This paper deals with state of the art in structural health monitoring (SHM) methods in
offshore and marine structures. Most SHM methods have been developed for onshore …

The benefits of tracking, identifying, measuring features of interest from structure responses
have endless applications for saving cost, time and improving safety. To date, structural …

To date, data mining (DM) techniques, ie artificial intelligence, machine learning, and
statistical methods have been utilized in a remarkable number of structural health monitoring …

Deep learning has ushered in many breakthroughs in vision‐based detection via
convolutional neural networks (CNNs), but the vibration‐based structural damage detection …

Concrete defect information is of vital importance to building maintenance. Increasingly,
computer vision has been explored for automated concrete defect detection. However …

The ship** industry faces a large challenge as it needs to significantly lower the amounts
of Green House Gas emissions. Traditionally, reducing the fuel consumption for ships has …

Computer vision leveraging deep learning has achieved significant success in the last
decade. Despite the promising performance of the existing deep vision inspection models …

Cables and hangers are critical components of long‐span bridges, tension forces of them
are needed to be accurately measured for ensuring the safety of bridges. Traditionally, cable …

Abstract Machine learning models have been developed to perform damage detection using
images to improve bridge inspection efficiency. However, in damage detection using images …

This study introduces a novel convolutional neural network (CNN)‐based approach for
structural health monitoring (SHM) that exploits a form of measured compressed response …