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Muhammad Abi Berkah Nadi, Sayed Ahmad Fauzan
Pág. 1 - 9
Recovery efforts following a disaster can be slow and painstaking work, and potentially put responders in harm's way. A system which helps identify defects in critical building elements (e.g., concrete columns) before responders must enter a structure ca...
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Xunqian Xu, Qi Li, Shue Li, Fengyi Kang, Guozhi Wan, Tao Wu and Siwen Wang
Based on the tunnel crack width identification, there are operating time constraints, limited operating space, high equipment testing costs, and other issues. In this paper, a large subway tunnel is a research object, and the tunnel rail inspection car i...
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Rong Wang, Xinyang Zhou, Yi Liu, Dongqi Liu, Yu Lu and Miao Su
To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResN...
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Jianyuan Li, Xiaochun Lu, Ping Zhang and Qingquan Li
The timely identification and detection of surface cracks in concrete dams, an important public safety infrastructure, is of great significance in predicting engineering hazards and ensuring dam safety. Due to their low efficiency and accuracy, manual de...
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Jingying Zhang and Tengfei Bao
Crack detection is an important component of dam safety monitoring. Detection methods based on deep convolutional neural networks (DCNNs) are widely used for their high efficiency and safety. Most existing DCNNs with high accuracy are too complex for use...
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Alexey N. Beskopylny, Evgenii M. Shcherban?, Sergey A. Stel?makh, Levon R. Mailyan, Besarion Meskhi, Irina Razveeva, Alexey Kozhakin, Diana El?shaeva, Nikita Beskopylny and Gleb Onore
The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical...
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Jie Wu, Yongjin He, Chengyu Xu, Xiaoping Jia, Yule Huang, Qianru Chen, Chuyue Huang, Armin Dadras Eslamlou and Shiping Huang
Crack detection is an important task in bridge health monitoring, and related detection methods have gradually shifted from traditional manual methods to intelligent approaches with convolutional neural networks (CNNs) in recent years. Due to the opaque ...
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Miaomiao Yuan, Zhuneng Fang, Peng Xiao, Ruijin Tong, Min Zhang and Yule Huang
Real-time systems for measuring structural cracks are of great significance due to their computational and cost efficacy, inherent hazards, and detection discrepancies associated with the manual visual assessment of structures. The precision and effectiv...
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Chuan Xu, Qi Zhang, Liye Mei, Xiufeng Chang, Zhaoyi Ye, Junjian Wang, Lang Ye and Wei Yang
Road crack detection is one of the important issues in the field of traffic safety and urban planning. Currently, road damage varies in type and scale, and often has different sizes and depths, making the detection task more challenging. To address this ...
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Yifan Liu, Weiliang Gao, Tingting Zhao, Zhiyong Wang and Zhihua Wang
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concret...
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