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在土石坝工程中,经高强度反复碾压后的筑坝土石料会达到非常密实的状态,从而表现出明显的超固结特性。在传统未考虑这种特性的土石坝应力变形计算分析中,通常会使低坝的变形计算结果偏大,高坝的变形计算结果偏小。通过设定初始屈服面或初始加载函数的方法,本文发展了一个可考虑坝料初始超固结特性的土石坝变形计算方法,分别结合沈珠江双曲服面模型和邓肯-张EB模型讨论了具体的实现方案。进行了不同坝高、考虑和不考虑坝料初始超固结特性的算例分析,结果表明,所提出的方法可有效解决土石坝变形“低坝算大,高坝算小”的现象。 相似文献
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糯扎渡高心墙堆石坝心墙砾石土料变形参数反演分析 总被引:3,自引:0,他引:3
采用室内中型三轴试验和现场碾压载荷试验对糯扎渡高心墙堆石坝心墙砾石土料的变形特性进行了研究。试验结果表明,采用掺入35%花岗岩碎石的风化混合土料作为心墙防渗土料是合适的,掺入的碎石料可显著提高心墙土料的变形模量,这对减少心墙和堆石体的不均匀沉降和拱效应,防止心墙发生水力劈裂是十分有益的。对现场载荷试验进行了基于神经网络和演化算法的位移反演分析,在有限元计算中提出了通过给定加载函数初始值来描述坝料由碾压过程所致的初始超固结状态的方法。研究结果表明,对糯扎渡心墙砾石土料,由室内三轴试验和现场载荷试验得到的变形特性基本一致。 相似文献
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糯扎渡高心墙堆石坝模型参数动态反演分析 总被引:2,自引:0,他引:2
采用基于人工神经网络与演化算法的土石坝参数反演方法,根据糯扎渡高心墙堆石坝填筑期和蓄水初期坝体变形的现场监测数据进行模型参数动态反演分析,并根据反演分析得到的邓肯-张E-B模型参数进行有限元计算,分析和预测坝体完工期的变形特性。综合考虑施工干扰和仪器精度造成的数据波动等因素,反演计算结果与现场实测结果的数值和变化规律总体符合较好,反演计算结果较为可靠。根据反演参数计算得到的坝体变形分布符合心墙土石坝的一般变形规律,且变形值在正常范围内。 相似文献
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在土石坝工程中,经高强度反复碾压后的筑坝土石料会达到非常密实的状态,从而表现出明显的超固结特性,未考虑这种特性的土石坝应力变形计算分析中,通常会使低坝的变形计算结果偏大,高坝的变形计算结果偏小。通过设定初始屈服面或初始加载函数,本文提出一种可考虑坝料初始超固结特性的土石坝变形计算方法,分别结合沈珠江双曲服面模型和邓肯-张EB模型讨论了具体的实现方案,进行了不同坝高、考虑和不考虑坝料初始超固结特性的算例分析,结果表明,所提出的方法可有效解决土石坝变形“低坝算大,高坝算小”的现象。 相似文献
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Recently, the radial point interpolation meshfree method has gained popularity owing to its advantages in large deformation and discontinuity problems, however, the accuracy of this method depends on many factors and their influences are not fully investigated yet. In this work, three main factors, i.e., the shape parameters, the influence domain size, and the nodal distribution, on the accuracy of the radial point interpolation method (RPIM) are systematically studied and conclusive results are obtained. First, the effect of shape parameters (R, q) of the multi-quadric basis function on the accuracy of RPIM is examined via global search. A new interpolation error index, closely related to the accuracy of RPIM, is proposed. The distribution of various error indexes on the R-q plane shows that shape parameters q ∈ [1.2, 1.8] and R ∈ [0, 1.5] can give good results for general 3-D analysis. This recommended range of shape parameters is examined by multiple benchmark examples in 3D solid mechanics. Second, through numerical experiments, an average of 30-40 nodes in the influence domain of a Gauss point is recommended for 3-D solid mechanics. Third, it is observed that the distribution of nodes has significant effect on the accuracy of RPIM although it has little effect on the accuracy of interpolation. Nodal distributions with better uniformity give better results. Furthermore, how the influence domain size and nodal distribution affect the selection of shape parameters and how the nodal distribution affects the choice of influence domain size are also discussed. 相似文献
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