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- Title
An optimization-based methodology for the definition of amplitude thresholds of the ground penetrating radar.
- Authors
Mohammed Abdelkader, Eslam; Marzouk, Mohamed; Zayed, Tarek
- Abstract
Existing infrastructure is aging, while the demands are growing for a better infrastructure system in response to the high standards of safety, health, population growth, and environmental protection. Bridges are subjected to severe deterioration agents such as variable traffic loading, deferred maintenance, cycles of freeze and thaw. The development of Bridge Management Systems (BMSs) has become a fundamental imperative nowadays due to the huge variance between the need for maintenance actions and the available funds to perform such actions. Condition assessment is regarded as one of the most critical and vital components of BMSs. Ground penetrating radar (GPR) is one of the nondestructive techniques that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. There is a major issue associated with the GPR which is the absence of a scale for the amplitude values. The objective of the proposed model is to compute standardized amplitude thresholds for corrosion maps. The proposed model considers eight un-supervised clustering algorithms to obtain the thresholds. The proposed model incorporates a multi-objective optimization-based methodology that employs three evolutionary optimization algorithms to calculate the optimum thresholds which are: (1) genetic algorithm, (2) particle swarm optimization algorithm, and (3) shuffled frog-leaping algorithm. Five multi-criteria decision-making techniques are used to provide a ranking for the solutions. Finally, group decision-making is performed to aggregate the results and obtain a consensus and compromise solution. The standardized thresholds obtained from the proposed methodology are: − 16.7619, − 8.8161, and − 2.9744 dB.
- Subjects
GROUND penetrating radar; PARTICLE swarm optimization; EVOLUTIONARY algorithms; GROUP decision making; MATHEMATICAL optimization; GENETIC algorithms
- Publication
Soft Computing - A Fusion of Foundations, Methodologies & Applications, 2019, Vol 23, Issue 22, p12063
- ISSN
1432-7643
- Publication type
Article
- DOI
10.1007/s00500-019-03764-3