Queensland Department of Main Roads, Australia, spends approximately A$ 1 billionudannually for road infrastructure asset management. To effectively manage roadudinfrastructure, firstly road agencies not only need to optimise the expenditure for dataudcollection, but at the same time, not jeopardise the reliability in using the optimiseduddata to predict maintenance and rehabilitation costs. Secondly, road agencies needudto accurately predict the deterioration rates of infrastructures to reflect localudconditions so that the budget estimates could be accurately estimated. And finally,udthe prediction of budgets for maintenance and rehabilitation must provide a certainuddegree of reliability.udThis paper presents the results of case studies in using the probability-based methodudfor an integrated approach (i.e. assessing optimal costs of pavement strength dataudcollection; calibrating deterioration prediction models that suit local condition andudassessing risk-adjusted budget estimates for road maintenance and rehabilitation forudassessing life-cycle budget estimates).udThe probability concept is opening the path to having the means to predict life-cycleudmaintenance and rehabilitation budget estimates that have a known probability ofudsuccess (e.g. produce budget estimates for a project life-cycle cost with 5%udprobability of exceeding).udThe paper also presents a conceptual decision-making framework in the form of riskudmapping in which the life-cycle budget/cost investment could be considered inudconjunction with social, environmental and political issues.
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