Classification of surface pavement cracks as top-down, bottom-up, and cement-treated reflective cracking based on deep learning methods

نویسندگان

چکیده

The treatment and repair strategies for reflective fatigue cracking that initiate at the pavement surface (i.e., top-down cracking) bottom of asphalt concrete layer bottom-up are noticeably different. However, management engineers facing difficulties in identifying these cracks field because they usually appear visually identical patterns. objective this study was to develop artificial neural network (ANN) convolutional (CNN) applications differentiate classify top-down, bottom-up, cement-treated in-service flexible pavements using deep-learning models. developed CNN model achieved an accuracy 93.8% testing 91% validation phases, ANN showed overall 92%. classification tool based on variables related crack characteristics including age, annual average daily traffic, thickness layer, type base, orientation, location.

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ژورنال

عنوان ژورنال: Canadian Journal of Civil Engineering

سال: 2022

ISSN: ['1208-6029', '0315-1468']

DOI: https://doi.org/10.1139/cjce-2020-0808