GA-based algorithm for selecting optimal repair and rehabilitation methods for reinforced concrete (RC) bridge decks

Lee, Cheol-Kyu; Kim, Sung-Keun

doi:10.1016/j.autcon.2006.03.001

Cited by 41 publications

(28 citation statements)

References 6 publications

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“…There are more applications in maintenance including designing a risk-informed balanced system using a genetic algorithm (Podofillini & Zio 2008) , studies on optimising outage maintenance schedule (Hadavi 2008), selecting optimal repair and rehabilitation methods for reinforced concrete bridge decks (Lee & Kim 2007), multi-year preventive maintenance program (Chootinan et al 2006) and a model for preventive maintenance planning (Lapa et al 2006). There has also been interest in application of GAs in fault diagnosis including power transformer (Fei & Zhang 2009), water hydraulic motor ) and gas turbine (Ogaji et al 2005b).…”

Section: Gas In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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Purpose: The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.Design/methodology/approach: The paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004(Kobbacy et al. 2007. Like the previous survey, it uses the Elsevier's ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings:The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

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Section: Gas In Quality Maintenance and Fault Diagnosismentioning

confidence: 99%

A survey of AI in operations management from 2005 to 2009

Kobbacy

Vadera

2011

Journal of Manufacturing Technology Management

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“…Tan, Chan, and Fwa (2004) developed a twostep fund allocation approach using GA to arrive at an optimal solution for budget allocation problems. In addition, Lee and Kim (2007) suggested a GA for a bridge management system to prioritise bridge maintenance activities at the network level and to show its implementation. Likewise, Okasha and Frangopol (2010) applied GA to solve the problem of optimum maintenance strategies with either or both essential and preventive maintenance actions.…”

Section: Po Challenges For Budget Allocation In Bridge Rehabilitationmentioning

confidence: 99%

“…In this regard, Cheng, Wu, Chen, and Weng (2009) organised the optimisation problem based on the maximisation of total benefits for rehabilitation activities while level of rehabilitation tasks are compatible with the type of damage and degree of deterioration for each bridge. In addition, Lee and Kim (2007) constructed the rehabilitation objective based on the recovering effect, the applicability and cost of the applied method and declared that objective function for optimal repair and rehabilitation may vary depending on the selected criteria. Gokey, Klein, and Mackey (2009) developed a methodology for prioritising bridge structures for maintenance in order to accommodate a limited budget applying a number of objectives including maintenance, economic and political issues to identify areas of concern when selecting bridges for further evaluation.…”

Section: Evaluation Criteria and Constraints In Bridge Rehabilitationmentioning

confidence: 99%

Post-Pareto optimality approach to enhance budget allocation process for bridge rehabilitation management

Yadollahi

Majid

Zin

2014

Structure and Infrastructure Engineering

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A variety of conflicting criteria in the form of objective functions exist in budget allocation optimisation problem for bridge rehabilitation projects. Budget allocation decision-making for such transportation assets is generally a combinatorial problem. The nature of the problem is a good reason for decision-makers to apply multi-objective optimisation techniques. However, manually choosing an acceptable solution from a set of optimal solutions is a time-consuming task, which would be avoided if the optimisation technique could be followed by a ranking method to obtain unique acceptable solution.To enhance the budget allocation process, this paper develops a posteriori approach to prioritise Pareto-Optimal (PO) solutions generated by genetic algorithm in order to identify a unique package of bridge rehabilitation activities. By identifying the most conventional objective functions for bridge rehabilitation based on technical and managerial criteria, a multi-objective knapsack problem is constructed. PO solutions will then be prioritised applying 'Technique for Order Preference by Similarity to an Ideal Solution'. The feasibility of the study will be finally demonstrated through an illustrative example. The proposed ranking approach may facilitate the budget allocation optimisation process for bridge rehabilitation where one or a few acceptable solutions are demanded.

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“…It is helpful in solving large and complicated problems using ideas from natural genetics and evolutionary principles [21]. Combining problem solving algorithms with the principles of evolution, GA demonstrates great operations in combinatorial optimization [22]. By giving more chances to the better elements to have offspring in the next generation, the GA facilitates an evolutionary process in which elements in a population progressively improve over time [23].…”

Section: Genetic Algorithms (Ga)mentioning

confidence: 99%