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 …

The existing methods of metro tunnel water leakage segmentation tasks are time-
consuming, which is not easy for practical applications. In this paper, we propose a …

Aiming to automatically segment multi-class objects on the tunnel point cloud, a deep
learning network named dual attention-based point cloud network (DAPCNet) is developed …

Accurate detection of pavement cracks is an important task in road maintenance and safety
management. However, accurate segmentation of road cracks at night is still challenging …

Construction engineering and management (CEM) has become increasingly complicated
with the increasing size of engineering projects under different construction environments …

Supervised learning (SL) methods have achieved good performance in image recognition of
water leakage in tunnels, but they require a large amount of labeled data. This paper adopts …

To overcome the shortcomings of the existing water leakage monitoring methods for traffic
and public utility tunnels, such as the high probability of missed detection and significant …

Urban water is known as a critical sector of urban environments which significantly impacts
the life quality and wellbeing of reinstates. In the context of developing sustainable urban …

Water leakage segmentation based on computer vision allows for efficient monitoring and
maintenance, ensuring the safety and integrity of tunnel infrastructure. In this study, a …

Water leakage poses a serious threat to tunnel integrity and safety, making early detection
critical. However, traditional techniques struggle with high-sensitivity leak monitoring. This …