Innovative Imaging and Analysis Techniques for Quantifying Spalling Repair Materials in Concrete Pavements

Junhwi Cho, Julian Kang, Yooseob Song, Seungjoo Lee and Jaeheum Yeon ()
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Junhwi Cho: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Republic of Korea
Julian Kang: Department of Construction Science, Texas A&M University, College Station, TX 77843, USA
Yooseob Song: Department of Civil and Environmental Engineering, University of Alabama in Huntsville, Huntsville, AL 35899, USA
Seungjoo Lee: Department of Korean Peninsula Infrastructure Special Committee, Korea Institute of Civil Engineering and Building Technology, Ilsanseo-gu, Goyang-si 10223, Republic of Korea
Jaeheum Yeon: Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Republic of Korea

Sustainability, 2023, vol. 16, issue 1, 1-14

Abstract: Traditional spalling repair on concrete pavement roads is labor-intensive. It involves traffic blockages and the manual calculation of repair areas, leading to time-consuming processes with potential discrepancies. This study used a line scan camera to photograph road surface conditions to analyze spalling without causing traffic blockage in an indoor setting. By using deep learning algorithms, specifically a region-based convolutional neural network (R-CNN) in the form of the Mask R-CNN algorithm, the system detects spalling and calculates its area. The program processes data based on the Federal Highway Administration (FHWA) spalling repair standards. Accuracy was assessed using root mean square error (RMSE) and Pearson correlation coefficient (PCC) via comparisons with actual field calculations. The RMSE values were 0.0137 and 0.0167 for the minimum and maximum repair areas, respectively, showing high accuracy. The PCC values were 0.987 and 0.992, indicating a strong correlation between the actual and calculated repair areas, confirming the high calculation accuracy of the method.

Keywords: concrete pavement; spalling; line scan camera; Mask R-CNN; repair area; amount of repair material (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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