Surrogate models based on sparse estimation for geotechnical reliability analysis

Research output: Contribution to conferencePaper

Abstract

This paper presents a method for building surrogate models for geotechnical reliability analysis based on sparse estimation. Sparse estimation, which is called least absolute shrinkage statistical operator (lasso) in statistics, has the property that some of the parameters in surrogate models are driven to zero and leads to simpler models. Building surrogate models can be divided into two processes, model selection and parameter estimation, and the sparse estimation enables to achieve these two processes at the same time. A surrogate model was designed to estimate consolidation settlement value of a specific time based on sparse estimation, and its applicability has been investigated by comparing the results by the surrogate model with those by finite element analysis.

Original languageEnglish
Publication statusPublished - 2020
Event16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019 - Taipei, Taiwan, Province of China
Duration: Oct 14 2019Oct 18 2019

Conference

Conference16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019
CountryTaiwan, Province of China
CityTaipei
Period10/14/1910/18/19

Keywords

  • Consolidation settlement
  • Lasso
  • Reliability analysis
  • Surrogate models

ASJC Scopus subject areas

  • Geotechnical Engineering and Engineering Geology

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