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 highway 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 "The authors gratefully acknowledge the partial financial support of the U.K. Highways Agency, the U.S. National Science Foundat~on through grants CMS-9912525 and CMS-0217290, and the Colorado Department of Transportat~on. The opinions and conclusions presented in this paper are those of the writers and do not necessarily reflect the views of the sponsoring organizations.

Multi-objective and probabilistic decision-making approaches to sustainable design and management of highway bridge decks

“…Several approaches have been developed to solve multiobjective optimization problems, including multi-attribute utility theory (Von Neumann and Morgenstern 1947;Keeney and Raiffa 1976), weighted sum approach (Zadeh 1963), compromise programming, constraint approach, and sequential optimization (Pareto 1919(Pareto , 1971Koski 1984;Cohn 1993, 1995;Frangopol et al 1993;2005). In this paper, the goal setting and compromise programming approach are used to solve the multiobjective bridge deck management problem.…”

“…The concept of Pareto optimality mentioned above, may be stated mathematically as follows (Pareto 1919;Koski 1984;Lounis and Cohn 1993;Frangopol 1993;2005):…”

“…Once the set of Pareto optima is generated, the "best" solution that achieves the best compromise between all competing objectives is sought. Such a solution is referred to as "satisficing" solution in the multi-objective optimization literature (Koski 1984;Yu 1985;Lounis & Cohn 1993, Frangopol et al 19932005 ).…”

“…It combines engineering principles with sound business practice and economic theory for resources allocation and utilization. The actual bridge management problem can be defined as a multi-objective optimization problem (Lounis and Cohn 1995;Frangopol et al 1993;2005) in which the decision maker is seeking to select the best decisions that achieve the best tradeoffs between different competing objectives, such as maximizing public safety, minimizing environmental impacts, maximizing the levels of services and minimizing the life cycle costs.…”

Multi-objective Optimization of Product Life-Cycle Costs and Environmental Impacts

“…Here it is possible to see the strong trade-off between initial costs and life cycle costs. Frangopol and Liu [7] and Okasha and Frangopol [12] applied a multi-objective genetic algorithm to optimize three different objectives: LCC, lifetime condition index value and lifetime safety index value for [7]; LCC, minimum redundancy index and maximum probability of failure for [12]. They are applied on structural maintenance.…”