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Volume 52 Issue 10
Oct.  2022
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ZHOU Hongwen, DONG Wenjun, ZHOU Bentao. Research on Destructiveness of Dam Failure of Tailings Reservoirs Based on Two-Dimensional Cloud Model[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 229-235. doi: 10.13204/j.gyjzG22072610
Citation: ZHOU Hongwen, DONG Wenjun, ZHOU Bentao. Research on Destructiveness of Dam Failure of Tailings Reservoirs Based on Two-Dimensional Cloud Model[J]. INDUSTRIAL CONSTRUCTION, 2022, 52(10): 229-235. doi: 10.13204/j.gyjzG22072610

Research on Destructiveness of Dam Failure of Tailings Reservoirs Based on Two-Dimensional Cloud Model

doi: 10.13204/j.gyjzG22072610
  • Received Date: 2022-07-26
    Available Online: 2023-03-22
  • This study is designed to accurately assess the probability of impact damage to downstream structures caused by debris flows after the dam failure of tailings reservoirs and the severity of the damage. Specifically, an impact risk assessment system for bridge projects was constructed according to the characteristics of the dam failure disaster of tailings reservoirs, the rheological characteristics of the debris flow after dam failure, and simulation technology. On the basis of this system, the two-dimensional (2D) cloud model theory was employed to generate the probability cloud and the severity cloud. For this purpose, weights were obtained with information entropies. Then, the evaluation cloud map was generated by the backward cloud model, and the left and right half clouds were generated by the standard cloud model. Finally, the similarity between the evaluation cloud and the standard cloud was determined by cloud similarity, and the actual safety status of each tailings reservoir was obtained by investigating the weights. A bridge project under impact damage by the debris flow after dam failure was discussed as an example of empirical research. The results show that the proposed risk assessment index system is highly practical, and the improved 2D cloud model can correct the weakness of the traditional cloud model that it directly removes the data at the interval breakpoint and thereby enhance the accuracy of the evaluation results and the comprehensiveness of the risk assessment process.
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