ANALYSIS AND DEVELOPMENT OF DECISION SUPPORT SYSTEM FOR PAVEMENT MAINTENANCE USING MACHINE LEARNING TECHNIQUES

This presents the analysis and development of a decision support system for pavement maintenance using machine learning techniques. The deterioration of pavements is a significant concern for transportation authorities, as it affects road safety and leads to increased maintenance costs. Therefore, an efficient and proactive maintenance strategy is crucial to ensure the longevity and usability of the road network. The proposed decision support system leverages machine learning techniques to predict pavement conditions and make informed maintenance decisions. The system utilizes historical pavement data, including variables such as traffic volume, climate conditions, and pavement age, to train predictive models. Various machine learning algorithms, such as random forests, support vector machines, and neural networks, are employed to analyse the data and generate accurate predictions. Once the predictive models are trained and validated, the decision support system provides maintenance recommendations based on the predicted pavement conditions. By considering factors such as cost-effectiveness, safety, and urgency, the system aids transportation authorities in prioritizing maintenance activities and allocating resources efficiently. This proactive approach helps prevent the deterioration of pavements and extends their service life, resulting in cost savings and improved road quality. The proposed decision support system offers several advantages over traditional approaches to pavement maintenance. By integrating machine learning techniques, it can capture complex patterns and correlations within the data, leading to more accurate predictions. Furthermore, the system can adapt and learn from new data, allowing continuous improvement of maintenance strategies over time. In this research presents the analysis and development of a decision support system for pavement maintenance using machine learning techniques. By harnessing the power of data and predictive models, the system provides transportation authorities with valuable insights for proactive pavement maintenance, ultimately enhancing road quality and ensuring the safety and efficiency of the transportation network.