Optimal Repair and Inspection Rules under Uncertainty

Jido, Mitsuru; Otazawa, Toshimori; Kobayashi, Kiyoshi

doi:10.1061/(asce)1076-0342(2008)14:2(150)

Cited by 27 publications

(5 citation statements)

References 12 publications

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“…Li and Madanat (2002) considered the problem of optimizing the frequency and the intensity of pavement resurfacing and proposed a method for deciding on the resurfacing that finds the optimal minimum serviceability level and decides on the resurfacing according to the minimum serviceability level. Jido et al (2008) focused on optimal inspection/repairing policies for public facilities like pavements and formulated an optimal impulse control problem and a solution algorithm showing an iterative calculation procedure.…”

Section: Review Of Intervention Planning Modelmentioning

confidence: 99%

See 1 more Smart Citation

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

Nakazato,

Mizutani

2024

Computer aided Civil Eng

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This study proposes a methodology for deriving the optimal work zone schedule for the annual routine maintenance planning in an infrastructure asset management system considering the (i) economies of scale in work zone costs due to work zone synchronization and (ii) user costs across the road network with traffic assignments. A key aspect of the proposed methodology is the ability to derive in detail optimal work zone schedules of realistic‐scale road networks in 100 m sections for 365 days, which is beneficial in practice. To this end, an optimization model of the work zone schedule is newly formulated as a mixed‐integer programming (MIP) problem, and a novel bilevel solution method for the model utilizing the conventional solver Gurobi Optimizer with MIP algorithms such as root relaxation, dual simplex, barrier methods, and cutting planes is proposed. In application examples, the proposed methodology is applied to two real‐world road networks, which confirms that the optimal work zone schedule can be obtained in 313.7 s for a network with 2640, 100 m, sections and in 168,180 s (46 h and 43 min) for a network with 5038, 100 m, sections.

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Section: Review Of Intervention Planning Modelmentioning

confidence: 99%

“…Jido et al. (2008) focused on optimal inspection/repairing policies for public facilities like pavements and formulated an optimal impulse control problem and a solution algorithm showing an iterative calculation procedure.…”

Section: Literature Reviewmentioning

confidence: 99%

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

Nakazato,

Mizutani

2024

Computer aided Civil Eng

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“…Maintenance activities may be planned, preventive or inspection-based; full retrofitting means that the performance index of the system is taken back to its target or design value. The role of inspections and the study of the uncertainties related with the actual knowledge of the real state of the system is of course crucial [4] to take right and timely decisions in this respect.…”

Section: Challenges and Opportunities In The Near Futurementioning

confidence: 99%

Examples of Future Potential Smart Civil Structures

Grosso

Andrea²

2008

Emboding Intelligence in Structures and Integrated Systems

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The paper starts reviewing the problems that the structural engineer of tomorrow will have to solve by taking advantage of the smart technologies. As a second aspect, the paper will summarize the smart technologies already available or that will most likely become available in the near future, in order to individuate the opportunities that these technologies will offer for the conception of innovative smart civil structures. Some examples of future smart civil structures that can be based on such technologies are finally presented.

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“…Life cycle costs are the sum of all expenses incurred to stakeholders (owners, users, and the public) during the lifetime of civil infrastructures. The LCCA is often carried out either with minimization of discount flow method (Jido et al 2008) or minimization of annual average cost method (Kobayashi and Kuhn 2007;Kaito et al 2005). One important, but subtle, feature of the LCCA is that it has to be dependent on the deterioration model, which is employed to predict the deterioration of civil infrastructure.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Estimation Method to Improve Deterioration Prediction for Infrastructure System with Markov Chain Model

Kobayashi¹,

Kaito²,

Lethanh³

2012

IJAEC

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In many practices of bridge asset management, life cycle costs are estimated by statistical deterioration prediction models based upon monitoring data collected through inspection activities. In many applications, it is, however, often the case that the validity of statistical deterioration prediction models is flawed by an inadequate stock of inspection dates. In this paper, a systematic methodology is presented to provide estimates of the deterioration process for bridge managers based upon empirical judgments at early stages by experts, and whereby revisions may be made as new data are obtained through later inspections. More concretely, Bayesian estimation methodology is developed to improve the estimation of Markov transition probability of the multi-stage exponential Markov model by Markov chain Monte Carlo method using Gibbs sampling. The paper concludes with an empirical example, using the real world monitoring data, to demonstrate the applicability of the model and its Bayesian estimation method in the case of incomplete monitoring data.

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Optimal Repair and Inspection Rules under Uncertainty

Cited by 27 publications

References 12 publications

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

365‐day sectional work zone schedule optimization for road networks considering economies of scale and user cost

Examples of Future Potential Smart Civil Structures

A Bayesian Estimation Method to Improve Deterioration Prediction for Infrastructure System with Markov Chain Model

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