As civil structures age and deteriorate, it becomes crucial to conduct structural health
monitoring (SHM) to ensure safety and timely maintenance. Surface defect detection plays a …

Urban underground infrastructure is a critical component in cities to provide essential
services to residents. Research efforts have been made to facilitate different activities of …

Convolutional neural networks (CNNs) have gained growing interest in recent years for their
advantages in detecting cracks on concrete bridge components. Class imbalance is a …

Zero‐shot learning, applied with vision‐language pretrained (VLP) models, is expected to
be an alternative to existing deep learning models for defect detection, under insufficient …

Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …

Safety assessment of post-event damaged structures is vital and significant because it
directly affects life security, structural repair, and economic loss, especially in earthquakes …

Pavement crack detection methods based on deep learning and computer vision can greatly
improve detection efficiency and accuracy, but in many cases the data in training set is …

Cracks are an essential indicator of infrastructure degradation, and achieving high-
precision, pixel-level crack segmentation is a common goal for artificial intelligence (AI) …

Fast and accurate crack segmentation plays an important role in the predictive maintenance
of constructed facilities and civil infrastructures. However, it is worth noting that current deep …

In recent years, the application of structural control strategies to attenuate the dynamic
response of civil engineering structures subjected to human-induced and environmental …