ArticleScientific reports2023
Assessment of small strain modulus in soil using advanced computational models.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
Funding
Abstract
Small-strain shear modulus ([Formula: see text]) of soils is a crucial dynamic parameter that significantly impacts seismic site response analysis and foundation design. [Formula: see text] is susceptible to multiple factors, including soil uniformity coefficient ([Formula: see text]), void ratio (e), mean particle size ([Formula: see text]), and confining stress ([Formula: see text]). This study aims to establish a [Formula: see text] database and suggests three advanced computational models for [Formula: see text] prediction. Nine performance indicators, including four new indices, are employed to calculate and analyze the model's performance. The XGBoost model outperforms the other two models, with all three models achieving [Formula: see text] values exceeding 0.9, RMSE values below 30, MAE values below 25, VAF values surpassing 80%, and ARE values below 50%. Compared to the empirical formula-based traditional prediction models, the model proposed in this study exhibits better performance in IOS, IOA, a20-index, and PI metrics values. The model has higher prediction accuracy and better generalization ability.
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Registered trials
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