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Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods

Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods

Eslam Mohammed Abdelkader, Mohamed Marzouk, Tarek Zayed
Copyright: © 2019 |Volume: 10 |Issue: 2 |Pages: 23
ISSN: 1947-8569|EISSN: 1947-8577|EISBN13: 9781522565734|DOI: 10.4018/IJSDS.2019040105
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MLA

Abdelkader, Eslam Mohammed, et al. "Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods." IJSDS vol.10, no.2 2019: pp.84-106. http://doi.org/10.4018/IJSDS.2019040105

APA

Abdelkader, E. M., Marzouk, M., & Zayed, T. (2019). Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods. International Journal of Strategic Decision Sciences (IJSDS), 10(2), 84-106. http://doi.org/10.4018/IJSDS.2019040105

Chicago

Abdelkader, Eslam Mohammed, Mohamed Marzouk, and Tarek Zayed. "Mapping Ground Penetrating Radar Amplitudes Using Artificial Neural Network and Multiple Regression Analysis Methods," International Journal of Strategic Decision Sciences (IJSDS) 10, no.2: 84-106. http://doi.org/10.4018/IJSDS.2019040105

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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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