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Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm
https://orcid.org/http://orcid.org/0000-0003-4769-4487
The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is significant to propose a model for accurate prediction of rock squeezing. In this research, the support vector machine (SVM) as a machine learning model was optimized by the whale optimization algorithm (WOA), WOA-SVM, to classify the tunnel squeezing based on 114 real cases. The role of WOA in this system is to optimize the hyper-parameters of SVM model for receiving a higher level of accuracy. In the established database, five input parameters, i.e., buried depth, support stiffness, rock tunneling quality index, diameter and the percentage strain, were used. In the process of model classification, different effective parameters of SVM and WOA were considered, and the optimum parameters were designed. To examine the accuracy of the WOA-SVM, the base SVM, ANN (refers to the multilayer perceptron) and GP (refers to the Gaussian process classification) were also constructed. Evaluation of these models showed that the optimized WOA-SVM is the best model among all proposed models in classifying the tunnel squeezing. It has the highest accuracy (approximately 0.9565) than other un-optimized individual classifiers (SVM, ANN, and GP). This was obtained based on results of different performance indexes. In addition, according to sensitivity analysis, the percentage strain is highly sensitive to the model, followed by buried depth and support stiffness. That means, ɛ, H and K are the best combination of parameters for the WOA–SVM model.
Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm
https://orcid.org/http://orcid.org/0000-0003-4769-4487
The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is significant to propose a model for accurate prediction of rock squeezing. In this research, the support vector machine (SVM) as a machine learning model was optimized by the whale optimization algorithm (WOA), WOA-SVM, to classify the tunnel squeezing based on 114 real cases. The role of WOA in this system is to optimize the hyper-parameters of SVM model for receiving a higher level of accuracy. In the established database, five input parameters, i.e., buried depth, support stiffness, rock tunneling quality index, diameter and the percentage strain, were used. In the process of model classification, different effective parameters of SVM and WOA were considered, and the optimum parameters were designed. To examine the accuracy of the WOA-SVM, the base SVM, ANN (refers to the multilayer perceptron) and GP (refers to the Gaussian process classification) were also constructed. Evaluation of these models showed that the optimized WOA-SVM is the best model among all proposed models in classifying the tunnel squeezing. It has the highest accuracy (approximately 0.9565) than other un-optimized individual classifiers (SVM, ANN, and GP). This was obtained based on results of different performance indexes. In addition, according to sensitivity analysis, the percentage strain is highly sensitive to the model, followed by buried depth and support stiffness. That means, ɛ, H and K are the best combination of parameters for the WOA–SVM model.
Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm
https://orcid.org/http://orcid.org/0000-0003-4769-4487
The squeezing behavior of surrounding rock can be described as the time-dependent large deformation during tunnel excavation, which appears in special geological conditions, such as weak rock masses and high in situ stress. Several problems such as budget increase and construction period extension can be caused by squeezing in rock mass. It is significant to propose a model for accurate prediction of rock squeezing. In this research, the support vector machine (SVM) as a machine learning model was optimized by the whale optimization algorithm (WOA), WOA-SVM, to classify the tunnel squeezing based on 114 real cases. The role of WOA in this system is to optimize the hyper-parameters of SVM model for receiving a higher level of accuracy. In the established database, five input parameters, i.e., buried depth, support stiffness, rock tunneling quality index, diameter and the percentage strain, were used. In the process of model classification, different effective parameters of SVM and WOA were considered, and the optimum parameters were designed. To examine the accuracy of the WOA-SVM, the base SVM, ANN (refers to the multilayer perceptron) and GP (refers to the Gaussian process classification) were also constructed. Evaluation of these models showed that the optimized WOA-SVM is the best model among all proposed models in classifying the tunnel squeezing. It has the highest accuracy (approximately 0.9565) than other un-optimized individual classifiers (SVM, ANN, and GP). This was obtained based on results of different performance indexes. In addition, according to sensitivity analysis, the percentage strain is highly sensitive to the model, followed by buried depth and support stiffness. That means, ɛ, H and K are the best combination of parameters for the WOA–SVM model.
Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm
Acta Geotech.
Zhou, Jian (author) / Zhu, Shuangli (author) / Qiu, Yingui (author) / Armaghani, Danial Jahed (author) / Zhou, Annan (author) / Yong, Weixun (author)
Acta Geotechnica ; 17 ; 1343-1366
2022-04-01
24 pages
Article (Journal)
Electronic Resource
English
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