Integration of stochastic deterioration models with multicriteria decision theory for optimizing maintenance of bridge decks

Morcous, George; Lounis, Z

doi:10.1139/l06-011

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…Liu and Frangopol presented a general computational multiobjective procedure to prioritize, annually, maintenance efforts for deteriorating reinforced concrete bridge crossheads over a designated time horizon based on condition, safety, and cost criteria (13). Morcous and Lounis proposed a formulation that integrates multicriteria decision making for maintenance optimization and stochastic Markov chains for modeling deterioration (14). This formu-lation is adopted in this study to apply Pareto trade-off analysis to bridge maintenance management.…”

Section: Formulation Of Bridge Maintenance Optimizationmentioning

confidence: 99%

Pareto Analysis for Multicriteria Optimization of Bridge Preservation Decisions

Morcous

2007

Transportation Research Record: Journal of the Transportation R

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A new approach is presented for solving infrastructure maintenance optimization problems involving multiple, conflicting, and incommensurable criteria. Pareto analysis is adapted from the theory of social welfare economics to identify the solutions that mutually achieve the best compromise among competing criteria. The proposed approach is applied to the domain of bridge maintenance management to assist decision makers in optimizing bridge preservation decisions. Two criteria are considered in this study: the minimization of life-cycle costs and the maximization of bridge network condition. Feasibility and capability of the proposed approach are demonstrated by using an application example of concrete bridge decks. The uncertainty in deck deterioration is considered by using stochastic Markov chain models developed from the condition data of concrete bridge decks in Nebraska. Further research is needed to expand the application of the proposed approach to other infrastructure facilities, and to consider other decision criteria, such as network reliability, functionality, and user cost.

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Section: Formulation Of Bridge Maintenance Optimizationmentioning

confidence: 99%

Pareto Analysis for Multicriteria Optimization of Bridge Preservation Decisions

Morcous

2007

Transportation Research Record: Journal of the Transportation R

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“…The state-based models predict bridge condition ratings using transition probabilities obtained from the difference between the two condition states at a given discrete interval. Markov chain models are one of the most common examples of the state-based models [4]. Several approaches have been developed in the past to estimate the transition probabilities of bridge components, such as the expected-value method [2], Poisson regression [5], and ordered profit model [6].…”

Section: Introductionmentioning

confidence: 99%

Long-Term Performance of Bridge Elements Using Integrated Deterioration Method Incorporating Elman Neural Network

Lee

Guan

et al. 2012

AMM

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Currently, probabilistic deterioration modeling techniques have been employed in most state-of-the-art Bridge Management Systems (BMSs) to predict future bridge condition ratings. As confirmed by many researchers, the reliability of the probabilistic deterioration models rely heavily on the sufficient amount of condition data together with their well-distributed historical deterioration patterns over time. However, inspection records are usually insufficient in most bridge agencies. As a result, a typical standalone probabilistic model (e.g. state-based or time-based model) is not promising for forecasting a reliable bridge long-term performance. To minimise the shortcomings of lacking condition data, an integrated method using a combination of state- and time-based techniques has recently been developed and has demonstrated an improved performance as compared to the standalone techniques. However, certain shortcomings still remain in the integrated method which necessities further improvement. In this study, the core component of the state-based modeling is replaced by an Elman Neural Networks (ENN). The integrated method incorporated with ENN is more effective in predicting long-term bridge performance as compared to the typical deterioration modeling techniques. As part of comprehensive case studies, this paper presents the deterioration prediction of 52 bridge elements with material types of Steel (S), Timber (T) and Other (O). These elements are selected from 94 bridges (totaling 4,115 inspection records). The enhanced reliability of the proposed integrated method incorporating ENN is confirmed.

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“…The results of a field study based on the bridge inventory database maintained by Ministere des Transports du Quebec, Canada showed that a slight improvement in the prediction accuracy from the proposed decision tree models could be achieved. Morcous and Lounis [12] also proposed a new approach which combines a Markov-chain deterioration model with two important criteria-minimization of maintenance cost and maximization of the network condition, to optimize the maintenance management of concrete bridge decks.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Binary Recursive Partitioning for Deterioration Prediction of Infrastructure Elements

Pittou

Karlaftis

2009

Advances in Civil Engineering

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This paper introduces binary recursive partitioning (BRP) as a method for estimating bridge deck deterioration and treats it as a classification and decision problem. The proposed BRP method is applied to the Indiana bridge inventory database containing 25 years of detailed information on approximately 5,500 bridges on state-maintained highways. Classification trees are separately created for 4 and 2 prediction classes and relatively high degrees of success are achieved for deck condition prediction. The significant variables identified as the most influential include current deck condition and deck age. The proposed method offers an alternative nonparametric approach for bridge deck condition prediction and could be used for cross comparisons of models calibrated using the widely applied parametric approaches.

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Integration of stochastic deterioration models with multicriteria decision theory for optimizing maintenance of bridge decks

Cited by 8 publications

References 14 publications

Pareto Analysis for Multicriteria Optimization of Bridge Preservation Decisions

Pareto Analysis for Multicriteria Optimization of Bridge Preservation Decisions

Long-Term Performance of Bridge Elements Using Integrated Deterioration Method Incorporating Elman Neural Network

Nonparametric Binary Recursive Partitioning for Deterioration Prediction of Infrastructure Elements

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