Inverse Analysis of Surrounding Rock Mechanical Parameters Based on LS-SVM

2011 ◽  
Vol 422 ◽  
pp. 547-550
Author(s):  
Xiao Long Li ◽  
Fu Ming Wang ◽  
Yan Hui Zhong ◽  
Cheng Chao Guo
Keyword(s):  
Support Vector Machine ◽  
Inverse Analysis ◽  
Back Analysis ◽  
Surrounding Rock ◽  
Support Vector ◽  
Inversion Method ◽  
Mechanical Parameters ◽  
Underground Engineering ◽  
Standard Support Vector Machine ◽  
Calculation Results

Inverse analysis is regarded as an ideal way to achieve the mechanical parameters of rock mass using in situ measured deformation data of surrounding rock during the construction of underground engineering. Aiming at the disadvantage of high computational complexity when identifying mechanical parameters of surrounding rock by employing the inverse method based on standard support vector machine (Vapnik’s SVM), a new back analysis method based on least squares support vector machine (LS-SVM) was presented. The basic principle of the method was introduced. An example was adopted to investigate the practicality and reliability of the method, and the calculation results indicated that, compared with the inversion method based on standard SVM, the method proposed in this paper possesses higher calculation efficiency and inversion precision.

Analysis for Incremental and Decremental Standard Support Vector Machine

2014 ◽  
Vol 24 (7) ◽  
pp. 1601-1613 ◽  
Author(s):  
Bin GU ◽  
Guan-Sheng ZHENG ◽  
Jian-Dong WANG
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Standard Support Vector Machine ◽  
Standard Support

Identification of Surrounding Rock Parameters Based on MSVR

2014 ◽  
Vol 580-583 ◽  
pp. 1227-1231
Author(s):  
Xiao Long Li ◽  
Jun Jing Zhang ◽  
Fu Ming Wang ◽  
Bei Zhang
Keyword(s):  
Support Vector Regression ◽  
Surrounding Rock ◽  
Orthogonal Test ◽  
Support Vector ◽  
Inversion Method ◽  
Single Output ◽  
Output System ◽  
Rock Parameters ◽  
Rock Deformations ◽  
Second Use

An inversion method based on multi-output support vector regression (MSVR) is proposed for identifying the mechanical parameters of surrounding rock. This method considers the surrounding rock as a multi-output system during excavation, and the surveyed rock deformations of each monitoring section as its output. First, perform numerical experiments based on the principle of orthogonal test to obtain the calculated deformation values corresponding to different rock parameter combinations, and use them as the samples for training the model of MSVR as reflecting the nonlinear mapping relationship between rock and its deformations. Second, use the PSO to seek the optimal rock parameters based on measured deformations of rock mass. An example is employed to test the presented inversion method. The results showed that compared with the inversion method based on single-output support vector regression (SSVR), the proposed one is more inclined to reach the global optimization goals and achieve more reliable inversion results due to its consideration of the inherent correlativity among the measured deformations of each monitoring section.

Probabilistic back analysis based on Bayesian and multi-output support vector machine for a high cut rock slope

2016 ◽  
Vol 203 ◽  
pp. 178-190 ◽  
Author(s):  
Shaojun Li ◽  
Hongbo Zhao ◽  
Zhongliang Ru ◽  
Qiancheng Sun
Keyword(s):  
Support Vector Machine ◽  
Rock Slope ◽  
Back Analysis ◽  
Support Vector

Design and implementation of construction cost prediction model based on SVM and LSSVM in industries 4.0

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Miao Fan ◽  
Ashutosh Sharma
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Original Data ◽  
Construction Cost ◽  
Support Vector ◽  
Competitive Growth ◽  
Cost Prediction ◽  
Content Type ◽  
Model Based ◽  
Standard Support Vector Machine

PurposeIn order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.Design/methodology/approachIn the competitive growth and industries 4.0, the prediction in the cost plays a key role.FindingsAt the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.Originality/valueThe prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

scholarly journals Back Analysis of Geomechanical Parameters in Underground Engineering Using Artificial Bee Colony

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Changxing Zhu ◽  
Hongbo Zhao ◽  
Ming Zhao
Keyword(s):  
Analytical Solution ◽  
Artificial Bee Colony ◽  
Optimal Algorithm ◽  
Back Analysis ◽  
Least Square ◽  
Support Vector ◽  
Underground Engineering ◽  
Bee Colony ◽  
Geomechanical Parameters ◽  
The Relationship

Accurate geomechanical parameters are critical in tunneling excavation, design, and supporting. In this paper, a displacements back analysis based on artificial bee colony (ABC) algorithm is proposed to identify geomechanical parameters from monitored displacements. ABC was used as global optimal algorithm to search the unknown geomechanical parameters for the problem with analytical solution. To the problem without analytical solution, optimal back analysis is time-consuming, and least square support vector machine (LSSVM) was used to build the relationship between unknown geomechanical parameters and displacement and improve the efficiency of back analysis. The proposed method was applied to a tunnel with analytical solution and a tunnel without analytical solution. The results show the proposed method is feasible.

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