Life-Cycle Cost Model Simulator for Decision Making in Bridge Network Management

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Future continued economic growth of the United States is linked to the reliability and sustainability of our civil infrastructure systems, such as highways and bridges. It is widely recognized that optimal resource use in bridge infrastructure management should be based on integrating structural reliability, evaluated from a probabilistic perspective, and life-cycle cost. In the United States, transportation officials consider life-cycle cost and reliability analysis for bridges to be the most advanced techniques for assisting with investment decisions. In so doing, they attempt to determine and implement the best possible strategy for maintaining an adequate level of bridge reliability at the lowest possible life-cycle cost. The research delves into to rationally distribute limited available resources.The main objective of the research is to develop a model-based simulator to optimize management decisions for bridge networks based on simulated time-dependent performance and life-cycle cost. This innovative model will integrate computational sciences, bridge network modeling, and reliability technologies for simulating and visualizing optimal network-level bridge maintenance planning and management processes based on lifetime reliability and life-cycle cost. The solution is based on characterizing abrupt discontinuities, including phenomena such as failure of a bridge in the network and large-scale performance simulations of bridge networks. The approach will also entail representation of uncertainties propagating through the network during the service life of each bridge. By simulating the time-dependent performance of the bridge network, new data will be exploited to update the network performance as well as to auto-adapt. Finally, to facilitate real-time cost-effective decision-making in the bridge management process performance visualization techniques will be developed for each individual bridge in the network, the overall performance of the network, and the cost-benefit of each maintenance strategy. A database of about three dozen bridges located in highway networks in Colorado will be used to demonstrate the applicability and efficiency of the methodology. The research represents an important step in creating the basis of a new generation of bridge management systems where optimal maintenance decisions based on life-cycle cost are made at the network-level while explicitly taking into account the propagation of uncertainties during the entire service life of individual bridges in network. In this manner, the multi-billion dollar investment in the maintenance of bridge infrastructure in the US will be optimized. The impact of the optimization on improving the quality and functionality of the nation's highway bridges could be enormous. Finally, life-cycle cost modeling and simulation results will be integrated into engineering education at both undergraduate and graduate levels.