Back-Analysis and Parameter Identification for Deep Excavation Based on Pareto Multiobjective Optimization

Huang, Zhenhua; Zhang, L. L.; Cheng, Sijing; Zhang, Jianyong; Xia, Xue

doi:10.1061/(asce)as.1943-5525.0000464

Cited by 24 publications

(19 citation statements)

References 40 publications

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“…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 . Therefore, this treatment may cause the results of inverse analysis to be inaccurate.…”

Section: Introductionmentioning

confidence: 99%

“…[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. Furthermore, if inverse analysis is implemented for each kind of observation separately, the parameters back-calculated by different procedures will not be consistent.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Papon et al [23] presented a multiobjective parameter identification method based on MOGA II and back-analyzed soil parameters using the data from two pressuremeter tests. Huang et al [24] developed a back analysis method based on AMALGAM algorithm and the Pareto multiobjective optimization for deep excavation. To the best knowledge of the authors, parameter estimation of dynamic compaction based on multiple types of field data, especially using the Pareto multiobjective optimization method, is very limited in the literature.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization

Wang

Zhang

Chen

et al. 2015

Shock and Vibration

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This paper presented a parameter estimation method based on a coupled hydromechanical model of dynamic compaction and the Pareto multiobjective optimization technique. The hydromechanical model of dynamic compaction is established in the FEM program LS-DYNA. The multiobjective optimization algorithm, Nondominated Sorted Genetic Algorithm (NSGA-IIa), is integrated with the numerical model to identify soil parameters using multiple sources of field data. A field case study is used to demonstrate the capability of the proposed method. The observed pore water pressure and crater depth at early blow of dynamic compaction are simultaneously used to estimate the soil parameters. Robustness of the back estimated parameters is further illustrated by a forward prediction. Results show that the back-analyzed soil parameters can reasonably predict lateral displacements and give generally acceptable predictions of dynamic compaction for an adjacent location. In addition, for prediction of ground response of the dynamic compaction at continuous blows, the prediction based on the second blow is more accurate than the first blow due to the occurrence of the hardening and strengthening of soil during continuous compaction.

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“…Over the years, research into geotechnical back analysis has focused mainly on optimisation techniques. The performance of optimisation techniques, such as gradient-based techniques, 3 heuristic algorithms, 4,5 surrogate-based optimisation 6,7,8 and multi-objective optimisation, 9,10 has been studied in the context of geotechnical back analysis. A comparative study on the performance of these optimisation algorithms has also been reported.…”

mentioning

confidence: 99%

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

Wang

Goh

Koh

et al. 2020

Num Anal Meth Geomechanics

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Summary Back analysis can provide engineers with important information for better decision‐making. Over the years, research on back analysis has focused mainly on optimisation techniques, while comparative studies of data‐interpretation methodologies have seldom been reported. This paper examines the use of three data‐interpretation methodologies on the performance of geotechnical back analysis. In general, there are two types of approaches for interpreting model predictions using field measurements, deterministic versus population‐based, both of which are considered in this study. The methodologies that are compared are (a) error‐domain model falsification (EDMF), (b) Bayesian model updating and (c) residual minimisation. Back analyses of an excavation case history in Singapore using the three methodologies indicate that each has strengths and limitations. Residual minimisation, though easy to implement, shows limited capabilities of interpreting measurement data with large uncertainty errors. EDMF provides robustness against incomplete information of the correlation structure. This is achieved at the expense of precision, as EDMF yields wider confidence intervals of the identified parameter values and predicted quantities compared with Bayesian model updating. In this regard, a modified EDMF implementation is proposed, which can improve upon the limitations of the traditional EDMF method, thus enhancing the quality of the identification outcomes.

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Back-Analysis and Parameter Identification for Deep Excavation Based on Pareto Multiobjective Optimization

Cited by 24 publications

References 40 publications

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Inverse analysis for geomaterial parameter identification using Pareto multiobjective optimization

Parameter Estimation for Coupled Hydromechanical Simulation of Dynamic Compaction Based on Pareto Multiobjective Optimization

Comparative study of the effects of three data‐interpretation methodologies on the performance of geotechnical back analysis

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