Bridge Management System and Maintenance Optimization for Existing Bridges

Miyamoto, Atsushi; Kawamura, K.; Nakamura, Hideaki

doi:10.1111/0885-9507.00170

Cited by 127 publications

(56 citation statements)

References 12 publications

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“…The GA method was invented by John Holland [4] while Goldberg was the first to solve engineering optimization problems using GA [5]. Miyamoto et al, [6] described the GA as a power tool for obtaining optimal maintenance plans. Other work using this approach for maintenance optimization is carried out by Liu and Frangopol [7], Furuta et al [8] and Morcous and Lounis [9].…”

Section: Ga Based Methodology For Optimum Pm Strategiesmentioning

confidence: 99%

“…However, it does provide accountable improvement to perform mutation to already good solutions. For this reason elitism selection is often embedded [6] [8] to enforce the preservation of the best individual chromosomes of the current generation to the next. With elitism, a number of good solutions are designated as elite solutions and are destined to propagate unchanged.…”

Section: Development and Implementation Of Ga Based Methodologymentioning

confidence: 99%

“…Crossover method Single point crossover [6] Crossover rate, Xrate Constant 100% [6] Mutation rate, Mrate Constant 6% [2] Maximum generation Until convergence or 300 generations the input data for this case ECE is identified as the best reactive PM measure to use as opposed to CP or CR when critical concentration of chloride is predicted on the rebar. This makes sense since the use of ECE has a dramatic impact on the p f profile at little cost compared to the other reactive options.…”

Section: Pilot Case: Effectiveness Of Different Proactive and Reactivmentioning

confidence: 99%

See 2 more Smart Citations

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Tantele¹,

Votsis²,

Onoufriou³

2014

OJCE

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Preventative maintenance (PM) measures for bridges are proactive maintenance actions which aim to prevent or delay a deterioration process that may lead to failure. This type of maintenance can be justified on economic grounds since it can extend the life of the bridge and avoid the need for unplanned essential/corrective maintenance. Due to the high importance of the effective integration of PM measures in the maintenance strategies of bridges, the authors have developed a two-stage evolutionary optimization methodology based on genetic algorithm (GA) principles which links the probabilistic effectiveness of various PM measures with their costs in order to develop optimum PM strategies. In this paper, the sensitivity of the methodology to various key input parameters of the optimization methodology is examined in order to quantify their effects and identify possible trends in the optimum PM intervention profiles. The results of the sensitivity studies highlight the combined use of both proactive and reactive PM measures in deriving optimum strategy solutions. The precise mix and sequence of PM measures is clearly a function of the relative effectiveness and cost of the different available PM options as well as the various key parameters such as discount rate, target probability of failure, initial probability of failure and service life period examined. While the results highlight the need for more reliable data they also demonstrate the robustness and usefulness of the methodology; in the case where data is limited it can be used as a comparative tool to improve understanding of the effects of various strategies and enhance the decision making process.

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Section: Ga Based Methodology For Optimum Pm Strategiesmentioning

confidence: 99%

Section: Development and Implementation Of Ga Based Methodologymentioning

confidence: 99%

Section: Pilot Case: Effectiveness Of Different Proactive and Reactivmentioning

confidence: 99%

See 1 more Smart Citation

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Tantele¹,

Votsis²,

Onoufriou³

2014

OJCE

View full text Add to dashboard Cite

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“…However, the numbers of experts and senior engineers are insufficient in the area of maintenance of bridge; for that reason, how to translate tacit experience to explicit knowledge becomes a major problem in bridge maintenance (Gattulli and Chiaramonte 2005;Miyamoto et al 2000;Testa 2002;Wang and Elhag 2008).…”

Section: Case Studymentioning

confidence: 99%

The Use of Knowledge Map Model in Construction Industry

Tserng

Yin

Lee

2010

Journal of Civil Engineering and Management

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Abstract.The construction industry consists of many unstructured documents, which accumulate a large volume of tacit knowledge. In the general domain, a Knowledge Map can illustrate connections of knowledge and transfer tacit knowledge to explicit knowledge, but this is unsuitable on specific domain such as construction industry. This research generalized existing mapping rules to be applied to general and specific domain; and presented a novel and practical model for building knowledge map, named Knowledge Map Model (KMM). This model comprised of five procedures, including identifying problems, discussing with experts and users, establishing the classification structure, establishing the document base, and deciding the display model. Furthermore, this research establishes Knowledge Map Model System (KMMS) as a major tool to improve the knowledge map reused and shared among the practical process. Finally, this research validated the KMM in a real project of bridge maintenance.

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“…A first take lies in casting this in an optimization framework, where the decision maker may choose to either optimize for separate objectives, usually tied to corresponding performance indicators, e.g. condition, availability, safety, or durability (Liu et al, 1997;Miyamoto et al, 2000;Furuta et al, 2004), or instead decide to simultaneously treat conflicting objectives. When adopting a Multi-Criteria Decision-Making (MCDM) approach, the preferred policy structure of the decision maker is adopted to transform the multiple objectives into a single optimization function.…”

Section: Introductionmentioning

confidence: 99%

Observation-based Decision-making for Infrastructure

Chatzi¹,

Παπακωνσταντίνου²,

Hajdin³

et al. 2017

Proceedings of the Joint COST TU1402 - COST TU1406 - IABSE WC1 Workshop: The Value of Structural Health Monitoring for the Reli

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Abstract. The aim of this review paper is to summarize available options for decision-support toward the optimal management of infrastructure systems under consideration of uncertainties. Thereafter, we elaborate on a particular variant, namely Partially Observable Markov Decision Processes (POMDP), as a flexible framework for the incorporation of information from observations (visual inspections, non-destructive testing and monitoring) in the context of optimal inspection and maintenance planning. Examples from the literature are presented, demonstrating the potential of planning under uncertainty and the links to the Value of Information (VoI) from Structural Health Monitoring (SHM).

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Bridge Management System and Maintenance Optimization for Existing Bridges

Cited by 127 publications

References 12 publications

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

Sensitivity Analysis of Key Parameters in Decision Making of Two-Stage Evolutionary Optimization Maintenance Strategies

The Use of Knowledge Map Model in Construction Industry

Observation-based Decision-making for Infrastructure

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