Abstract
Pavement transverse cracks are associated with pavement performance. However, due to low recognition accuracy, the transverse cracking detection through vehicle’s vibration response has hardly been considered. This paper proposes a novel method for pavement transverse cracking detection using reconstructed vehicle vibration signal. An accelerometer installed in the vehicle collects the vibration response between the vehicle and the pavement transverse cracks. A brand-new approach to data processing is suggested that transforms a one-dimensional vibration signal into a two-dimensional vibration image. The two-dimensional vibration image generated can obtain different features effectively from the time domain. Based on Gray Level Concurrence Matrix (GLCM) algorithm, a characteristic index established from the vibration image’s contrast can distinguish transverse cracks from uncracked sections. Then its feasibility was investigated by 410 pavement sections, the accuracy of the index is 93.84%. The transverse crack detection method proposed in this paper seems promising for precisely identifying pavement transverse cracks.
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Acknowledgments
The paper was supported by the National Key R&D Program of China, Operation Performance Evaluation and Tendency Analysis for Road Infrastructure Acceptance Report (No. 2018YFB1600301), the National Natural Science Foundation of China under Grant, Analysis and Evolutionary Model of Asphalt Pavement Structure Based on Pattern Recognition (No. 51778482).
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Yuan, W., Yang, Q. A Novel Method for Pavement Transverse Crack Detection Based on 2D Reconstruction of Vehicle Vibration Signal. KSCE J Civ Eng 27, 2868–2881 (2023). https://doi.org/10.1007/s12205-023-1972-1
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DOI: https://doi.org/10.1007/s12205-023-1972-1