Scholarlink

<p>The quantitative modeling of the dependency of shear modulus on shear strain is a central aspect of the characterization of cyclic and dynamic properties of soils in the nonlinear elastic domain for earthquake geotechnical engineering applications such as site response analysis. This study investigates the dependency of normalized shear modulus on shear strain for clayey soils using the UNIFI database, which contains results from laboratory tests performed on soils from Italian geotechnical sites. A hybrid intelligent metamodeling framework is proposed within the Group Method of Data Handling (GMDH) paradigm. The approach combines the COMBI algorithm for systematic identification of candidate model structures with the shuffled complex evolution (SCE) algorithm for global optimization of model parameters, resulting in a hybrid COMBI-SCE modeling framework. The modeling procedure includes dependency analysis for the identification of influential predictors, generation of candidate model structures through COMBI, and parameter optimization using the SCE algorithm Query ID="Q3" Text="Please check the edit made in the article title. The predictive capability of the resulting models was evaluated using independent training and testing datasets and multiple statistical goodness-of-fit indicators. The results indicate that the most influential predictors of normalized shear modulus degradation in the analyzed dataset include the overconsolidation ratio, plasticity index, initial void ratio, mean confining stress, and shear strain amplitude. The resulting COMBI-SCE metamodel provides a compact analytical representation capable of capturing the nonlinear relationship between shear strain and normalized shear modulus while maintaining physical interpretability. The study demonstrates the potential of hybrid intelligent modeling approaches for developing predictive relationships for dynamic soil behavior and highlights the importance of incorporating site-specific data when developing data-driven geotechnical models.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Metamodeling-based geotechnical characterization of the shear strain dependency of shear modulus of clayey soils from an Italian database

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

查看全文
  • Mojtaba Masoumi Shahrbabak,
  • Marco Uzielli,
  • Johann Facciorusso,
  • Claudia Madiai

摘要

The quantitative modeling of the dependency of shear modulus on shear strain is a central aspect of the characterization of cyclic and dynamic properties of soils in the nonlinear elastic domain for earthquake geotechnical engineering applications such as site response analysis. This study investigates the dependency of normalized shear modulus on shear strain for clayey soils using the UNIFI database, which contains results from laboratory tests performed on soils from Italian geotechnical sites. A hybrid intelligent metamodeling framework is proposed within the Group Method of Data Handling (GMDH) paradigm. The approach combines the COMBI algorithm for systematic identification of candidate model structures with the shuffled complex evolution (SCE) algorithm for global optimization of model parameters, resulting in a hybrid COMBI-SCE modeling framework. The modeling procedure includes dependency analysis for the identification of influential predictors, generation of candidate model structures through COMBI, and parameter optimization using the SCE algorithm Query ID="Q3" Text="Please check the edit made in the article title. The predictive capability of the resulting models was evaluated using independent training and testing datasets and multiple statistical goodness-of-fit indicators. The results indicate that the most influential predictors of normalized shear modulus degradation in the analyzed dataset include the overconsolidation ratio, plasticity index, initial void ratio, mean confining stress, and shear strain amplitude. The resulting COMBI-SCE metamodel provides a compact analytical representation capable of capturing the nonlinear relationship between shear strain and normalized shear modulus while maintaining physical interpretability. The study demonstrates the potential of hybrid intelligent modeling approaches for developing predictive relationships for dynamic soil behavior and highlights the importance of incorporating site-specific data when developing data-driven geotechnical models.