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Accurate prediction of pavement performance is important for efficient management of road infrastructure. Pavement performance prediction models developed for low-volume roads are mainly based on deterministic approach. The deterministic prediction models are inadequate to completely capture the deterioration mechanism. Uncertainties may occur in pavement behaviour under changing traffic loads and environment conditions, which may not be realistically represented by deterministic model. The objective of this paper is to develop pavement deterioration prediction models by probabilistic approach, for various distresses observed on low-volume roads in the state of Kerala in India, with the help of existing deterministic models. The major distresses observed on low-volume roads were ravelling, pothole and edge failure. Load-associated distresses were rarely observed on these roads as the maximum cumulative standard axle observed was only one million standard axle (msa). Hence, lack of proper drainage and construction quality (CQ) could be attributed as the major reasons for the pavement deterioration. Progression of deterioration of pavements with age has been studied and the intensity of distresses along with corresponding probabilities was arrived at. The distresses predicted by probabilistic models were compared with those predicted by deterministic models and the actual distress values observed in the field. The prediction models were validated using Mean Absolute Percentage Error, a statistical method for accuracy measurement of forecasting models. A risk analysis was then conducted to select the critical percentile value for each type of distress corresponding to varying pavement age. A sensitivity analysis was also carried out to study the effect of pavement age and CQ on the progression of pavement deterioration. [ABSTRACT FROM AUTHOR]

India is having the second largest road network in the world and low volume roads contributes to about 61% of the total road network. Low volume roads in the rural areas face serious problems due to absence of timely maintenance resulting from stringent budget availability. For proper management of these roads, scientific pavement management tools are necessary. Right maintenance treatment is to be given to the right place at the right time. For this, the roads in a network are to be prioritized based on its importance with regard to the extent of deterioration. The deterioration of the pavement can be both functional and structural. Hence, the functional distresses, roughness and deflection of the pavement were selected as the performance indicators in this study while prioritising. The collection of distress data over the pavement life is a tedious process, where as the collection of roughness data which is a result of the distresses occurring on pavement is much easier. This paper attempts to compare the unified pavement condition indices developed using two approaches i) using combination of distresses and characteristic deflection and ii) using a combination of roughness and characteristic deflection. For the purpose of prioritisation of roads, pavement deterioration prediction models that can predict the condition of pavements at a future time are essential. Probabilistic approach is considered while developing the pavement prediction models and critical percentile values were used for prioritisation purpose. For roughness and deflection, non linear deterministic models were first developed and the corresponding probabilistic models were arrived at. Unified pavement condition indices were developed using Analytic Hierarchy Process (AHP) and the priority of the roads was compared. AHP is a simple and effective tool which uses pair wise comparison and relies on the judgments of experts to derive priority scales. Comparative approach in AHP is intuitively appealing and supported by evidence from cognitive psychology. The result shows that the indices developed using both approaches are comparable and hence prioritisation of the low volume roads can easily be done using the index developed by combining roughness and deflection values. [ABSTRACT FROM AUTHOR]