• 全国中文核心期刊
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CHEN Bo, CHEN Wei-nan, GONG Xue-xian, HE Ming-ming. Prediction model for deformation of concrete dams in cold regions considering hysteresis and reduction effects from environment[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(12): 2315-2322. DOI: 10.11779/CJGE202112019
Citation: CHEN Bo, CHEN Wei-nan, GONG Xue-xian, HE Ming-ming. Prediction model for deformation of concrete dams in cold regions considering hysteresis and reduction effects from environment[J]. Chinese Journal of Geotechnical Engineering, 2021, 43(12): 2315-2322. DOI: 10.11779/CJGE202112019

Prediction model for deformation of concrete dams in cold regions considering hysteresis and reduction effects from environment

  • Under the influences of the extreme temperature loads, the service behavior of concrete dams in cold regions with high latitude and high altitude is complex, so it is very difficult to excavate, interpret and accurately predict the deformation behavior of dams. The hysteresis and amplitude reduction effects are introduced to analyze the influences of the external temperature on the internal temperature field of the dam body, and an equivalent model is established to analyze the internal temperature field of the dam body with and without insulation layer. The influences of the complex extreme temperature loads on dam deformation in cold regions are simulated by Rayleigh distribution hysteresis effect function, and a prediction model for the deformation of concrete dams in cold regions is established cosidering continuous hysteresis and reduction effects from temperature. The model parameters are optimized by the PSO algorithm with improved inertia weight. The simulation examples and engineering practice show that the temperature variation law of concrete dam body in cold regions is reasonable. The proposed prediction model agrees with the working characteristics of concrete dams in cold regions and the complex correlation coefficient and residual standard deviation are superior to those by the traditional models, with better fitting effect and higher prediction accuracy.
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