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机构地区:[1]广东省电力设计研究院,广东广州510663 [2]清华大学土木水利学院,北京100084 [3]河海大学岩土工程科学研究所,江苏南京210098
出 处:《中南大学学报(自然科学版)》2013年第4期1626-1633,共8页Journal of Central South University:Science and Technology
基 金:国家自然科学基金资助项目(51109069)
摘 要:根据粒子群算法(PSO)和支持向量机(SVM)的特点和局限性,对其进行改进,建立随机权重粒子群最小二乘支持向量机(RandWPSO-LSSVM)反演模型,由正交设计、均匀设计和三维有限元数值计算给出学习和测试样本,反演糯扎渡水电站大型调压井工程区的围岩力学参数和初始地应力场。研究表明:RandWPSO对LSSVM预测模型的优化效果明显比PSO好;反演得到工程区x向和y向侧压力系数分别为2.641 5和2.083 1,微新岩体、弱风化下层、弱风化上层、全强风化层岩体、断层等围岩的弹性模量分别为24.849 2,10.898 7,2.839 8,0.270 4和0.651 3 GPa;反馈计算得到的位移相对实测位移误差较小,验证反演参数的合理性,也表明所建立的RandWPSO-LSSVM反演模型合理可靠,可有效指导大型地下工程参数设计和施工稳定性分析。The PSO and SVM were improved because of their characteristics and limitations, and the RandWPSO-LSSVM inversion model was established. After the learning and test samples were obtained by orthogonal experimental design, uniform experimental design and three-dimensional finite-element computation, the mechanics parameter of adjoining rock and initial stress field of large-scale surge shaft engineering of Nuozhadu hydro-power station were inversed. The results show that the optimization effect of RandWPSO is better than that of PSO for the LSSVM prediction model. Through inversing, x direction lateral pressure coefficient is 2.641 5 and y direction lateral pressure coefficient is 2.083 1, and the modulus of the fresh rock mass, the upper bed of slightly weathered rock mass, the lower course of slightly weathered rock mass, the strong weathered rock mass and the fault are 24.849 2, 10.898 7, 2.839 8, 0.270 4 and 0.651 3 GPa, respectively. The error between displacement obtained by feedback calculation and displacement obtained by measurement is little, therefore, the parameter obtained by inversion and the RandWPSO-LSSVM inversion model are established reasonably, and the parameter design and stability analysis in construction of large-scale underground engineering could be guided effectively.
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