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

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 …

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 …

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 …

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

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 …

This paper reviews recent developments in deep learning-based crack segmentation
methods and investigates their performance under the impact from different image types …

Deep learning, especially convolutional neural network (CNN), is becoming a popular and
powerful tool for crack detection. This work aims to apply deep learning and infrared …

Current procedures for the rapid inspection of buildings and infrastructure are subjective,
time-consuming, and cumbersome to document, necessitating new technologies to …

Fatigue cracking is usually associated with the structural failure of asphalt pavement. This
work aims to apply infrared thermography and deep learning, especially convolutional …