Computationally Efficient Simulation Techniques for Bridge Network Maintenance Optimization under Uncertainty

doi:10.3850/978-981-08-7619-7_P010

Computationally Efficient Simulation Techniques for Bridge Network Maintenance Optimization under Uncertainty

Paolo Bocchini^1,a, Dan M. Frangopol^1,b and George Deodatis²

¹Department of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh University, 117 ATLSS Drive, Bethlehem, PA 18015-4729, USA.

^apaolo.bocchini@lehigh.edu ^bdan.frangopol@lehigh.edu

²Department of Civil Engineering and Engineering Mechanics, Columbia University in the City of New York, 630 SW Mudd, 500 West 120th Street, New York, NY 10027, USA.

deodatis@civil.columbia.edu

ABSTRACT

The number of structurally deficient bridges all over the world is rapidly increasing and the bridge maintenance under limited resources has become a topic of socioeconomic prominent importance. In the last years, several studies have focused on the analysis and optimization at the bridge network level. The majority of them use Genetic Algorithms (GAs) as numerical optimization tool. Unfortunately, when dealing with an entire bridge stock and considering the whole life-cycle, aleatory and epistemic uncertainties strongly affect most of the variables involved in the problem at hand. The large coefficients of variations and the necessity to take into account the space and/or time correlation among some of these random variables suggest the use of Monte Carlo Simulation (MCS) as stochastic numerical technique for the solution of the problem. However, GAs and MCS are both computationally very expensive tools and their joint use can be impractical for many applications. This paper presents efficient simulation techniques that can be exploited in bridge network maintenance (multi-objective) optimization problems in order to keep the computational cost acceptable for real applications while conserving a reasonable accuracy in the solution and in the probabilistic model.

Keywords: Bridge networks, Maintenance optimization, Latin hypercube, Random fields simulation, Genetic Algorithms, Pareto front, Life-cycle management.

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Computationally Efficient Simulation Techniques for Bridge Network Maintenance Optimization under Uncertainty

Paolo Bocchini1,a, Dan M. Frangopol1,b and George Deodatis2

1Department of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh University, 117 ATLSS Drive, Bethlehem, PA 18015-4729, USA.

2Department of Civil Engineering and Engineering Mechanics, Columbia University in the City of New York, 630 SW Mudd, 500 West 120th Street, New York, NY 10027, USA.

ABSTRACT

Paolo Bocchini^1,a, Dan M. Frangopol^1,b and George Deodatis²

¹Department of Civil and Environmental Engineering, ATLSS Engineering Research Center, Lehigh University, 117 ATLSS Drive, Bethlehem, PA 18015-4729, USA.

²Department of Civil Engineering and Engineering Mechanics, Columbia University in the City of New York, 630 SW Mudd, 500 West 120th Street, New York, NY 10027, USA.