A Genetic Algorithm-based Decision Framework to Incorporate Climate Impact on Pavement Maintenance Planning

A recent trend of increase in the vulnerable behavior of roads to potential climate events is observed worldwide. However, a few studies conducted incorporate climate impact into road maintenance and rehabilitation. To address this, a genetic algorithm (GA) based optimization approach is proposed in this study with a climate risk index (CRI) in terms of criticality of roads, probability of occurrence of a climate event, and existing severity level of pavement. Criticality is defined by road functional class, availability of alternative routes and land use. Probability is determined by historical events and topography, while severity is defined by existing pavement condition. The CRI is incorporated as a generic constraint to the GA-based optimization model to maximize the average network condition under a given budget. To demonstrate this, a case study is conducted using twenty roads in different climatic conditions in Sri Lanka. The results show that in 25% and 50% of required total budget conditions there is a clear separation between priority roads IRI and non-priority roads IRI due to the generic constraint. This is an indication that the optimization model effectively prioritizes roads when there is a budget constraint. This concludes that the proposed approach can be utilized to make the most of the available budget for road maintenance by prioritizing roads that are highly vulnerable to climate events without compromising the overall network condition. Further, the proposed maintenance optimization approach can be extended to long term maintenance planning economically for developing countries.