The Experts below are selected from a list of 13998 Experts worldwide ranked by ideXlab platform
Baozhen Yao - One of the best experts on this subject based on the ideXlab platform.
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improved support vector machine regression in multi step ahead prediction for tunnel surrounding Rock Displacement
Scientia Iranica, 2014Co-Authors: Baozhen Yao, Jinbao Yao, Mingheng ZhangAbstract:A dependable long-term prediction of tunnel surrounding Rock Displacement is an effective way to predict the Rock Displacement values into the future. A multi-step-ahead prediction model, which is based on support vector machine (SVM), is proposed for tunnel surrounding Rock Displacement prediction. To improve the performance of SVM, parameter identification is used for SVM. In addition, to treat with the time-varying features of tunnel surrounding Rock Displacement, a forgetting factor is introduced to adjust the weights between new and old data.At last, the data from the Chijiangchong tunnel are selected to examine the performance of the prediction model. Comparative results were presented between SVMFF (SVM with a forgetting factor) and artificial neural network with a forgetting factor (ANNFF) show that SVMFF is generally better than ANNFF. This indicates that a forgetting factor can effectively improve the performance of SVM, especially for the time-varying problems.
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hybrid model for Displacement prediction of tunnel surrounding Rock
Neural Network World, 2012Co-Authors: J B Yao, Baozhen Yao, Y L JiangAbstract:This paper presents a hybrid method to predict tunnel surrounding Rock Displacement, which is one of the most important factors for quality control and safety during tunnel construction. The hybrid method comprises two phases, one is support vector machine (SVM)-based model for predicting the tunnel surrounding Rock Displacement, and the other is GA-based model for optimizing the parameters in the SVM. The proposed model is evaluated with the data of tunnel surrounding Rock Displacement on the tunnel of Wuhan-Guangzhou railway in China. The results show that genetic algorithm (GA) has a good convergence and relative stable performance. The comparison results also show that the hybrid method can generally provide a better performance than artificial neural network (ANN) and finite element method (FEM) for tunnel surrounding Rock Displacement prediction.
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tunnel surrounding Rock Displacement prediction using support vector machine
International Journal of Computational Intelligence Systems, 2010Co-Authors: Baozhen Yao, Chengyong Yang, Jinbao Yao, Jian SunAbstract:Multi-step-ahead prediction of tunnel surrounding Rock Displacement is an effective way to ensure the safe and economical construction of tunnels. This paper presents a multi-step-ahead prediction model, which is based on support vector machine (SVM), for tunnel surrounding Rock Displacement prediction. To improve the training efficiency of SVM, shuffled complex evolution algorithm (SCE-UA) is also performed through some exponential transformation. The data from the Chijiangchong tunnel are used to examine the performance of the prediction model. Results show that SVM is generally better than artificial neural network (ANN). This indicates that SVM is a feasible and effective multi-step method for tunnel surrounding Rock Displacement prediction.
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Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement
Applied Mechanics and Materials, 2010Co-Authors: Baozhen Yao, Chengyong Yang, Fang Fang JiaAbstract:Displacement prediction of tunnel surrounding Rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding Rock Displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding Rock Displacement prediction.
Xinfei Wang - One of the best experts on this subject based on the ideXlab platform.
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ICNC (2) - Application of grey majorized model in tunnel surrounding Rock Displacement forecasting
Lecture Notes in Computer Science, 2005Co-Authors: Yu Zhao, Xiaoguang Jin, Xinfei WangAbstract:Source grey GM(1,1) model usually be used simulation and prediction of equidistant monitoring data sequent. But to non-equidistant and high growth data sequent, had to build the grey GM(1,1) model through equidistant treatment of non-equidistant data or to build directly non-equidistant grey model through complex transfermation , and usually had larger lagging error. In time sequent [k,k+1] interval, in order to majorize and increase accuracy of background value z(1) (k+1), the area of [k,k+1] interval and GM(1,1) function curve envelope had been replaced by n small interval trapezoidal area.The GM(1,1) grey majorized model was built based on majorized grey model background value generally be used simulation and prediction of equidistant or non-equidistant and low or high growth data sequent of surrounding Rock Displacement in tunnel. Data sequent characters of I,II and III shape of surrounding Rock Displacement can be simulated and predicted better by the grey majorized model, and the model had higher simulation and prediction accuracy.
