Effective post‐earthquake response requires a prompt and accurate assessment of
earthquake‐induced damage. However, existing damage assessment methods cannot …

Artificial neural networks have been used to predict nonlinear structural time histories under
seismic excitation because they have a significantly lower computational cost than the …

Every year, earthquakes result in severe economic losses and a significant number of
casualties worldwide. In limiting the losses that occur after these extreme events, timely and …

Early and accurate damage evaluation after earthquakes is critical for planning an efficient
and timely emergency response. State-of-the-art rapid evaluation techniques of structural …

This paper presents a comprehensive study on developing advanced deep learning
approaches for nonlinear structural response modeling and prediction. Two schemes of the …

Accurate prediction of bridge responses plays an essential role in health monitoring and
safety assessment of bridges subjected to dynamic loads such as earthquakes. To this end …

After earthquakes, an accurate and efficient seismic-damage prediction is indispensable for
emergency response. Existing methods face the dilemma between accuracy and efficiency …

As urban systems become more highly sophisticated and interdependent, their vulnerability
to earthquake events exhibits a significant level of uncertainties. Thus, community‐level …

A rapid assessment of the seismic damage to buildings can facilitate improved emergency
response and timely relief in earthquake-prone areas. In this study, an automated building …

This paper proposes a novel damage detection and localization method of civil structures
using a windowed Long Short-Term Memory (LSTM) network. A sequence of windowed …