剥落
开裂
交叉口(航空)
热的
结构工程
计算机科学
过程(计算)
材料科学
深度学习
接头(建筑物)
人工智能
工程类
复合材料
操作系统
物理
航空航天工程
气象学
作者
A. Diana Andrushia,N. Anand,Éva Lublóy,Prince Arulraj G
标识
DOI:10.1177/1369433220986637
摘要
Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).
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