Compressive strength (CS) is the maximum resistance of concrete against axial
compressive loading in standard conditions. Estimation of this parameter is essential for the …

Recently developed Machine Learning (ML) interpretability techniques have the potential to
explain how predictors influence the dependent variable in high-dimensional and non-linear …

To comprehend the empirical seismic vulnerability capability of various typical structures in
towns and suburbs, this study considers, as the research background, the structural damage …

Scour of pile group foundations is a common hazard for cross-river bridges and can produce
considerable damage to piles in earthquake-prone regions. The fragility-based performance …

This paper is devoted to the research on applying the deep learning method to nonlinear
structural post-disaster damage state assessment. Transformer and Informer networks with a …

Determining the risk priorities for the building stock in highly seismic-prone regions and
making the final decisions about the buildings is one of the essential precautionary …

Predicting the post-earthquake residual displacement of isolated bridges is crucial in
deciding their operational and recovery strategies after a seismic event. This study proposes …

In this paper, we develop a novel ensemble model for seismic building damage prediction
that leverages machine learning algorithms of two completely different mechanisms, tree …

Purpose Slope stability analysis is essential for ensuring the safe design of road
embankments. While various conventional methods, such as the finite element approach …

This study demonstrates the development of bivariate fragility curves for a fire-exposed
simple-span overpass bridge prototype with composite steel plate girders. The fire and …