Deep Learning Framework for Total Stress Detection of Steel Components
Publication: Journal of Bridge Engineering
Volume 26, Issue 1
Abstract
Accurately obtaining the stress of steel components is of great importance for the condition assessment of civil structures. Currently, methods for structure stress detection have some drawbacks such as the capability of obtaining structural stress increment rather than the total stress, causing structural damage, and high cost. To overcome these drawbacks, a deep learning framework for total stress detection of steel components is proposed and its feasibility is illustrated with an example. First, the adopted deep neural network is briefly introduced, followed by the introduction of the dataset preparation. In order to maximize the stress detection accuracy, parameter analysis was conducted and the mean average precision achieved by the well-trained model for detection of the stresses under consideration is 89.67%. The robustness of the trained model was further examined and the procedures for application of the proposed approach were summarized. The presented method provides a new idea to detect the total stress of structure components that is difficult to obtain with a traditional sensor-based method.
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Acknowledgments
The authors would like to acknowledge the financial support provided by the National Natural Science Foundation of China (Grant Nos. 51808209, 51778222, and 51478176), Natural Science Foundation of Hunan Province (2019JJ50065), and the Fundamental Research Funds for the Central Universities (Grant No. 531118010081).
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Information
Published In
Journal of Bridge Engineering
Volume 26 • Issue 1 • January 2021
Copyright
© 2020 American Society of Civil Engineers.
History
Received: Dec 28, 2019
Accepted: Aug 13, 2020
Published online: Nov 9, 2020
Published in print: Jan 1, 2021
Discussion open until: Apr 9, 2021
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