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22 March 2021 Automated two-stage approach for detection and quantification of surface defects in concrete bridge decks
Qianyun Zhang, Amir H. Alavi
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Proceedings Volume 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV; 115920I (2021) https://doi.org/10.1117/12.2580806
Event: SPIE Smart Structures + Nondestructive Evaluation, 2021, Online Only
Abstract
We present a two-stage method for detection and quantification of surface defects in concrete bridge decks using a hybrid deep learning and image processing technique. In the first stage, a multi classifier based on an integrated convolutional neural network and long short-term memory architecture is developed to detect cracking and spalling regions. A new algorithm based on denoising and nearest neighbor methods is then developed to quantify the crack length within the detected cracking regions. The proposed method offers an acceptable damage detection and quantification performance on rough concrete surfaces. We highlight various aspects of a software program developed using the proposed method for autonomous inspection of bridge and pavement systems.
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Qianyun Zhang and Amir H. Alavi "Automated two-stage approach for detection and quantification of surface defects in concrete bridge decks", Proc. SPIE 11592, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XV, 115920I (22 March 2021); https://doi.org/10.1117/12.2580806
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KEYWORDS
Bridges
Inspection
Algorithm development
Nondestructive evaluation
Calibration
Convolutional neural networks
Defect detection
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