Flexible pavement crack’s severity identification and classification using deep convolution neural network / A. Ibrahim …[et al.]

Ibrahim, A. and M. Zukri, N. A. Z. and Ismail, B. N. and Osman, M. K. and Yusof, N. A. M. and Idris, M. (2021) Flexible pavement crack’s severity identification and classification using deep convolution neural network / A. Ibrahim …[et al.]. Journal of Mechanical Engineering (JMechE), 8 (2). pp. 193-201. ISSN (eISSN):2550-164X


Effective road maintenance program is vital to ensure traffic safety, serviceability, and prolong the life span of the road. Maintenance will be carried out on pavements when signs of degradation begin to appear and delays may also lead to increased maintenance costs in the future, when more severe changes may be required. In Malaysia, manual visual observation is practiced in the inspection of distressed pavements. Nonetheless, this method of inspection is ineffective as it is more laborious, time consuming and poses safety hazard. This study focuses in utilizing an Artificial Intelligence (AI) method to automatically classify pavement crack severity. Field data collection was conducted to allow meaningful verification of accuracy and reliability of the crack’s severity prediction based on AI. Several important phases are required in research methodology processes including data collection, image labelling, image resizing, image enhancement, deep convolution neural network (DCNN) training and performance evaluation. Throughout the analysis of image processing results, the image output was successfully classified using MATLAB software. The good agreement between field measurement data and DCNN prediction of crack’s severity proved the reliability of the system. In conclusion, the established method can classify the crack’s severity based on the JKR guideline of visual assessment.


Item Type: Article
Email / ID Num.
Ibrahim, A.
M. Zukri, N. A. Z.
Ismail, B. N.
Osman, M. K.
Yusof, N. A. M.
Idris, M.
Subjects: Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Q Science > QA Mathematics > Analytic mechanics
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Mechanical Engineering
Journal or Publication Title: Journal of Mechanical Engineering (JMechE)
UiTM Journal Collections: UiTM Journal > Journal of Mechanical Engineering (JMechE)
ISSN: (eISSN):2550-164X
Volume: 8
Number: 2
Page Range: pp. 193-201
Keywords: Road maintenance, DCNN, Crack severity
Date: April 2021
URI: https://ir.uitm.edu.my/id/eprint/47721
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