Predictive Artificial Intelligence Modelling Integrating Transient Seepage Analysis with Machine Learning for Slope Stability Early Warning Systems

Hybrid FEM and machine learning framework for probabilistic seepage failure prediction in embankment dams under drawdown conditions

Conventional embankment dam safety assessments under reservoir drawdown conditions are typically based on deterministic finite element and limit equilibrium analyses. That use single representative values of hydraulic and shear strength parameters. Such approaches often neglect the inherent uncertainty and spatial va...

A data-driven framework for accurate surface spectral acceleration prediction with integrated uncertainty control

Leveraging explainable artificial intelligence and ensemble learning as surrogate models for slope stability evaluation

Predicting blast-induced slope stability using a hybrid field-monitoring and machine learning surrogate model

This study evaluates the impact of mine production blasts on ubiquitous jointed slopes using field data and three machine learning algorithms: an ensemble of trees (LSBoost), Gaussian Process Regression (GPR), and Support Vector Machines (SVM). Rather than relying on pure data driven approach, this study develops predi...

Machine Learning Surrogate Modeling for Rapid Prediction Response in Automated Fem Pipelines: A Comparative Study With Uncertainty Quantification

New construction in densely populated urban areas inevitably disturbs existing buildings. Real-time assessment of the mechanical response of disturbed structures is crucial for construction safety, but traditional finite element analysis is difficult to meet real-time monitoring requirements due to its computationally...

Uncertainty-aware slope stability assessment of opencast coal mines using PLAXIS-based numerical modelling and hybrid ANN–XGBoost prediction

ABSTRACT Slope stability is a critical concern in open-pit coal mining, where slope integrity directly impacts operational safety and environmental sustainability. This study develops a hybrid modelling framework integrating PLAXIS-based numerical simulations with Artificial Neural Networks (ANN) and Extreme Gradient B...