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Published on: February 1, 2020
Uncertainty analysis in probabilistic design of detention rockfill dams using Monte-Carlo simulation model and
Mohammad Mehdi Riyahi1, Hossien Riahi-Madvar2
1Department of Civil Engineering, Faculty of Civil Engineering and Architecture, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Abstract:
The detention rockfill dams are of promising importance in flood control projects, due to their minimal technical requirement, low cost, minimal environmental side effects, and self-automotive operation process. However, due to the complexity of Non-Darcian flow interactions with stability and uncertainties of dam, the reliable design is a challenging topic. This study aimed to examine the effects of uncertainties in probabilistic design of these dams. We proposed a reliable design framework for detention rockfill dams with a focus on the importance of stability analysis. The effects of design uncertainty sources on the stability of dam, safety factors of overturning, sliding and bearing, along with the hydraulic performance of the dam were examined. The results of the model revealed that the uncertainties in input parameters can effectively regenerate uncertainties in the hydraulic performance ranges from - 53.54 to + 110.11%. The safety factor against the sliding (SFS) has maximum dependencies with the uncertainties ranging - 32.63 to + 87.81%. The Monte-Carlo Simulation (MCS) and fitting probability distribution functions to the safety factor histograms, and uncertainty quantifications results in 88.3%in increasing the safety factors as a reliable methodology for stability design of detention rockfill dams. Thus, the study calls for reliable, certain, and safe design of flood protection rockfill ponds. The ecological evaluation and applying more advanced uncertainty assessment methods remains a future research direction of the current study. The developed framework can be used to acquire future detention rockfill dam design/modeling requirements for reliability-based design optimization as a simulation-optimization model coupled whit MCS.
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