专题：Dam Engineering and Safety

This cluster of papers focuses on the statistics, mechanisms, and modelling of embankment dam failures, including topics such as internal erosion, piping phenomena, seismic analysis, suffusion characteristics in granular soils, and the deformation of concrete dams. It also explores hydraulic gradients and breaching parameters.
最新文献
Geotechnical evaluation of embankment stability in seismic zones using Monte Carlo and subset simulations within an LRFD framework aided by machine learning

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Prediction of the Wetting-Induced Compression of Collapsible Soils Using Ensemble Machine Learning

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Numerical implication of water–soil leakage induced by longitudinal tunnel segment dislocation opening in saturated strata

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Brittle fracture modelling in layered rocks using an adaptive phase-field modelling with a combined acceleration scheme

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Hydraulic Performance Modeling of Inclined Double Cutoff Walls Beneath Hydraulic Structures Using Optimized Ensemble Machine Learning

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Quantifying the influence of soil-rock interfaces on water infiltration rate in karst landscapes

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Research on prediction method of rock uniaxial compressive strength based on interpretable INFO-Stacking model

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Physics-informed machine learning approach for the prediction of critical column and explosive demolition planning of frame structure

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三峡库区万州区滑坡易发性演化规律

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Time-frequency analysis on seismic response and pre-toppling damage evolution of anti-dip rock slope under earthquake sequences by shaking table tests

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近5年高被引文献
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

article Full Text OpenAlex 499 FWCI51.275

Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls

article Full Text OpenAlex 328 FWCI29.544

Desiccation cracking of soils: A review of investigation approaches, underlying mechanisms, and influencing factors

review Full Text OpenAlex 280 FWCI7.499

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

article Full Text OpenAlex 265 FWCI30.68

Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)

article Full Text OpenAlex 221 FWCI58.438

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP

article Full Text OpenAlex 218 FWCI21.609

Slope stability prediction using ensemble learning techniques: A case study in Yunyang County, Chongqing, China

article Full Text OpenAlex 197 FWCI53.052

Machine learning and landslide studies: recent advances and applications

article Full Text OpenAlex 174 FWCI46.05

AI-powered landslide susceptibility assessment in Hong Kong

article Full Text OpenAlex 169 FWCI40.599

A comparative study of different machine learning methods for reservoir landslide displacement prediction

article Full Text OpenAlex 169 FWCI44.704