Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

Zhou, Jian; Zhu, Shuangli; Qiu, Yingui; Armaghani, Danial Jahed; Zhou, Annan; Yong, Weixun

doi:10.1007/s11440-022-01450-7

Cited by 80 publications

(17 citation statements)

References 85 publications

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“…On the testing dataset, the KNN's forecasting outcomes were obtained. Various performance indices including precision, recall, and F1-score have been employed by the researchers to evaluate the performance of a classi cation model [77]. In this study, precision, recall, and F1-score have been used to predict the outcomes of the proposed KNN algorithm when associated with ISOMAP and FCM.…”

Section: Resultsmentioning

confidence: 99%

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Kamran

Ullah

Ahmad

et al. 2022

Preprint

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The rockburst phenomenon is the major source of the high number of casualties and fatalities during the construction of deep underground projects. Rockburst poses a severe hazard to the safety of employees and equipment in subsurface mining operations. It is a hot topic in recent years to examine and overcome rockburst risks for the safe installation of deep urban engineering designs. Therefore, for a cost-effective and safe underground environment, it is crucial to determine and predict rockburst intensity prior to its occurrence. A novel model is presented in this study that combines unsupervised and supervised machine learning approaches in order to predict rockburst risk. The database for this study was built using authentic microseismic monitoring occurrences from the Jinping-II hydropower project in China, which consists of 93 short-term rockburst occurrences with six influential features. The prediction process was succeeded in three steps. Firstly, the original rockburst database's magnification was reduced using a state-of-the-art method called isometric mapping (ISOMAP) algorithm. Secondly, the dataset acquired from ISOMAP was categorized using the fuzzy c-means algorithm (FCM) to reduce the minor spectral heterogeneity impact in homogenous areas. Thirdly, K-Nearest neighbour (KNN) was employed to anticipate different levels of short-term rockburst datasets. The KNN's classification performance was examined using several performance metrics. The proposed model correctly classified about 96% of the rockbursts events in the testing datasets. Hence, the suggested model is a realistic and effective tool for evaluating rockburst intensity. Therefore, the proposed model can be employed to forecast the rockburst risk in the early stages of underground projects that will help to minimize casualties from rockburst.

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Section: Resultsmentioning

confidence: 99%

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Kamran

Ullah

Ahmad

et al. 2022

Preprint

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“…Many ensemble tree models, i.e., GBM, XGB, and RF, perform better than other ML models. Taylor diagrams [58,59] were introduced to determine the strength of the DF model compared to other models. In this study, Taylor diagrams combine the Matthews correlation coefficient (MCC), centered root mean square error (green dotted lines in Figure 11), and standard deviation into a polar diagram.…”

Section: Model Performance Comparisonmentioning

confidence: 99%

Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

Liu

Armaghani

et al. 2022

Mathematics

Self Cite

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The occurrence of rockburst can cause significant disasters in underground rock engineering. It is crucial to predict and prevent rockburst in deep tunnels and mines. In this paper, the deficiencies of ensemble learning algorithms in rockburst prediction were investigated. Aiming at these shortages, a novel machine learning model, deep forest, was proposed to predict rockburst risk. The deep forest combines the characteristics of deep learning and ensemble models, which can solve complex problems. To develop the deep forest model for rockburst prediction, 329 real rockburst cases were collected to build a comprehensive database for intelligent analysis. Bayesian optimization was proposed to tune the hyperparameters of the deep forest. As a result, the deep forest model achieved 100% training accuracy and 92.4% testing accuracy, and it has more outstanding capability to forecast rockburst disasters compared to other widely used models (i.e., random forest, boosting tree models, neural network, support vector machine, etc.). The results of sensitivity analysis revealed the impact of variables on rockburst levels and the applicability of deep forest with a few input parameters. Eventually, real cases of rockburst in two gold mines, China, were used for validation purposes while the needed data sets were prepared by field observations and laboratory tests. The promoting results of the developed model during the validation phase confirm that it can be used with a high level of accuracy by practicing engineers for predicting rockburst occurrences.

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“…One of the typical examples is the work on estimating the residential building energy consumption by Tabrizchi et al [19] where a multi-verse optimizer is employed for tuning for -SVR with cross-validation. Considering actual applications, researchers have searched for the tuning in -SVR [18] with meta-heuristic algorithms, such as moth flame optimization (MFO) [20], whale optimization algorithm (WOA) [21], grey wolf optimizer (GWO) [22], grasshopper optimization algorithm (GOA) [23], flower pollination algorithm (FPA) [24], differential evolution [25], and particle swarm optimization [26]. This kind of combined method based on cross-validation often requires high computational costs to obtain a good optimum for the insensitivity parameter.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In addition, the 10-CV [9] was applied in the insensitivity parameter selection with the same alternative parameter settings as the former simulations. In addition, according to our literature review, we employed three recent meta-heuristics method with 10-CV to tune the insensitivity parameter for the -SVR: whale optimization algorithm (WOA) [21], grey wolf optimizer (GWO) [22], multi-verse optimizer (MVO) [19] with 10 search agents. In addition, all algorithms are performed on an Intel i7-8700 CPU with 16.0 GB of RAM.…”

Section: Case Studiesmentioning

confidence: 99%

A working likelihood approach to support vector regression with a data-driven insensitivity parameter

Wang

2022

Int. J. Mach. Learn. & Cyber.

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The insensitivity parameter in support vector regression determines the set of support vectors that greatly impacts the prediction. A data-driven approach is proposed to determine an approximate value for this insensitivity parameter by minimizing a generalized loss function originating from the likelihood principle. This data-driven support vector regression also statistically standardizes samples using the scale of noises different from conventional response scaling method. Statistical standardization together with probabilistic regularization based on a working likelihood function produces data-dependent values for the hyperparameters including the insensitivity parameter. The exact asymptotical solutions are provided when the noises are normally distributed. Nonlinear and linear numerical simulations with three types of noises ($$\epsilon$$ ϵ -Laplacian distribution, normal distribution, and uniform distribution), and in addition, five real benchmark data sets, are used to test the capacity of the proposed method. Based on all the simulations and the five case studies, the proposed support vector regression using a working likelihood, data-driven insensitivity parameter is superior and has lower computational costs.

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Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm

Cited by 80 publications

References 85 publications

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Application of KNN-based Isometric Mapping and Fuzzy C-Means Algorithm to Predict Short-term Rockburst Risk in Deep Underground Projects

Novel Ensemble Tree Solution for Rockburst Prediction Using Deep Forest

A working likelihood approach to support vector regression with a data-driven insensitivity parameter

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