A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods

Zhu, Yantao; Niu, Xinjian; Gu, Chongshi; Dai, Bo; Huang, Lixian

doi:10.1155/2021/7093256

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…The approach considers hazard’s severity and subsystem’s reliability as the input variables to the fuzzy-logic system. Fuzzy logic has already been used for the description of the failure modes, but the applications were site-specific or focused only on dam safety problems (Kutlu and Ekmekçioǧlu 2012 ; Patricio et al 2012 ; Singh and Sarkar 2017 ; Fu et al 2018 ; Yang et al 2020 ; Ribas et al 2021 ; Zhu et al 2021 ; Sang et al 2022 ). Here, a general fuzzy logic-based simulator is developed to generate failure magnitude values on a universal (0–1) scale (applicable to any DRS).…”

Section: Introductionmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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Climate change, energy transition, population growth and other natural and anthropogenic impacts, combined with outdated (unfashionable) infrastructure, can force Dam and Reservoir Systems (DRS) operation outside of the design envelope (adverse operating conditions). Since there is no easy way to redesign or upgrade the existing DRSs to mitigate against all the potential failure situations, Digital Twins (DT) of DRSs are required to assess system’s performance under various what-if scenarios. The current state of practice in failure modelling is that failures (system’s not performing at the expected level or not at all) are randomly created and implemented in simulation models. That approach helps in identifying the riskiest parts (subsystems) of the DRS (risk-based approach), but does not consider hazards leading to failures, their occurrence probabilities or subsystem failure exposure. To overcome these drawbacks, this paper presents a more realistic failure scenario generator based on a causal approach. Here, the novel failure simulation approach utilizes fuzzy logic reasoning to create DRS failures based on hazard severity and subsystems’ reliability. Combined with the system dynamics (SD) model this general failure simulation tool is designed to be used with any DRS. The potential of the proposed method is demonstrated using the Pirot DRS case study in Serbia over a 10-year simulation period. Results show that even occasional hazards (as for more than 97% of the simulation there were no hazards), combined with outdated infrastructure can reduce DRS performance by 50%, which can help in identifying possible “hidden” failure risks and support system maintenance prioritization. Graphical Abstract

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Section: Introductionmentioning

confidence: 99%

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Milašinović

Ivetić

Stojković³

et al. 2023

Water Resour Manage

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“…Terefore, it is of great signifcance to ensure the safety of concrete dams. Te behavior of dam foundation seepage is one of the key factors afecting the safety and stability of concrete dams [1][2][3][4]. On the one hand, the uplift pressure generated by seepage directly acting on the dam foundation is one of the important unfavorable loads afecting the structural stability of the gravity dam; on the other hand, under the long-term efect of seepage, the joint fssure of the rock mass around the dam foundation may transform into a weak interlayer [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…Te behavior of dam foundation seepage is one of the key factors afecting the safety and stability of concrete dams [1][2][3][4]. On the one hand, the uplift pressure generated by seepage directly acting on the dam foundation is one of the important unfavorable loads afecting the structural stability of the gravity dam; on the other hand, under the long-term efect of seepage, the joint fssure of the rock mass around the dam foundation may transform into a weak interlayer [4][5][6]. Te seepage pressure is an important manifestation of dam foundation seepage, so it is of great signifcance to establish a high-performance concrete dam foundation seepage pressure prediction model [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Coupled Model for Dam Foundation Seepage Behavior Monitoring and Forecasting Based on Variational Mode Decomposition and Improved Temporal Convolutional Network

Zhu

Zhang

et al. 2023

Structural Control and Health Monitoring

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Grasping the change behavior of dam foundation seepage pressure is of great significance for ensuring the safety of concrete dams. Because of the environmental complexity of the dam location, the prototypical seepage pressure data are easy to be contaminated by noise, which brings challenges to accurate prediction. Traditional denoising methods will lose the detailed characteristics of the objects, resulting in prediction models with limited flexibility and prediction accuracy. To address these problems, the prototypical data with noise are denoised using the variational mode decomposition (VMD)-wavelet packet denoising method. Then, an improved temporal convolutional network (ITCN) model is built for dam foundation seepage pressure data prediction. A hysteresis experiment is carried out to optimize the model structure by correlating the receptive field size of the ITCN model with the hysteresis of the dam foundation seepage pressure. Finally, the optimal ITCN dam foundation seepage pressure prediction model of each measurement point is obtained after the training. Three state-of-the-art methods in dam seepage monitoring are used as benchmark methods to compare the prediction performance of the proposed method. Four evaluation indicators are introduced to quantitatively evaluate and compare the prediction performance of the proposed method. The experimental results prove that the proposed method achieves high prediction accuracy flexibility. The indicator values of the ITCN model are only 50%–90% of those of LSTM and RNN models and 15%–40% of those of the stepwise regression model, and the values are all small.

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Risk Management Decision of Reservoir Dams Based on the Improved Life Quality Index

Zhang

et al. 2023

Water Resour Manage

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A Fuzzy Clustering Logic Life Loss Risk Evaluation Model for Dam-Break Floods

Cited by 9 publications

References 19 publications

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

Failure Conditions Assessment of Complex Water Systems Using Fuzzy Logic

A Coupled Model for Dam Foundation Seepage Behavior Monitoring and Forecasting Based on Variational Mode Decomposition and Improved Temporal Convolutional Network

Risk Management Decision of Reservoir Dams Based on the Improved Life Quality Index

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