Risk Assessment of Dams

Güven, Aytaç; Aydemir, Alper

doi:10.1007/978-3-030-47139-2_3

Cited by 2 publications

(3 citation statements)

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“…The presented calculations may constitute valuable supplementation from the side of numerous authors e.g. [31][32][33]; for the rapidly developing domain of dam risk analysis.…”

Section: Uncertainty Of the Mttfmentioning

confidence: 99%

Mean time to failure of dams

Opyrchał

Bąk

2023

Preprint

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Dams are among the largest structures constructed by human. Their disasters result in numerous casualties and large material losses. The statistical analysis of the reasons for dam disasters was carried out by the International Commission on Large Dams. The hazard function h(t) and next, the reliability function R(t) were calculated based on that data, using the fitting of the power function in the infant mortality period and a constant function in the operation period. The knowledge of the reliability function allowed for calculating the mean time to failure which was 112957 ± 12443 years. It was also demonstrated that in the operation period the annual dam failure ratio is 8.719×10− 6 ± 0.297×10− 6. It is a value that is proximate to the recommendations of the U.S. Army Corps of Engineers which suggests the tolerance for the annual dam disaster risk not to exceed 10− 6 for newly-constructed dams and 10− 4 for already existing ones.

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“…The presented calculations may constitute valuable supplementation from the side of numerous authors e.g. [31][32][33]; for the rapidly developing domain of dam risk analysis.…”

Section: Uncertainty Of the Mttfmentioning

confidence: 99%

Mean time to failure of dams

Opyrchał

Bąk

2023

Preprint

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“…Type of dam can be either earth (1), rockfill (2), gravity (3), buttress (4), arch (5), multi-arch (6), roller-compacted concrete (7), concrete (8), masonry (9), stone (10), timber-crib (11), or other (12).…”

Section: Primary Dam Typementioning

confidence: 99%

“…In addition, it was found that 4% of dam failures between 1850 and 2017 were linked with casualties, causing 3495 fatalities in total [7]. In this context, the hazard classification of dams is established based on the potential downstream consequences to property, business, life, and the environment [8,9]. As such, dam hazard classification was introduced to serve as a preliminary mechanism to plan maintenance and rehabilitation needs for existing dams through estimating the magnitude of dam failures.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential

et al. 2022

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There are a large number of dams throughout the United States, and a considerable portion of them are categorized as having high hazard potential. This state of affairs constitutes a challenge, especially when coupled with their rapid deterioration. As such, this research paper proposes an optimized data-driven model for the fast and efficient prediction of dam hazard potential. The proposed model is envisioned on two main components, namely model development and model assessment. In the first component, a hybridization of the differential evolution algorithm and regression tree to forecast downstream dam hazard potential is proposed. In this context, the differential evolution (DE) algorithm is deployed to: (1) automatically retrieve the optimal set of input features affecting dam hazard potential; and (2) amplify the search mechanism of regression tree (REGT) through optimizing its hyper parameters. As for the second component, the developed DE-REGT model is validated using four folds of comparative assessments to evaluate its prediction capabilities. In the first fold, the developed DE-REGT model is trialed against nine highly regarded machine learning and deep learning models. The second fold is designated to structure, an integrative ranking of the investigated data-driven models, counting on their scores in the performance evaluation metrics. The third fold is used to study the effectiveness of using differential evolution for the hyper parameter optimization of regression tree. The fourth fold aims at testing the usefulness of using differential evolution as a feature extractor algorithm. Performance comparative analysis demonstrated that the developed DE-REGT model outperformed the remainder of the data-driven models. It accomplished mean absolute percentage error, relative absolute error, mean absolute error, root squared error, root mean squared error and a Nash–Sutcliffe efficiency of 9.62%, 0.27, 0.17, 0.31, 0.41 and 0.74, respectively. Results also revealed that the developed model managed to perform better than other meta-heuristic-based regression tree models and classical feature extraction algorithms, exemplifying the appropriateness of using differential evolution for hyper parameter optimization and feature extraction. It can be argued that the developed model could assist policy makers in the prioritization of their maintenance management plans and reduce impairments caused by the failure or misoperation of dams.

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Risk Assessment of Dams

Cited by 2 publications

References 0 publications

Mean time to failure of dams

Mean time to failure of dams

Hybrid Differential Evolution-Based Regression Tree Model for Predicting Downstream Dam Hazard Potential

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