A systematic review of convolutional neural network-based structural condition assessment techniques
With recent advances in non-contact sensing technology such as cameras, unmanned aerial
and ground vehicles, the structural health monitoring (SHM) community has witnessed a …
and ground vehicles, the structural health monitoring (SHM) community has witnessed a …
Review on automated condition assessment of pipelines with machine learning
Y Liu, Y Bao - Advanced Engineering Informatics, 2022 - Elsevier
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 …
incidents continue occurring. Condition assessment of pipelines is essential to identify …
Automatic concrete crack segmentation model based on transformer
W Wang, C Su - Automation in Construction, 2022 - Elsevier
Routine visual inspection of concrete structures is essential to maintain safe conditions.
Therefore, studies of concrete crack segmentation using deep learning methods have been …
Therefore, studies of concrete crack segmentation using deep learning methods have been …
Review of artificial intelligence-based bridge damage detection
Bridges are often located in harsh environments and are thus extremely susceptible to
damage. If the initial damage is not detected in time, it can develop further causing safety …
damage. If the initial damage is not detected in time, it can develop further causing safety …
MaDnet: multi-task semantic segmentation of multiple types of structural materials and damage in images of civil infrastructure
Manual visual inspection is the most common means of assessing the condition of civil
infrastructure in the United States, but can be exceedingly laborious, time-consuming, and …
infrastructure in the United States, but can be exceedingly laborious, time-consuming, and …
A lightweight crack segmentation network based on knowledge distillation
W Wang, C Su, G Han, H Zhang - Journal of Building Engineering, 2023 - Elsevier
This paper presents a novel approach for addressing the challenges of large parameter
volumes and high computational complexity in existing deep learning models for crack …
volumes and high computational complexity in existing deep learning models for crack …
How computer vision can facilitate flood management: A systematic review
Better prediction and monitoring of flood events are key factors contributing to the reduction
of their impact on local communities and infrastructure assets. Flood management involves …
of their impact on local communities and infrastructure assets. Flood management involves …
Failure signature classification in solar photovoltaic plants using RGB images and convolutional neural networks
Physical fault detection in panels that are part of photovoltaic (PV) plants typically involves
the analysis of thermal and electroluminescent images, which makes it either difficult or …
the analysis of thermal and electroluminescent images, which makes it either difficult or …
Convolutional neural network-based pavement crack segmentation using pyramid attention network
W Wang, C Su - Ieee Access, 2020 - ieeexplore.ieee.org
Cracks are the most common road pavement damage. Due to the propagation of cracks, the
detection of early cracks has great practical significance. Traditional manual crack detection …
detection of early cracks has great practical significance. Traditional manual crack detection …
Convolutional neural network based defect recognition model for phased array ultrasonic testing images of electrofusion joints
Y Tao, J Shi, W Guo, J Zheng - Journal of …, 2023 - asmedigitalcollection.asme.org
This technical brief proposes a defect recognition model to recognize four typical defects of
phased array ultrasonic testing (PA-UT) images for electrofusion (EF) joints. PA-UT has …
phased array ultrasonic testing (PA-UT) images for electrofusion (EF) joints. PA-UT has …