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This study compares the effectiveness of Histogram-Based Gradient Boosting Classification Machine (HGBCM), Random Forest (RF), and Deep Convolutional Neural Network (DCNN) for classifying the severity of pavement raveling, a common defect in asphalt pavements. Using a dataset of 3600 images categorized into non-raveling, minor raveling, and severe raveling, the HGBCM achieved superior performance with an accuracy rate exceeding 96%. The research highlights the importance of timely detection of raveling to e

Author

pattara.rodleang

Language

EN