混凝土面板堆石坝施工期面板温控措施优选

发布时间：2018-07-31 08:52

【摘要】：混凝土面板堆石坝主要靠上游面板挡水,施工期面板裂缝是影响面板堆石坝质量和安全的关键因素之一。面板混凝土属于准大体积混凝土,尽管其散热面较大、最高水化热温升不是很高,但如果不采取合理的温控防裂措施,仍然会导致面板产生温度裂缝。针对施工期面板混凝土温度裂缝问题,以某在建高混凝土面板堆石坝工程为例,建立了包含地基的三维有限元温度场和徐变温度应力场仿真模型,利用数值方法产生神经网络的学习样本,然后采用遗传算法优化的BP神经网络对所获得的样本进行网络训练,从而获得温控措施优选网络模型,进行已知混凝土热力学材料参数情况下的温控措施优选。由神经网络优选结果可知,本文所采用的面板混凝土温控措施优选方法是合理可行的。
[Abstract]:The concrete face rockfill dam mainly depends on the upstream face slab to keep water, and the crack of the face slab is one of the key factors that affect the quality and safety of the concrete face rockfill dam during the construction period. Slab concrete is a kind of quasi-mass concrete. Although its heat dissipation surface is large and the maximum hydration heat temperature rise is not very high, if reasonable temperature control and crack prevention measures are not taken, it will still lead to temperature cracks in the face slab. In order to solve the problem of temperature crack of concrete face slab during construction period, taking a high concrete face rockfill dam under construction as an example, a three dimensional finite element temperature field and creep temperature stress field simulation model including foundation is established. The learning samples of neural network are generated by numerical method, and then the BP neural network optimized by genetic algorithm is used to train the obtained samples, and the optimal network model of temperature control measures is obtained. The optimal selection of temperature control measures for concrete with known thermodynamic material parameters is carried out. According to the results of neural network optimization, it is reasonable and feasible to select the temperature control measures for concrete face slab in this paper.
【作者单位】：湖北省洪湖分蓄洪区工程管理局;三峡大学水利与环境学院;湖北省水利水电规划勘测设计院;三峡大学科技学院;
【基金】：国家自然科学基金资助项目(51209124)
【分类号】：TV544

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