Integrated Maintenance-Monitoring-Management Framework for Optimal Decision Making in Bridge Life-Cycle Performance

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CMS-0509772 --- Abstract Integrated Maintenance-Monitoring-Management Framework for Optimal Decision Making in Bridge Life-Cycle Performance In this proposed research, an analytical and computational framework for integrated maintenance-monitoring-management systems of highway bridges will be developed. This framework will combine in a novel manner emerging health monitoring techniques, time-dependent structural reliability theory, life-cycle costing, Bayesian updating approaches, highway transportation network analysis, and optimization. Methodologies for predicting lifetime safety and performance of highway bridges with and without monitored data will be developed. Particular emphases will be placed on proper treatment of various uncertainties associated with loading, environmental stressors, structural resistances, deterioration processes, and monitoring and maintenance activities. Cost-effectiveness of different techniques in improving the prediction of bridge safety and performance and in improving the quality of subsequent management decisions will be systematically addressed. The proposed integrated maintenance-monitoring-management framework will be formulated as a nonlinear, discrete, combinatorial optimization problem for which multiple and conflicting objectives will be considered. These objectives will address bridge safety and performance as well as long-term economic consequences. Evolutionary computation will be performed to produce a group of Pareto-optimal tradeoff solutions for the decision-making process. The novel integrated framework will be applied at project-level for individual bridges and at network-level for a group of bridges geographically distributed to form highway networks.Future sustained economic growth and social development of the Nation is very much linked to the reliability and durability of its civil infrastructure systems such as highway bridges. The highway bridge infrastructure has been undergoing severe safety and condition deterioration due to gradual aging, aggressive environmental stressors, and increasing traffic loads. Maintenance needs for deteriorating highway bridges, however, have far outpaced available scarce funds that the U.S. federal and state highway agencies can provide. Bridge management systems are thus critical to cost-effectively allocate limited maintenance resources to bridges for achieving satisfactory lifetime safety and performance. To this end, advanced technologies including emerging health monitoring techniques, innovative preservation strategies, improved uncertainty analysis, life-cycle costing methods, and computational optimization all become very important. In existing bridge management systems, however, visual inspections are the most widely adopted practice to quantify and assess bridge conditions, which are unable to reflect the structural capacity deterioration. Failure to detect structural deficiency due to, for example, corrosion, damage, fatigue and inability to faithfully assess real bridge health states may lead to unreliable bridge management decisions and even enormous safety and economic consequences. In addition, most research and practice in civil infrastructure health monitoring have been centered on development of sensing technologies, prototype devises, and data processing algorithms. Little research has been done on systematical integration of health monitoring and maintenance into management of civil infrastructure for more reliable performance prediction. The research efforts proposed will have a significant impact on the evolution of highway bridge infrastructure management by innovatively bridging the apparent gap between the current highway infrastructure management and monitoring research and practice. Using the proposed integrated framework, the entire bridge engineering community will become aware of the tradeoffs existing between competing objectives that have both immediate and long-term safety and economic implications. Therefore, this research will be an important step in forming the necessary basis for the next-generation of bridge management systems, which should produce optimum maintenance solutions by integrating updated bridge information and by considering multiple and conflicting criteria. In addition, the proposed research has the potential to significantly reduce life-cycle costs and to increase the longevity for the built environment by impacting the safety, maintenance, and management of the Nation's civil infrastructure.