Yu Zhao - One of the best experts on this subject based on the ideXlab platform.
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ICNC (2) - Application of grey majorized model in tunnel surrounding Rock Displacement forecasting
Lecture Notes in Computer Science, 2005Co-Authors: Yu Zhao, Xiaoguang Jin, Xinfei WangAbstract:Source grey GM(1,1) model usually be used simulation and prediction of equidistant monitoring data sequent. But to non-equidistant and high growth data sequent, had to build the grey GM(1,1) model through equidistant treatment of non-equidistant data or to build directly non-equidistant grey model through complex transfermation , and usually had larger lagging error. In time sequent [k,k+1] interval, in order to majorize and increase accuracy of background value z(1) (k+1), the area of [k,k+1] interval and GM(1,1) function curve envelope had been replaced by n small interval trapezoidal area.The GM(1,1) grey majorized model was built based on majorized grey model background value generally be used simulation and prediction of equidistant or non-equidistant and low or high growth data sequent of surrounding Rock Displacement in tunnel. Data sequent characters of I,II and III shape of surrounding Rock Displacement can be simulated and predicted better by the grey majorized model, and the model had higher simulation and prediction accuracy.
Shan Zhong - One of the best experts on this subject based on the ideXlab platform.
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In Situ Observation of Hard Surrounding Rock Displacement at 2400-m-Deep Tunnels
Rock Mechanics and Rock Engineering, 2017Co-Authors: Xia-ting Feng, Zhi-bin Yao, Chengxiang Yang, Hao-sen Guo, Shan ZhongAbstract:This paper presents the results of in situ investigation of the internal Displacement of hard surrounding Rock masses within deep tunnels at China’s Jinping Underground Laboratory Phase II. The Displacement evolution of the surrounding Rock during the entire excavation processes was monitored continuously using pre-installed continuous-recording multi-point extensometers. The evolution of excavation-damaged zones and fractures in Rock masses were also observed using acoustic velocity testing and digital borehole cameras, respectively. The results show four kinds of Displacement behaviours of the hard surrounding Rock masses during the excavation process. The Displacement in the inner region of the surrounding Rock was found to be greater than that of the Rock masses near the tunnel’s side walls in some excavation stages. This leads to a multi-modal distribution characteristic of internal Displacement for hard surrounding Rock masses within deep tunnels. A further analysis of the evolution information on the damages and fractures inside the surrounding Rock masses reveals the effects of excavation disturbances and local geological conditions. This recognition can be used as the reference for excavation and supporting design and stability evaluations of hard-Rock tunnels under high-stress conditions.
Chengyong Yang - One of the best experts on this subject based on the ideXlab platform.
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tunnel surrounding Rock Displacement prediction using support vector machine
International Journal of Computational Intelligence Systems, 2010Co-Authors: Baozhen Yao, Chengyong Yang, Jinbao Yao, Jian SunAbstract:Multi-step-ahead prediction of tunnel surrounding Rock Displacement is an effective way to ensure the safe and economical construction of tunnels. This paper presents a multi-step-ahead prediction model, which is based on support vector machine (SVM), for tunnel surrounding Rock Displacement prediction. To improve the training efficiency of SVM, shuffled complex evolution algorithm (SCE-UA) is also performed through some exponential transformation. The data from the Chijiangchong tunnel are used to examine the performance of the prediction model. Results show that SVM is generally better than artificial neural network (ANN). This indicates that SVM is a feasible and effective multi-step method for tunnel surrounding Rock Displacement prediction.
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Applying Support Vector Machines to Predict Tunnel Surrounding Rock Displacement
Applied Mechanics and Materials, 2010Co-Authors: Baozhen Yao, Chengyong Yang, Fang Fang JiaAbstract:Displacement prediction of tunnel surrounding Rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding Rock Displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding Rock Displacement prediction.
