With the informatization of the building and infrastructure industry, conventional analysis
methods are gradually proving inadequate in meeting the demands of the new era, such as …

L-shaped buildings subjected to ground accelerations undergo coupled lateral and torsional
vibrations that can result in critical stress concentrations at their re-entrant corners. Even so …

Probabilistic seismic demand analysis (PSDA) is the most time-and effort-intensive step in
risk-based assessment of the built environment. A typical PSDA requires subjecting the …

Artificial intelligence is gaining increasing popularity in structural analysis. However, at the
structural system level, the appropriateness of data representation, the paucity of data, and …

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 …

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 …

Abstract This paper presents DeepSNA (Deep Structural Nonlinear Analysis), the first
general end-to-end computational framework in civil engineering that can predict the full …

This paper presents a hybrid deep learning methodology for seismic structural monitoring,
damage detection, and localization of instrumented buildings. The proposed methodology …

A data-driven deep neural network (DNN) based approach is presented to accelerate FE 2
analysis. It is computationally expensive to perform multiscale FE 2 analysis since at each …

Parametric inverse analysis/identification provides significant information for structural
damage detection and construction in dam engineering. The main challenge in inverse …