Machine Learning–Based Predictive Modeling of Time-Dependent Deformation in Braced Excavations in Soft Alluvial Clay

Prediction and Generalization Capability of Machine Learning Models for Shield TBM-Induced Settlement

Ground settlement induced by shield tunnel boring machine (TBM) excavation is a major geotechnical concern in urban tunneling because it may affect the safety of adjacent structures and underground infrastructure. In this study, machine learning models were developed to predict the maximum settlement induced by shield...

Bayesian-Optimized Ensemble Machine Learning for Predicting Settlement of Cohesionless Soil Under Loads

Integrating Finite Element Analysis and Machine Learning to Predict the Bearing Capacity of Strip Footings on Slopes

The results confirm the accuracy, interpretability, and computational efficiency of the integrated FEA-ML approach as an alternative to traditional bearing capacity analysis.

Tree Based Machine Learning Framework for Stability Evaluation of Square Tunnels in Sloping Rock

The proposed AFELA–machine learning framework provides a computationally efficient, reliable, and interpretable approach for tunnel stability assessment in sloping rock mass conditions.

Data-Driven Mechanical ROP Prediction: Construction and Validation of Committee Machine Model

With the continuous development of drilling technology, accurately predicting mechanical penetration rates is particularly important for improving operational efficiency and reducing costs. Existing methods often struggle to provide reliable predictions when faced with complex geological conditions and variable drillin...

Machine Learning Prediction of the Ground Reaction Curve in Sand with the MATLAB GUI Platform

The proposed framework combined a curated database, neural network-based curve prediction, and hyperparameter optimization, providing a robust approach for evaluating the soil arching effect, providing a robust approach for evaluating the soil arching effect.