Assessment of Road Surface Conditions Using Machine Learning

Abstract

Interstate and asphalt configuration assumes an Assessing road surface conditions is essential for effective transportation infrastructure management, ensuring safety, operational efficiency, and cost-effective maintenance. Conventional evaluation methods are often slow, labor-intensive, and susceptible to human error. This study investigates the use of machine learning techniques to automate and enhance the accuracy of road condition assessments. By integrating image processing, sensor data, and deep learning algorithms, the proposed approach enables real-time detection and classification of pavement distresses such as cracks, potholes, and rutting. The research utilizes data from multiple sources, including satellite imagery, LiDAR (Light Detection and Ranging), and vehicle-mounted sensors, to develop predictive models for road deterioration trends. Findings indicate that machine learning significantly improves the efficiency and reliability of road surface evaluations compared to traditional methods. This study underscores the transformative role of artificial intelligence in modernizing transportation infrastructure maintenance, leading to more sustainable and cost-efficient road management solutions.

Keywords: Road Surface Assessment, Machine Learning, Pavement Distress Detection, Deep Learning, Artificial Intelligence, Transportation Infrastructure, LiDAR