Civil infrastructure (eg, buildings, roads, underground tunnels) could lose its expected
physical and functional conditions after years of operation. Timely and accurate inspection …

Abstract Convolutional Neural Networks (CNN) have immense potential to solve a broad
range of computer vision problems. It has achieved encouraging results in numerous …

Recently, crack segmentation studies have been investigated using deep convolutional
neural networks. However, significant deficiencies remain in the preparation of ground truth …

Masonry structures represent the highest proportion of building stock worldwide. Currently,
the structural condition of such structures is predominantly manually inspected which is a …

Abstract Applications of Machine Learning (ML) algorithms in Structural Health Monitoring
(SHM) have become of great interest in recent years owing to their superior ability to detect …

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 …

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 …

With the advancement of machine learning (ML) and deep learning (DL), there is a great
opportunity to enhance the development of automatic crack detection algorithms. In this …

Cracks are important indicators of pavement structural and operational conditions. Early
pavement crack detection and treatments can help extend pavement service life, reduce fuel …

Automatic detection and segmentation of concrete cracks in tunnels remains a high-priority
task for civil engineers. Image-based crack segmentation is an effective method for crack …