摘要
在综合分析龙滩左岸边坡泥板岩样室内蠕变试验资料和现场工程区域岩体风化程度、岩性和节理分布等特性的基础上,建立了龙滩左岸边坡泥板岩体的蠕变本构模型;采用均匀设计方法和三维显式有限差分法对72#试验洞三维地质概化模型进行了开挖模拟和蠕变计算;以72#试验洞的变形监测资料为目标,采用遗传–神经网络方法对泥板岩体的蠕变参数进行了智能反演分析,获取了岩体的蠕变参数。利用其进行正演计算,结果表明未用于反演的监测断面的实测位移值与相应断面的计算值在量值上相当,变形趋势上也基本相同。这表明所确定的龙滩水电站左岸高边坡泥板岩体蠕变本构与参数基本合理,同时说明进化神经网络演化有限差分方法在由小尺度岩石室内蠕变试验参数过渡到现场大尺度岩体蠕变参数的过程中起到了重要的桥梁作用。
Based on the comprehensive analysis of the creep experimental data of left bank slope shale samples at Longtan Hydropower Station and the weathered degree of rock masses, rock characteristics and joint distribution in local area, a creep model of shale rock masses of the left bank is set up. The uniform design method and FLAC3D are used to simulate the excavation and creeping process on the simplified geological model of the No.72 test-tunnel, which is located in the central portion of the left bank high slope. With the deformation monitoring data of the No.72 test-tunnel as the aim, the intelligent inversion is made for the creeping parameters by way of combined neural network and genetic algorithm. With these parameters, numerical simulation is conducted. Results indicate that the monitoring displacements not used in inversion are similar to the calculated displacements. This reveals the reasonability of the rheology model and parameters of left bank high slope shale rock masses of Longtan Hydropower Station as well as the important bridging function of intelligent displacement back analysis in the process of the transition from creeping experimental parameters of small-scale rock masses to those of the local large-scale rock masses.
出处
《岩石力学与工程学报》
EI
CAS
CSCD
北大核心
2005年第17期3064-3070,共7页
Chinese Journal of Rock Mechanics and Engineering
基金
国家重点基础研究发展规划(973)项目(2002CB412708)
国家杰出青年科学基金资助项目(50325414)
关键词
岩石力学
龙滩水电站
高边坡
蠕变参数
遗传-神经网络方法
反分析
rock mechanics
Longtan Hydropower Station
high slope
creeping parameters
genetic-neural network
back analysis
