This article presents a state-of-the-art review of the applications of Artificial Intelligence (AI),
Machine Learning (ML), and Deep Learning (DL) in building and construction industry 4.0 in …

Structural health monitoring at local and global levels using computer vision technologies
has gained much attention in the structural health monitoring community in research and …

Abstract Machine learning models have been shown to be useful for predicting and
assessing structural performance, identifying structural condition and informing preemptive …

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 …

Machine learning (ML) has evolved rapidly over recent years with the promise to
substantially alter and enhance the role of data science in a variety of disciplines. Compared …

Computer vision techniques, in conjunction with acquisition through remote cameras and
unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure …

Computer vision‐based techniques were developed to overcome the limitations of visual
inspection by trained human resources and to detect structural damage in images remotely …

This paper compares the performance of common edge detectors and deep convolutional
neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of …

The detection of tunnel surface defects is the very important part to ensure tunnel safety.
Traditional tunnel detection mainly relies on naked-eye inspection, which is time-consuming …

Deep learning‐based structural damage detection methods overcome the limitation of
inferior adaptability caused by extensively varying real‐world situations (eg, lighting and …