Evaluating Cracks in Concrete Dams using Meta-heuristic Algorithms and Artificial Neural Networks
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Necessity to a complete and accurate analysis of the crack behavior in concrete dams using new methods is felt due to the sensitivity of the cracking problem in these dams. Meanwhile, meta-heuristic algorithms have a very good performance and accuracy in evaluating and predicting problems rather than other methods. In this study, Zayandehrood arch concrete dam has been chosen as the case study and the displacements in the cracks of this dam have been investigated by using election algorithm (EA). Water level and concrete temperature from 2000 to 2013 were considered as input parameters and also horizontal and vertical displacement of cracks were selected as output parameters. The results were compared with genetic algorithm (GA) and artificial neural networks (ANN). To evaluate the performance of the proposed method, three statistical criteria including correlation coefficient (R2), root mean square error (RMSE) and Nash-Sutcliff efficiency (NSE) were utilized. The results show that EA has a higher efficiency with R2 = 0.96, RMSE = 0.022 and NSE = 0.74, compared to GA and ANN. However, due to the lack of sufficient data, the amount of regression coefficient for spillway cracks was lower than the dam cracks. It is concluded that for evaluating the displacements of cracks in concrete dams and predicting their variations in future, meta-heuristic algorithms can be utilized as a very exact and powerful method. These methods can help dam managers and decision-makers in monitoring and vulnerability analysis of dams during their operation.
Keywords:
Language:
Persian
Published:
Journal of Structural and Construction Engineering, Volume:8 Issue: 6, 2021
Pages:
194 to 215
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