Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Sun, Yang; Feng, Xianjin; Yang, Lingqiang

doi:10.1155/2018/4543984

Cited by 27 publications

(19 citation statements)

References 31 publications

(27 reference statements)

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“…The main goal of the SVM is to find the hyperplane that maximizes the margin between the classes, as shown in Figure 5. A recent study applied SVM to predict tunnel squeezing based on four parameters: diameter, buried depth, support stiffness and the Q Tunnelling Index [69]. An 8-fold cross-validation was used to create multiple models (called an ensemble) to get a measure of the performance of the model.…”

Section: Support Vector Machinementioning

confidence: 99%

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

2019

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Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

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Section: Support Vector Machinementioning

confidence: 99%

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

2019

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“…erefore, in view of the nonlinear and nonstationary characteristics of the immovable relic disease data series, the quantitative correlation between the independent variables of environmental characteristics and the dependent variables of the disease can be obtained by WCA. On the other hand, the disease prediction of immovable cultural relics is still in its infancy; then, the existing prediction methods are all based on the backpropagation neural network (BPNN) [18,19], support vector machine (SVM) [20,21], and relevance vector machine (RVM) [22,23]. However, BPNN is of the gradient descent method to obtain the minimum value of the objective function, which is easy to fall into the local optimum.…”

Section: Introductionmentioning

confidence: 99%

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Liu

et al. 2021

Mathematical Problems in Engineering

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Preventive protection of cultural relics is to make use of all the science and technology beneficial to the research and protection of archaeological heritage to predict the disease of cultural relics. The existing preventive cultural relics protection system has made some achievements in environmental monitoring, but the analysis and utilization of large data of cultural relics are still insufficient. In this paper, under the idea of multisource information fusion, a least squares support vector machine regression method based on multivariate time series wavelet correlation analysis is proposed to achieve accurate crack prediction of stone cultural relics. Firstly, the correlation of multivariate time series of stone cultural relics are quantitatively analyzed and the validity of characteristic variables of the crack is discriminated by wavelet correlation analysis; then, a least squares support vector machine prediction model is constructed based on the correlation obtained from the analysis; finally, the good performance of the method is verified by using the environmental monitoring data of the rock mass fracture in the North Qianfo Cliff of Dafo Temple in Binzhou City of Shaanxi Province. The experimental results show that the proposed method is more effective than the traditional backpropagation neural network, support vector machine, and relevance vector machine regression methods. This method is universal and easy to implement for multisource data prediction of nonmovable cultural relics diseases. It provides a scientific theoretical reference for the preventive protection of cultural relics.

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“…The negative consequences of tunnel squeezing have been reported repeatedly since it was first discovered during the construction of the Simplon Tunnel in Switzerland (Yassaghi and Salari-Rad, 2005). Tunnel squeezing usually causes construction delays, budget overruns, shield blockage and even possibly results in tunnel instability as well as casualties (Sun, et al 2018). Therefore, when designing and constructing tunnels it is very important to adopt a reliable method for predicting rock squeezing surrounding the tunnel.…”

Section: Introductionmentioning

confidence: 99%

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

et al. 2021

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Rock squeezing has a large influence on tunnel construction safety; thus, when designing and constructing tunnels it is highly important to use a reliable method for predicting tunnel squeezing from incomplete data. In this study, a combination SVM-BP (support vector machine-back-propagation) model is proposed to classify the deformation caused by surrounding rock squeezing. We designed different characteristic parameters and three types of classifiers (an SVM model, a BP model, and the proposed SVM-BP model) for the tunnel-squeezing prediction experiments and analysed the accuracy of predictions by different models and the influences of characteristic parameters on the prediction results. In contrast to other prediction methods, the proposed SVM-BP model is verified to be reliable.The results show that four characteristics: tunnel diameter (D), tunnel buried depth (H), rock quality index (Q) and support stiffness (K) reflect the effect of rock squeezing sufficiently for classification.The SVM-BP model combines the advantages of both an SVM and a BP neural network. It possesses flexible nonlinear modelling ability and the ability to perform parallel processing of large-scale information. Therefore, the SVM-BP model achieves better classification performance than do the 2 SVM or BP models separately. Moreover, coupling D, H, and K has a significant impact on the predicted results of tunnel squeezing.

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Predicting Tunnel Squeezing Using Multiclass Support Vector Machines

Cited by 27 publications

References 31 publications

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

Crack Prediction Based on Wavelet Correlation Analysis Least Squares Support Vector Machine for Stone Cultural Relics

Predicting Tunnel Squeezing Using the SVM-BP Combination Model

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