A Modified Empirical Model for Predicting Liquefaction in Partially Saturated Sands

Geotechnical and Geological Engineering, Год журнала: 2025, Номер 43(5)

Опубликована: Апрель 21, 2025

Язык: Английский

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Soft voting ensemble classifier for liquefaction prediction based on SPT data

Artificial Intelligence Review, Год журнала: 2025, Номер 58(8)

Опубликована: Май 3, 2025

Язык: Английский

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Experimental and Machine Learning-Based Analysis of Oxide Ratios in Alumina-Silicate Geopolymer Formation

Iranian Journal of Science and Technology Transactions of Civil Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 8, 2025

Язык: Английский

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Prediction and Factor Analysis of Liquefaction Ground Subsidence Based on Machine-Learning Techniques

Applied Sciences, Год журнала: 2024, Номер 14(7), С. 2713 - 2713

Опубликована: Март 23, 2024

Liquefaction is a significant challenge in the fields of earthquake risk assessment and soil dynamics, as it has potential to cause extensive damage buildings infrastructure through ground failure. During 2011 Great East Japan Earthquake, Urayasu City Chiba Prefecture experienced severe liquefaction, leading evacuation losses due effect liquefaction on roads. Therefore, developing quantitative predictions subsidence caused by understanding its contributing factors are imperative preparing for future mega-earthquakes. This research novel because previous primarily focused predictive models determining presence or absence there few examples available magnitude after occurred. study extracts features from existing datasets builds model, supplemented factor analysis. Using Cabinet Office Japan’s Nankai Trough Megathrust Earthquake liquefaction-induced was designated dependent variable. A gradient-boosted decision-tree (GDBT) prediction model then developed. Additionally, Shapley additive explanations (SHAP) method employed analyze contribution each feature results. The found that XGBoost outperformed LightGBM terms accuracy, with predicted values closely aligned actual measurements, thereby proving effectiveness predicting liquefaction. Furthermore, demonstrated assessments, which were previously challenging, can now be interpreted using SHAP factors. enables accountable wide-area subsidence.

Язык: Английский

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Modeling Static Liquefaction Susceptibility of Saturated Clayey Sand using Advanced Machine-Learning techniques

Transportation Infrastructure Geotechnology, Год журнала: 2024, Номер 11(5), С. 2903 - 2931

Опубликована: Апрель 27, 2024

Язык: Английский

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