Probabilistic machine learning for predicting desiccation cracks in clayey soils

Jamhiri, Babak; Xu, Yongfu; Shadabfar, Mahdi; Costa, Susanga

doi:10.1007/s10064-023-03366-2

Cited by 9 publications

(1 citation statement)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Probabilistic machine learning has been integrated into predictions of desiccation cracks with uncertain input 13 . Six different machine learning algorithms were analyzed, and it was found that the enhanced XGBT model had greater predictive capacity.…”

Section: Introductionmentioning

confidence: 99%

Assessment of small strain modulus in soil using advanced computational models

Fan,

Hang,

Song

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Small-strain shear modulus ($$G_0$$ G 0 ) of soils is a crucial dynamic parameter that significantly impacts seismic site response analysis and foundation design. $$G_0$$ G 0 is susceptible to multiple factors, including soil uniformity coefficient ($$C_u$$ C u ), void ratio (e), mean particle size ($$d_{50}$$ d 50 ), and confining stress ($$\sigma '$$ σ ′ ). This study aims to establish a $$G_0$$ G 0 database and suggests three advanced computational models for $$G_0$$ G 0 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 $$R^2$$ R 2 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.

show abstract

Section: Introductionmentioning

confidence: 99%