Multiobjective optimization for risk-based maintenance and life-cycle cost of civil infrastructure systems

Abstract

Reliability and durability of civil infrastructure systems such as highway bridges play a very important role in sustainable economic growth and social development of any country. The 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 highway agencies can provide. Bridge management systems (BMSs) are thus critical to cost-effectively allocate limited maintenance resources to bridges for achieving satisfactory lifetime safety and performance. In existing BMSs, however, visual inspections are the most widely adopted practice to quantify and assess bridge conditions, which are unable to faithfully reflect structural capacity deterioration. Failure to detect structural deficiency due to, for example, corrosion and fatigue, and inability to accurately assess real bridge health states may lead to unreliable bridge management decisions and even enormous safety and economic consequences. In this paper, recent advances in risk-based life-cycle maintenance management of deteriorating civil infrastructure systems with emphasis on high-way bridges are reviewed. Methods of predicting lifetime safety and performance of highway bridges with and without maintenance are discussed. Treatment of various uncertainties associated with the complex deterioration processes due to time-dependent loading, environmental stressors, structural resistances, and maintenance actions are emphasized. The bridge maintenance management is formulated as a nonlinear, discrete, combinatorial optimization problem with simultaneous consideration of multiple and conflicting objectives, which address bridge safety and performance as well as long-term economic consequences. The effectiveness of genetic algorithms as a numerical multiobjective optimizer for producing Pareto-optimal tradeoff solutions is demonstrated. The proposed probabilistic multiobjective optimization BMS is applied at project-level for similar bridges and at network-level for a group of different bridges that form a highway network. © 2006 International Federation for Information Processing.