Deep learning breakthrough stimulates new research trends in civil infrastructure inspection,
whereas the lack of quality-guaranteed, human-annotated, free-of-charge, and publicly …

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 …

While there have been a considerable number of studies on computer vision (CV)-based
crack detection on concrete/asphalt public facilities, such as sewers and tunnels, masonry …

Strain is one of the most frequently monitored parameters in civil structural health monitoring
(SHM) applications, and strain-based approaches were among the first to be explored and …

In recent years, visual automatic non-destructive testing using machine vision algorithms
has been widely used in industry. This approach for detecting, classifying, and segmenting …

The integration of visible and thermal images has demonstrated the potential ability to
enhance crack segmentation accuracy. However, due to the intricate texture of masonry …

The deployment of machine learning for image crack detection requires model robustness
via an adaptive generalisation to unprecedented scenarios. Eg, in masonry, backgrounds …

Environmental effects may lead to cracking, stiffness loss, brace damage, and other
damages in bridges, frame structures, buildings, etc. Structural Health Monitoring (SHM) …

Cultural heritage (CH) constructions involve the use of diverse masonry materials. Under
natural and human influences, masonry materials can undergo various types of damages …

In this paper, we present a method based on an ensemble of convolutional neural networks
(CNNs) for the prediction of residual drift capacity in unreinforced damaged masonry walls …