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Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods
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Author(s): Eslam Mohammed Abdelkader (Building, Civil and Environmental Engineering, Concordia University, Montreal, Canada), Mohamed Marzouk (Structural Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt)and Tarek Zayed (Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2022
Pages: 25
Source title:
Research Anthology on Artificial Neural Network Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-2408-7.ch049
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Abstract
Bridges are aging and deteriorating. Thus, the development of Bridge Management Systems (BMSs) became imperative nowadays. Condition assessment is one of the most critical and vital components of BMSs. Ground Penetrating Radar (GPR) is one of the non-destructive techniques (NDTs) that are used to evaluate the condition of bridge decks which are subjected to the rebar corrosion. The objective of the proposed method is to develop standardized amplitude scale for bridge decks based on a hybrid optimization-decision making model. Shuffled frog leaping algorithm is employed to compute the optimum thresholds. Then, polynomial regression and artificial neural network models are designed to predict the prioritizing index based on a set of multi-criteria decision-making methods. The weibull distribution is utilized to capture the stochastic nature of deterioration of concrete bridge decks. Lastly, a case study is presented to demonstrate the capabilities of the proposed method.
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