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

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 …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

Masonry structures dominate cultural heritage sites worldwide. Public authorities ought to
preserve and safeguard such structures for future generations. However, precise evaluation …

Previous research has shown the high accuracy of convolutional neural networks (CNNs) in
asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation …

Applying computer science techniques such as artificial intelligence (AI), deep learning (DL),
and computer vision (CV) on digital image data can help monitor and preserve cultural …

Pipelines carrying energy products play vital roles in economic wealth and public safety, but
incidents continue occurring. Condition assessment of pipelines is essential to identify …

The performance of crack segmentation is influenced by complex scenes, including
irregularly shaped cracks, complex image backgrounds, and limitations in acquiring global …

Artificial intelligence (AI) and robotics research and implementation emerged in the
architecture, engineering, and construction (AEC) industry to positively impact project …

This paper aims to improve automation in brick segmentation and crack detection of
masonry walls through image-based techniques and machine learning. Initially, a large …