Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint mode...Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.展开更多
In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximati...In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximating process.展开更多
利用少量传感器融合机器学习技术进行系统多故障诊断是实现气动系统低成本智能化故障诊断的潜在途径。以气动系统中常见的泄漏故障为例,探究了利用上游单点测量信息实现下游并联双气缸泄漏故障诊断的可行性。上游单点测量信息包括压力...利用少量传感器融合机器学习技术进行系统多故障诊断是实现气动系统低成本智能化故障诊断的潜在途径。以气动系统中常见的泄漏故障为例,探究了利用上游单点测量信息实现下游并联双气缸泄漏故障诊断的可行性。上游单点测量信息包括压力、流量和[火用]数据,预处理后的数据通过栈式自编码器(Stacked Autoencoder,SAE)进行特征提取,并将提取的特征送入高斯过程分类器(Gaussian Process Classifier,GPC)中进行学习分类。实验结果表明:通过机器学习模型学习分析上游单点测量信号来实现对下游并联双气缸泄漏故障的诊断和定位是可行的;在本实验同等条件下,基于[火用]数据的平均分类准确率达到100%,高于基于流量数据的98.99%和基于压力数据的77.38%。展开更多
基金This work was supported by the National Natural Science Foundation of China(Nos.51678101,52078093)Liaoning Revitalization Talents Program(No.XLYC1905015).
文摘Due to the geological body uncertainty,the identification of the surrounding rock parameters in the tunnel construction process is of great significance to the calculation of tunnel stability.The ubiquitous-joint model and three-dimensional numerical simulation have advantages in the parameter identification of surrounding rock with weak planes,but conventional methods have certain problems,such as a large number of parameters and large time consumption.To solve the problems,this study combines the orthogonal design,Gaussian process(GP)regression,and difference evolution(DE)optimization,and it constructs the parameters identification method of the jointed surrounding rock.The calculation process of parameters identification of a tunnel jointed surrounding rock based on the GP optimized by the DE includes the following steps.First,a three-dimensional numerical simulation based on the ubiquitous-joint model is conducted according to the orthogonal and uniform design parameters combing schemes,where the model input consists of jointed rock parameters and model output is the information on the surrounding rock displacement and stress.Then,the GP regress model optimized by DE is trained by the data samples.Finally,the GP model is integrated into the DE algorithm,and the absolute differences in the displacement and stress between calculated and monitored values are used as the objective function,while the parameters of the jointed surrounding rock are used as variables and identified.The proposed method is verified by the experiments with a joint rock surface in the Dadongshan tunnel,which is located in Dalian,China.The obtained calculation and analysis results are as follows:CR=0.9,F=0.6,NP=100,and the difference strategy DE/Best/1 is recommended.The results of the back analysis are compared with the field monitored values,and the relative error is 4.58%,which is satisfactory.The algorithm influencing factors are also discussed,and it is found that the local correlation coefficientσf and noise standard deviationσn affected the prediction accuracy of the GP model.The results show that the proposed method is feasible and can achieve high identification precision.The study provides an effective reference for parameter identification of jointed surrounding rock in a tunnel.
文摘In the paper, the deviation of the spline estimator for the unknown probability density is approximated with the Gauss process. It is also found zeros for the infimum of variance of the derivation from the approximating process.
文摘利用少量传感器融合机器学习技术进行系统多故障诊断是实现气动系统低成本智能化故障诊断的潜在途径。以气动系统中常见的泄漏故障为例,探究了利用上游单点测量信息实现下游并联双气缸泄漏故障诊断的可行性。上游单点测量信息包括压力、流量和[火用]数据,预处理后的数据通过栈式自编码器(Stacked Autoencoder,SAE)进行特征提取,并将提取的特征送入高斯过程分类器(Gaussian Process Classifier,GPC)中进行学习分类。实验结果表明:通过机器学习模型学习分析上游单点测量信号来实现对下游并联双气缸泄漏故障的诊断和定位是可行的;在本实验同等条件下,基于[火用]数据的平均分类准确率达到100%,高于基于流量数据的98.99%和基于压力数据的77.38%。
