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Computational Science, Engineering & Technology Series
ISSN 1759-3158
Edited by: B.H.V. Topping, C.A. Mota Soares
Chapter 14

Probabilistic Maintenance and Optimization Strategies for Deteriorating Civil Infrastructures

D.M. Frangopol and L.C. Neves

Department of Civil, Environmental and Architectural Engineering, University of Colorado at Boulder, United States of America

Full Bibliographic Reference for this chapter
D.M. Frangopol, L.C. Neves, "Probabilistic Maintenance and Optimization Strategies for Deteriorating Civil Infrastructures", in B.H.V. Topping, C.A. Mota Soares, (Editors), "Progress in Computational Structures Technology", Saxe-Coburg Publications, Stirlingshire, UK, Chapter 14, pp 353-377, 2004. doi:10.4203/csets.11.14
Keywords: bridges, cost, deterioration, existing structures, genetic algorithms, maintenance, optimization, simulation, uncertainty.

In developed countries, civil infrastructures are one of the most significant investments of governments, corporations, and individuals. Among these, transportation infrastructures, including highways, bridges, airports, and ports, are of huge importance, both economical and social. Most developed countries have built a fairly complete network of highways to fit their needs. As a result, the required investment in building new highways has diminished during the last decade, and should be further reduced in the following years. On the other hand, significant structural deteriorations have been detected in transportation networks, and a huge investment is necessary to keep these infrastructures safe and serviceable. Due to the significant importance of bridges in the serviceability of highway networks, maintenance of these structures plays a major role.

In the United States, currently available bridge management systems, including PONTIS and BRIDGIT, are directly related to the condition states of bridge elements. The number of condition states is limited (e.g., five) for each bridge element. Each condition describes the type and severity of element deterioration in visual terms. Poorer conditions indicate the need for more extensive maintenance actions. PONTIS and BRIDGIT assume that the condition states incorporate all the information necessary to predict future deterioration and use a Markovian deterioration model to predict the annual probability of transition among condition states. This is a quite simple approach requiring relatively limited computational power. The approach is also intuitive, as it relates the need for maintenance with aspects that can be visually observed.

The Markovian approach used in currently available bridge management systems has several important limitations, such as: (a) severity of deterioration is described in visual terms only; (b) condition deterioration is assumed to be a single step function; (c) transition rates among condition states of a bridge element are not time dependent; and (d) bridge system condition deterioration is not explicitly considered. Experience gained in different countries shows that the major part of the work on existing bridges depends on the load carrying capacity (or structural reliability) of the bridge system rather than the condition states of the bridge elements alone. The use of condition state as the only indicator of the performance of a structure can be misleading. In fact, structural defects that are not visible and/or not discovered by visual inspections can be extremely detrimental to the structural safety. Furthermore, results of visual inspection can be significantly influenced by the experience of the inspectors, accessibility to the structure, and recent repairs that might have hidden existing defects. Consequently, bridge management systems have to also consider the load carrying capacity (or structural reliability) deterioration.

In this paper, recent progress in probabilistic maintenance and optimization strategies for deteriorating structures with emphasis on bridges is summarized. A novel model including interaction between structural safety analysis, through the safety index, and visual inspections and non destructive tests, through the condition index, is presented. Single objective optimization techniques leading to maintenance strategies associated with minimum expected cumulative cost and acceptable levels of condition and safety are presented. Furthermore, multi-objective optimization is used to simultaneously consider several performance indicators such as safety, condition, and cumulative cost. Realistic examples of the application of some of these techniques and strategies are also presented.

Results presented by the authors and co-workers at the University of Colorado [1,2,3,4,5] show the crucial role of preventive maintenance actions in reducing the overall maintenance costs, and the need for essential maintenance actions in keeping structures safe and serviceable, during their entire service life.

D.M. Frangopol, J.S. Kong, E.S. Gharaibeh "Reliability-based life-cycle management of highway bridges", Journal of Computing in Civil Engineering, ASCE, 15(1), 27-47, 2001. doi:10.1061/(ASCE)0887-3801(2001)15:1(27)
D.M. Frangopol "A probabilistic model based on eight random variables for preventive maintenance of bridges", Presented at the Progress Meeting "Optimum Maintenance Strategies for Different Bridge Types", Highways Agency, London, November, 1998.
D.M. Frangopol, L.C. Neves "Life-cycle maintenance strategies for deteriorating structures based on multiple probabilistic performance indicators", System-based Vision for Strategic and Reactive Design, Bontempi F, ed., Sweets & Zeitlinger, Lisse, 1, 3-9 (keynote paper), 2003.
L.C. Neves, D.M. Frangopol "Condition, safety and cost profiles for deteriorating structures with emphasis on bridges", submitted, 2004. doi:10.1016/j.ress.2004.08.018
M. Liu, D.M. Frangopol "Optimal bridge maintenance planning based on probabilistic performance prediction", Engineering Structures, Elsevier, 26(7), 991-1002, 2004. doi:10.1016/j.engstruct.2004.03.003

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