The South-to-North Water Diversion Project will pose a great threat to the lives and property safety of people along the route once a dangerous situation occurs. Therefore, this paper analyzes the historical risk data of the South-to-North Water Transfer Project to identify risk sources, combs the actual risk occurrence process with the basic structure of "Risk Factor→Destruction Link→Destruction Mode", and builds a main canal system risk interpretation model. At the same time, taking the typical high-fill section of the main canal of the South-to-North Water Transfer Project as an example, a typical failure evolution process is selected to construct a dynamic simulation model based on system dynamics to simulate the failure process of the canal embankment under three kinds of manual intervention, as the channel operation under risk input scheduling decisions provides new ideas.
To solve the problem of the high cost of transient temperature simulation in the whole construction process of an asphalt-concrete core wall, a novel adaptive degree of freedom condensation algorithm for simulating transient temperature is proposed. This method establishes the judgment criterion of degree of freedom condensation based on the error estimator of mesh and the artificial energy added by degree of freedom condensation. In this method, the transformation matrix between the master and slave degrees of freedom is constructed based on the shape function interpolation relationship between the initial coarse mesh and the multi-level refined mesh. In the transient calculation process, this method can automatically identify the positions where temperature distribution and value are stable and condense the considered slave degrees of freedom to master degrees of freedom through the transformation matrix at any time to reduce the unnecessary degrees of freedom. In this paper, three numerical examples show that the proposed method can effectively reduce the cost of matrix factorization and the solving the equation in the finite element method at the cost of small precision loss in the long-term transient temperature simulation.
Earthen dams operate in complex environments where their safety is often affected by multiple uncertain risks. A Bayesian network (BN) is often used to analyze the dam failure risk, which is an effective tool for this issue as its excellent ability in representing uncertainty and reasoning. The validity of the BN model is strongly dependent on the quality of the sample data, making convincing modeling rationale a challenge, which limits its use. There has been a lack of systematic analysis of the dam failure data of China, which further leads to a lack of in-depth exploration of potential associations between risk factors. In this paper, we established a comprehensive database containing various dam failure cases in China. Herein, historical dam failure statistics are used to develop BN models for risk analysis of earthen dams in China. In order to unleash the value of the historical data, we established a Bayesian network through the Causal Loop Diagrams (CLD) based on the nonlinear causal analysis. We determined the conditional probabilities using Word Frequency Analysis (WFA). By comparing with the Bayesian learning results, the modeling method of BN proposed in our study has apparent advantages. According to the BN model established in this paper, the probabilities of dam failure with three damage modes of seepage damage, overtopping and structural instability are 22.1%, 58.1%, and 7.9%, respectively. In addition, we demonstrated how to perform the inference process of the dam failure path. This will provide helpful information for dam safety practitioners in their decision-making process.
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