurements. All comparisons demonstrate that the proposed MBSA-LS algorithm can give accurate results in a short time. Finally, to conveniently solve the problems of parameter identification, a practical tool ErosOpt for parameter identification is developed by integrating the proposed MBSA-LS and some other efficient algorithms for readers to conduct the parameter identification using optimisation algorithms. K E Y W O R D Sconstitutive model, graphical user interface, local search, optimisation, parameter identification, pressuremeter