Examination of Parameters and Methodology of Decision Support Systems for Pavement Maintenance

Roads are the primary component of infrastructure; they directly affect people's lives by delivering accessibility and communication. Identifying critical maintenance components and their negative effects on road safety is the first and most significant task of determining maintenance requirements to improve road safety. Network level monitoring of the distress of pavement has been emphasized by researchers. The International Roughness Index (IRI) is the most common predictor for highway repair. Relation between IRI and Pavement Condition Index (PCI) has provided positive results to estimate the distress. A technology platform has enabled the development of Decision support system (DSS) that can perform with speed and accuracy. Artificial neural network (ANN), Genetic Algorithm, Geographical Information System (GIS), Artificial Intelligence (AI), Machine learning (ML) are being widely used platforms. This paper presents a detailed review on the various equations derived to measure Pavement Serviceability Index (PSI) and the various technology platforms used by researchers for developing DSS