Design and Optimization of CNN Architecture to Identify the Types of Damage Imagery

Ching-Lung Fan () and Yu-Jen Chung
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Ching-Lung Fan: Department of Civil Engineering, The Republic of China Military Academy, Kaohsiung 830, Taiwan
Yu-Jen Chung: Department of Marine Science, The Republic of China Naval Academy, Kaohsiung 813, Taiwan

Mathematics, 2022, vol. 10, issue 19, 1-20

Abstract: Damage to the surface construction of reinforced concrete (RC) will impact the security of the facility’s structure. Deep learning can effectively identify various types of damage, which is useful for taking protective measures to avoid further deterioration of the structure. Based on deep learning, the multi-convolutional neural network (MCNN) has the potential for identifying multiple RC damage images. The MCNN6 of this study was evaluated by indicators (accuracy, loss, and efficiency), and the optimized architecture was confirmed. The results show that the identification performance for “crack and rebar exposure” (Type B) by MCNN6 is the best, with an accuracy of 96.81% and a loss of 0.07. The accuracy of the other five types of damage combinations is also higher than 80.0%, and the loss is less than 0.44. Finally, the MCNN6 model can be used in the detection of various damage to achieve automated assessment for RC facility surface conditions.

Keywords: convolutional neural network; damage images; reinforced concrete; image recognition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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