Feasibility of Advanced Reflective Cracking Prediction and Detection for Pavement Management Systems Using Machine Learning and Image Detection

Abstract

This research manuscript presents a comprehensive investigation into the prediction and detection of reflective cracking in pavement infrastructure through a combination of machine learning approaches and advanced image detection techniques. Leveraging machine learning algorithms, reflective cracking prediction models were developed and optimized for accuracy and efficiency. Additionally, the efficacy of image detection methods, particularly utilizing Mask R-CNN, was explored for robust and precise identification of reflective cracking on pavement surfaces. The study not only aims to enhance the predictive capabilities of pavement management systems (PMSs) through machine learning-based models but also seeks to integrate advanced image detection technologies to support real-time monitoring and assessment of pavement conditions. By providing accurate and timely detection of reflective cracking, these methodologies contribute to the optimization of pavement maintenance strategies and the overall improvement of pavement infrastructure management practices. Results indicate that the developed machine learning models achieve an average predictive accuracy of over 85%, with some models achieving accuracies exceeding 90%. Moreover, the utilization of a mask region-based convolutional neural network (Mask R-CNN) for image detection demonstrates exceptional precision, with a detection accuracy of over 95% on average across different pavement types and weather conditions. The results demonstrate the promising performance of the developed machine learning models in predicting reflective cracking, while the utilization of Mask R-CNN showcases exceptional accuracy in the detection of reflective cracking from images. This research underscores the importance of leveraging cutting-edge technologies to address challenges in pavement infrastructure management, ultimately supporting the sustainability and longevity of transportation networks.