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反求堤坝渗流计算参数的复合形法 被引量:4
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作者 汪自力 杨静熙 《大连理工大学学报》 EI CAS CSCD 北大核心 1993年第S1期41-45,共5页
以堤坝渗流分析为基础,根据典型堤段的测压管观测资料及现场勘测和 室内试验结果,运用反问题分析中的间接分析法,将数值分析中的有限元法 和数学规划中的复合形法结合起来,通过不断修正土的渗透系数,使得一些 观测值与相应的计算... 以堤坝渗流分析为基础,根据典型堤段的测压管观测资料及现场勘测和 室内试验结果,运用反问题分析中的间接分析法,将数值分析中的有限元法 和数学规划中的复合形法结合起来,通过不断修正土的渗透系数,使得一些 观测值与相应的计算值差异最小,从而使堤段的渗流计算结果更切合实际. 算例表明了此种算法的有效性. 展开更多
关键词 渗流 有限元 优化/反分析
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RandWPSO-LSSVM optimization feedback method for large underground cavern and its engineering applications 被引量:2
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作者 聂卫平 徐卫亚 刘兴宁 《Journal of Central South University》 SCIE EI CAS 2012年第8期2354-2364,共11页
According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flo... According to the characteristics of large underground caverns, by using the safety factor of surrounding rock mass point as the control standard of cavern stability, RandWPSO-LSSVM optimization feedback method and flow process of large underground cavern anchor parameters were established. By applying the optimization feedback method to actual project, the best anchor parameters of large surge shaft five-tunnel area underground cavern of the Nuozhadu hydropower station were obtained through optimization. The results show that the predicted effect of LSSVM prediction model obtained through RandWPSO optimization is good, reasonable and reliable. Combination of the best anchor parameters obtained is 114131312, that is, the locked anchor bar spacing is 1 m x 1 m, pre-stress is 100 kN, elevation 580.45-586.50 m section anchor bar diameter is 36.00 mm, length is 4.50 m, spacing is 1.5 m × 2.5 m; anchor bar diameter at the five-tunnel area side wall is 25.00 mm, length is 7.50 m, spacing is 1 m× 1.5 m, and the shotcrete thickness is 0.15 m. The feedback analyses show that the optimization feedback method of large underground cavern anchor parameters is reasonable and reliable, which has important guiding significance for ensuring the stability of large underground caverns and for saving project investment. 展开更多
关键词 random weight particle swarm optimization least squares support vector machine large undergrotmd cavern anchor oarameters optimization feedback rock-ooint safety factor
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