Prediction of loess collapsibility coefficient using bayesian optimized random forest model

Prediction of loess collapsibility coefficient using bayesian optimized random forest model

Abstract Accurately predicting the collapsibility coefficient of loess is crucial for mitigating the hazards associated with loess collapsibility in engineering projects, natural environment, and socio-economic activities. The traditional method for determining the collapsibility coefficient is time...

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Bibliographic Details
Main Authors: Wan Zhang, Jiangtao Guo, Zhaopeng Li, Ruifang Cheng, Cuiping Ning, Hongfeng Niu, Ze Liu
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Loess
Collapsibility coefficient
Bayesian optimization
Random forest
Predicition
Online Access:https://doi.org/10.1038/s41598-025-11121-8
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https://doi.org/10.1038/s41598-025-11121-8

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