Suherman Suherman


The prediction of road pavement performance may be facilitated by appropriate models developed by analyzing sets of historical data or data collected from accelerated pavement testing facilities. However, there may be systematic or random errors in these data sets and also the data sets may not be complete or represent the full range of conditions likely to occur in the field. As a result the predictions made by the models may not be fully accurate and include a degree of uncertainty. Therefore, ideally the behavioral modeling of long-term pavement performance should include a procedure for taking into account the uncertainty in the data and quantify it accordingly. This paper presents such a methodology that first defines the reliability of pavement performance predictions and its associated risk using a probabilistic approach. It then demonstrates how the reliability of pavement performance predictions can be estimated by considering the variability of the parameters (such as pavement strength, cumulative equivalent standard axle load and initial pavement roughness) that make up the performance model. A framework is presented that uses the Monte Carlo simulation to evaluate the effect of the model parameters variability on the allowable cumulative equivalent standard axle load applications. The analysis demonstrates that data variability has a significant influence on the reliability of pavement performance prediction.


pavement management system, Reliability analysis.

Full Text:


Template MTS (Pedoman Penulisan Artikel).pdf