Data-driven prediction models for curved thin-walled box-girder bridge modal analysis: transitioning from linear regression to Gaussian processes

A surrogate modeling approach for rapid moving-load prediction of steel box girder bridges within a unified COMSOL framework

A unified surrogate modeling framework to predict the structural responses of a steel box girder bridge under moving-load analysis is developed, ensuring consistency in both data generation and model assessment.

Seismic Response Prediction of a 2D Single-Story Steel Frame Using Physics-Guided Support Vector Regression Ensemble

Predicting the nonlinear seismic response of structures that have entered the plastic range under strong ground motions is severely constrained by data scarcity and computational cost. In this article, to address this dual challenge, we propose a physics-guided ensemble model based on Support Vector Regression. A finit...

Coupled dynamic interaction analysis of box-girder bridge and high-speed train using an artificial neural network framework

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

Prediction of punching shear strength of RC flat plates using a refined machine learning model and a closed-form equation

Reinforced concrete (RC) flat plate column-slab connections are highly vulnerable to brittle punching shear failure, which can trigger the progressive collapse of the entire structure. Accurate prediction of the punching shear strength is crucial for ensuring structural safety. However, this prediction remains challeng...

Buckling prediction of stainless steel circular hollow sections beam–columns using finite element analysis and machine learning

A data-driven approach to predict the buckling resistance of stainless steel circular hollow section beam–columns by combining finite element analysis and machine learning is presented, supporting the value of machine-learning-based predictions.