Bridge maintenance prioritization using analytic hierarchy process and fusion tables

Contreras-Nieto, Cristian; Shan, Yongwei; Lewis, Phil; Hartell, Julie Ann

doi:10.1016/j.autcon.2019.01.016

Cited by 48 publications

(18 citation statements)

References 23 publications

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“…In annual budget preparations, maintenance costs are wasted when the preventive maintenance is inefficient [4,5]. The government public sector has a limited annual budget that is always insufficient to serve the needs of routine maintenance [23,24]. Alberto et al [25] and Xie and Zhang [26] considered these maintenance budget limitations to reduce the bridge maintenance cost ratio, in order to optimize structure safety from earthquake events.…”

Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Optimization Model for Life Cycle Maintenance Scheduling of Bridge Infrastructure

2020

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As bridge infrastructure ages, the deterioration of materials and hazard events reduce the service quality and compromise the safety of the structure. Therefore, there is a tremendous need for bridge maintenance planning, and such maintenance studies during recent years have focused on the life cycle aspect. To fulfill the budget requirements of life cycle maintenance, an important issue is to ensure that the limited maintenance budget is utilized in an effective way. However, there are few studies that have aimed to assess the topic of budget allocation and the adjustment of bridge life-cycle maintenance issues. In order to resolve such issues, a two-stage optimization model based on constraint programming (CP) is proposed in this study to deal with maintenance scheduling problems. This is facilitated by adopting the resource-constrained project scheduling problem (RCPSP) framework, in which, three plans according to the maintenance time point are considered (i.e., early, middle, and late plans). According to the RCPSP concepts, this study views the budget ceiling as the resource limit, and maintenance plans as activities, so that the feasibility of each maintenance plan depends on the sufficiency of the budget. As the first stage, Model-I (the life cycle lifespan evaluation model) takes a life cycle perspective, evaluating how long it will take to keep all bridges in a serviceable condition with minimum expenditure over the planning cycle, and evaluates the annual budgets that can be used as a reference for users to draft a budget plan. Based on the planning result from Model-I and the actual annual budget approved for the current year, the second stage, Model-II (the annual budget allocation model) then reallocates the actual budget to take into account the importance of all bridges and different costs and benefits of maintenance plans, and revises the suggested annual budget values obtained by Model-I for the following years. Through a case study, the optimized result demonstrates that annual recursive implementation of this two-stage model satisfies the need to adjust existing budgetary data, and provides management personnel with optimized and realistic maintenance decision support for bridge infrastructure.

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Section: Literature Reviewmentioning

confidence: 99%

Two-Stage Optimization Model for Life Cycle Maintenance Scheduling of Bridge Infrastructure

2020

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“…Thus, much effort has been focused on the development of bridge prioritization strategies that optimize returns on MRR investments. Most recent studies have adopted a risk-based approach [1,3,6,13]. They often determine the risk associated with continuing the usage of a bridge based on its current and projected bridge performance.…”

Section: Research Motivationmentioning

confidence: 99%

“…This data-driven decision-making process requires information obtainable from the quantitative analysis of transportation assets' inspection records. Thus, there has been an increasing interest in the performance analysis of transportation assets among agencies around the world because each nation's infrastructure is essential for supporting economic development and sustainability and boosting public health and safety [1]. Among transportation infrastructure, bridges constitute the most expensive assets, by mile, for transportation agencies across the United States [2] and around the world.…”

Section: Introductionmentioning

confidence: 99%

A Strategic Move for Long-Term Bridge Performance within a Game Theory Framework by a Data-Driven Co-Active Mechanism

Oyegbile

Chorzepa

2020

Infrastructures

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The Federal Highway Administration (FHWA) requires that states have less than 10% of the total deck area that is structurally deficient. It is a minimum risk benchmark for sustaining the National Highway System bridges. Yet, a decision-making framework is needed for obtaining the highest possible long-term return from investments on bridge maintenance, rehabilitation, and replacement (MRR). This study employs a data-driven coactive mechanism within a proposed game theory framework, which accounts for a strategic interaction between two players, the FHWA and a state Department of Transportation (DOT). The payoffs for the two players are quantified in terms of a change in service life. The proposed framework is used to investigate the element-level bridge inspection data from four US states (Georgia, Virginia, Pennsylvania, and New York). By reallocating 0.5% (from 10% to 10.5%) of the deck resources to expansion joints and joint seals, both federal and state transportation agencies (e.g., FHWA and state DOTs in the U.S.) will be able to improve the overall bridge performance. This strategic move in turn improves the deck condition by means of a co-active mechanism and yields a higher payoff for both players. It is concluded that the proposed game theory framework with a strategic move, which leverages element interactions for MRR, is most effective in New York where the average bridge service life is extended by 15 years. It is also concluded that the strategic move can lead to vastly different outcomes. Pennsylvania’s concrete bridge management strategy currently appears to leverage a co-active mechanism in its bridge MRR strategies. This is noteworthy because its bridges are exposed to similar environmental conditions to what is obtainable in Virginia and New York and are subjected to more aggressive weather conditions than those in Georgia. This study illustrates how a strategic move affects the payoffs of different players by numerically quantifying changes in service life from bridge time-dependent bridge performance relationships.

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“…Numerous previous studies used the AHP approach for complex decision making applications in different disciplines. A few recent studies that used AHP or other multi-criteria decision making approaches in bridge and pipeline infrastructure management include [11][12][13][14].…”

Section: Previous Researchmentioning

confidence: 99%

A Failure Risk-Based Culvert Renewal Prioritization Framework

2019

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Transportation agencies are currently challenged to keep up with culvert infrastructure that is rapidly deteriorating due to lack of adequate maintenance and capital improvement. It is imperative for the transportation agencies to identify and rehabilitate deteriorated culverts prior to their failures. Among several concerns, lack of rational rehabilitation prioritization tools is foremost. Complicating this need further, current practices vary widely across the state departments of transportation (DOTs) which makes it difficult to develop a universal approach for prioritizing failing culverts. This paper presents and demonstrates a failure risk-based culvert prioritization approach that is compliant with the inspection procedures of the South Carolina DOT. The approach presented in this paper is specifically developed for reinforced concrete pipe (RCP) and corrugated metal pipe (CMP) materials because of their wide popularity. Outcomes from a survey of state DOTs informed the development of parametric weightings using the principles of analytical hierarchy process (AHP). Weightings developed for several critical inspection parameters are combined with the corresponding condition assessment scores to determine the failure criticality of culverts, which are subsequently combined with estimated failure consequences to determine failure risk estimates. The prioritization approach is demonstrated using the condition assessment scores of over 5200 culvert structures in South Carolina.

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Bridge maintenance prioritization using analytic hierarchy process and fusion tables

Cited by 48 publications

References 23 publications

Two-Stage Optimization Model for Life Cycle Maintenance Scheduling of Bridge Infrastructure

Two-Stage Optimization Model for Life Cycle Maintenance Scheduling of Bridge Infrastructure

A Strategic Move for Long-Term Bridge Performance within a Game Theory Framework by a Data-Driven Co-Active Mechanism

A Failure Risk-Based Culvert Renewal Prioritization Framework

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