Reliability of optimal intervals for pavement strength data collection at the network level

In road asset management, knowledge of current condition and understanding of deterioration rates of pavement strength is essential input parameter for estimating fund allocations for maintenance and rehabilitation work. However, the cost of collecting data on road pavement strength is relatively high. In a previous pilot study, a procedure was developed for optimising longitudinal sampling intervals for collection of pavement strength data for use in network level of road asset management for the State of Queensland, Australia. The findings indicated that pavement strength data could be collected at 1000-meter intervals rather than at 200-meter intervals for a tropical region of northeast Queensland, Australia. This paper presents the results of the continuing research to assess the reliability of the usage of the 1000-metre interval pavement strength data in predicting budget estimates for road maintenance and rehabilitation at the network level. In the reliability assessment, the 95th percentile budget estimates were compared with the budget estimates calculated from 1000-metre interval pavement strength data. The results indicated that the differences between the 95th percentile budget estimates and the budgets estimated from the 1000-metre interval pavement strength data were less than four per cent for 10-,15-, 20- and 25-year budget estimates, and were approximately 12.25 per cent for 5-year periods

Identification of critical input variables for risk-based cost estimates for road maintenance and rehabilitation

An estimation of costs for maintenance and rehabilitation is subject to variation due to the\ud uncertainties of input parameters. This paper presents the results of an analysis to identify input parameters\ud that affect the prediction of variation in road deterioration. Road data obtained from 1688 km of a national\ud highway located in the tropical northeast of Queensland in Australia were used in the analysis. Data were\ud analysed using a probability-based method, the Monte Carlo simulation technique and HDM-4’s roughness\ud prediction model. The results of the analysis indicated that among the input parameters the variability of\ud pavement strength, rut depth, annual equivalent axle load and initial roughness affected the variability of the\ud predicted roughness. The second part of the paper presents an analysis to assess the variation in cost\ud estimates due to the variability of the overall identified critical input parameters

Risk assessment in life-cycle costing for road asset management

Queensland Department of Main Roads, Australia, spends approximately A$ 1 billion\ud annually for road infrastructure asset management. To effectively manage road\ud infrastructure, firstly road agencies not only need to optimise the expenditure for data\ud collection, but at the same time, not jeopardise the reliability in using the optimised\ud data to predict maintenance and rehabilitation costs. Secondly, road agencies need\ud to accurately predict the deterioration rates of infrastructures to reflect local\ud conditions so that the budget estimates could be accurately estimated. And finally,\ud the prediction of budgets for maintenance and rehabilitation must provide a certain\ud degree of reliability.\ud This paper presents the results of case studies in using the probability-based method\ud for an integrated approach (i.e. assessing optimal costs of pavement strength data\ud collection; calibrating deterioration prediction models that suit local condition and\ud assessing risk-adjusted budget estimates for road maintenance and rehabilitation for\ud assessing life-cycle budget estimates).\ud The probability concept is opening the path to having the means to predict life-cycle\ud maintenance and rehabilitation budget estimates that have a known probability of\ud success (e.g. produce budget estimates for a project life-cycle cost with 5%\ud probability of exceeding).\ud The paper also presents a conceptual decision-making framework in the form of risk\ud mapping in which the life-cycle budget/cost investment could be considered in\ud conjunction with social, environmental and political issues

Mechanistic interpretation of braking distance specifications and pavement friction requirements

10.3141/2155-16Transportation Research Record2155145-157TRRE