岩体分类
灵敏度(控制系统)
理论(学习稳定性)
流离失所(心理学)
趋同(经济学)
支持向量机
算法
超参数
岩土工程
结算(财务)
计算机模拟
数值分析
工程类
地质学
计算机科学
模拟
机器学习
数学
电子工程
经济增长
数学分析
万维网
付款
经济
心理学
心理治疗师
作者
Xiangyu Chang,Hao Wang,Yiming Zhang
标识
DOI:10.1016/j.compgeo.2023.105738
摘要
Efficient determination of the rock mass properties is vitally important for calculating and evaluating tunnel stability in tunnel engineering. The back analysis method has been widely used as an indirect method for determining rock mass parameters based on field measurements. However, most back analysis methods are generally time-consuming for numerical simulation and are merely based on the measured displacement, which leads to the identification of rock mass parameters that cannot fully reflect the characteristics of the surrounding rock. To improve the accuracy of the estimation of rock mass parameters, this paper presents a back analysis method based on multi-output support vector regression (MSVR) and differential evolution (DE) algorithms. Firstly, the global sensitivity analysis of rock mass parameters is analyzed using the elementary effects method. Numerical simulation is then carried out to prepare training samples. DE algorithm is used to determine the optimum hyperparameters of MSVR. Based on the monitoring data, the rock mass properties of the selected sensitive parameters are estimated by the constructed MSVR model. A high-speed railway tunnel is utilized to demonstrate the effectiveness of the MSVR with the DE algorithm (DE-MSVR). The results show that the DE-MSVR with mixed monitoring data of vault settlement, convergence, and rock mass stress has higher forecasting performance than these models with a single type of monitoring data. It is feasible to use the monitoring data at the early stages combined with the numerical simulation for parameter back analysis. Moreover, the comparison results show that the presented method exhibits higher prediction accuracy than the existing back analysis models.
科研通智能强力驱动
Strongly Powered by AbleSci AI