Risk based probabilistic pavement deterioration prediction models for low volume roads. Issue Volume 19:Issued 1(2018) (2nd January 2018)

• Record Type: Journal Article
• Authors: Rose, Susan; Mathew, Binu Sara; Isaac, Kuncheria P.; Abhaya, A. S.
• Is Part Of: International journal of pavement engineering. Volume 19:Issued 1(2018)
• Page Start: 88
• Page End: 97
• Publication Date: 2018-01-02
• Subjects: Pavement performance -- probabilistic modelling -- low-volume roads -- construction quality -- pavement distresses -- risk analysis; Pavements -- Design and construction -- Periodicals; Highway engineering -- Periodicals; 625.805
• DOI: 10.1080/10298436.2016.1162308
• Languages: English
• ISSNs: 1029-8436

Abstract

Abstract: 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.