Risk assessment in life-cycle costing for road asset management

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

An Integrated Risk Analysis Methodology in a Multidisciplinary Design Environment

Design of complex, one-of-a-kind systems, such as space transportation systems, is characterized by high uncertainty and, consequently, high risk. It is necessary to account for these uncertainties in the design process to produce systems that are more reliable. Systems designed by including uncertainties and managing them, as well, are more robust and less prone to poor operations as a result of parameter variability. The quantification, analysis and mitigation of uncertainties are challenging tasks as many systems lack historical data. In such an environment, risk or uncertainty quantification becomes subjective because input data is based on professional judgment. Additionally, there are uncertainties associated with the analysis tools and models. Both the input data and the model uncertainties must be considered for a multi disciplinary systems level risk analysis. This research synthesizes an integrated approach for developing a method for risk analysis. Expert judgment methodology is employed to quantify external risk. This methodology is then combined with a Latin Hypercube Sampling - Monte Carlo simulation to propagate uncertainties across a multidisciplinary environment for the overall system. Finally, a robust design strategy is employed to mitigate risk during the optimization process. This type of approach to risk analysis is conducive to the examination of quantitative risk factors. The core of this research methodology is the theoretical framework for uncertainty propagation. The research is divided into three stages or modules. The first two modules include the identification/quantification and propagation of uncertainties. The third module involves the management of uncertainties or response optimization. This final module also incorporates the integration of risk into program decision-making. The risk analysis methodology, is applied to a launch vehicle conceptual design study at NASA Langley Research Center. The launch vehicle multidisciplinary environment consists of the interface between configuration and sizing analysis outputs and aerodynamic parameter computations. Uncertainties are analyzed for both simulation tools and their associated input parameters. Uncertainties are then propagated across the design environment and a robust design optimization is performed over the range of a critical input parameter. The results of this research indicate that including uncertainties into design processes may require modification of design constraints previously considered acceptable in deterministic analyses