Advancing safety assessment of granular pile supported embankments: a comparative study of state-of-the-art machine learning predictive models

Bayesian-optimised machine learning for predicting aggressive environment resistance and service life of recycled aggregate geopolymer concrete

Developing reliable computational tools for durability and service-life assessment of concrete structures in aggressive environments is essential for advancing predictive modeling in structural engineering. This study introduces machine learning (ML)–based models for forecasting the sulfate and acid resistance of recyc...

Geotechnical Evaluation of Gradient-Based Neural Networks for Factor of Safety Prediction in Homogeneous Soil Slopes Under Hydraulic Variability

Slope stability assessment remains a fundamental challenge in geotechnical engineering because of the complex nonlinear interactions among soil properties, slope geometry, and hydraulic conditions, particularly variations in pore-water pressure. This study investigates the reliability of Artificial Neural Network–Multi...

Machine Learning Framework for Reliability-Based Design of Screw Piles

Probabilistic and Interpretable Machine Learning Framework for Predicting Pile Unit Base Resistance in Soft Soil

Accurate prediction of pile base resistance is essential for the safe and economical design of deep foundations, particularly in soft soils where load-transfer mechanisms are highly nonlinear and uncertain. This study develops a comparative, probabilistic, and interpretable machine learning framework for predicting pil...

Applications of Machine Learning Models to Predict the Uniaxial Compressive Strength of Sandstone from Muzaffarabad - A Case Study

Machine Learning Based on Hybrid Optimization Algorithm for the Prediction and Risk Assessment of Rock Tunnel Lining Displacement

Accurate prediction of tunnel vault displacement and reliable assessment of deformation risk are essential for tunnel safety management under complex geological conditions. This study develops an integrated data-driven framework combining machine-learning prediction, metaheuristic hyperparameter optimization, statistic...