Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to
recent technological advancements in sensors, as well as high-speed internet and cloud …

In the past two decades, structural health monitoring (SHM) systems have been widely
installed on various civil infrastructures for the tracking of the state of their structural health …

In recent years, the construction industry has experienced an expansion in the multitude of
projects and emergent information. With the advent of deep learning, new opportunities …

Image-based analysis techniques offer a robust way to solve engineering problems due to
the availability of visual data (eg, surveillance cameras). Hence, research efforts have …

This review identifies current machine-learning algorithms implemented in building
structural health monitoring systems and their success in determining the level of damage in …

Earthquake damage investigation is critical to post‐earthquake structural recovery and
reconstruction. In this study, a method of assessing the component failure mode and …

Due to the increasing demand on road maintenance around the whole world, advanced
techniques have been developed to automatically detect and segment pavement cracks …

Recent advances in learning systems and sensor technology have enabled powerful
strategies for autonomous data-driven damage detection in structural systems. This work …

Structural health monitoring research traditionally focuses on detecting damage in members
excluding the possibility of weakened joint conditions. Efficient model-based joint damage …