Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine

Yan, Shaohong; Zhang, Yanbo; Liu, Xiangxin; Liu, Runze

doi:10.3390/math10183276

Cited by 8 publications

(5 citation statements)

References 5 publications

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“…The macroscopic block characteristics of rockburst fragments reflect the degree of rock fragmentation, which increases with the loading rate (Sun et al, 2022). (Yan et al, 2022) found that the prediction results of the rockburst intensity classification prediction model and SVM discriminant method based on the analysis of a large number of sample data processing were in good agreement with the actual rockburst intensity (Xia et al, 2022). proposed a data-driven approach based on spectral clustering to predict rockburst intensity (Yang et al, 2020).…”

Section: Introductionmentioning

confidence: 92%

Characterization of true triaxial rock bursts in sandstones with different water contents

Chen¹,

Zhou²,

Pan³

et al. 2023

Front. Earth Sci.

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The rockburst phenomenon occurs in dry red sandstone under high in situ stress, and the rockburst effect is weaker for a water-bearing rock. The rockburst effect on red sandstone with different water contents is analyzed in this paper. A true triaxial testing machine is used to conduct the loading, and acoustic emission recording equipment and a high-speed camera are used to monitor the acoustic signal inside the rock and the rock-caving situation throughout the entire process in order to analyze the characteristics of the acoustic emissions and the ejection form of the rockburst. The results show that rockburst occurs in dry red sandstone and 50% saturated red sandstone but not in saturated red sandstone. The phrase characteristics of the stress–strain curve of the dry rock vary more significantly than those of the water-bearing rock, and the elastic strain energy inside the rock decreases gradually as the water content increases. The double peak of the acoustic emissions curve occurs during the failure process of the dry rock and gradually transitions to a stepped pattern as the water content increases. The ejected fragments of dry red sandstone during the rockburst are abundant and large. The true triaxial test results illustrate the characteristic effect of the rockburst on red sandstone with different water contents, reveal the failure mode and ejection characteristics of red sandstone with different water contents, and demonstrate the influence of the water content on the rockburst characteristics of red sandstone. The results of this study provide a theoretical reference for the study of the rockburst mechanisms of similar hard rocks.

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Section: Introductionmentioning

confidence: 92%

Characterization of true triaxial rock bursts in sandstones with different water contents

Chen¹,

Zhou²,

Pan³

et al. 2023

Front. Earth Sci.

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“…It can solve the problem of how to determine the hyperparameters reasonably in the prediction model; the optimal hyperparameter is output in the hyperparameter combination, and it is a commonly used optimization algorithm. Based on References [37,38], this paper briefly introduces the Bayesian optimization algorithm:…”

Section: Bayesian Optimization Algorithmmentioning

confidence: 99%

“…According to the posterior probability distribution, the sampling function samples the region where the global optimal solution is most likely to appear and the region that has not been sampled, and selects the optimal sample points from the candidate set to minimize the value of the objective function. The Gaussian regression process and PI (Probability of Improvement) function are generally adopted as prior functions and sampling functions [38].…”

Section: Bayesian Optimization Algorithmmentioning

confidence: 99%

Rock Burst Intensity-Grade Prediction Based on Comprehensive Weighting Method and Bayesian Optimization Algorithm–Improved-Support Vector Machine Model

Bao,

Hou,

Sun

2023

Sustainability

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In order to accurately judge the tendency of rock burst disaster and effectively guide the prevention and control of rock burst disaster, a rock burst intensity-grade prediction model based on the comprehensive weighting of prediction indicators and Bayesian optimization algorithm–improved-support vector machine (BOA-SVM) is proposed for the first time. According to the main factors affecting the occurrence and intensity of rock burst, the rock stress coefficient (σθ/σc), brittleness coefficient (σc/σt) and elastic energy index (Wet) are selected to construct the rock burst prediction indicator system. On the basis of the research of other scholars, according to the main performance and characteristics of rock burst, rock burst is divided into four intensity levels. The collected and sorted 120 sets of rock burst case data at home and abroad are taken as learning samples, and the T-SNE algorithm is used to perform dimensionality-reduction visualization processing on the sample data, visually display the distribution of samples of different grades, evaluate the representativeness of the sample data and prejudge the feasibility of the machine learning algorithm to distinguish different rock burst intensity levels. The combined improved analytic hierarchy process (IAHP) and Delphi method determine the subjective weight of the indicators; the combined entropy weight method and CRITIC method determine the objective weight of the indicator, and use the harmonic mean criterion of information theory to synthesize the subjective weight and objective weight of the indicator to obtain the comprehensive weight of the indicators. After weighted prediction indicators, a rock burst intensity-grade prediction model is constructed based on the support vector machine, and the hyperparameters of three types of support vector machines are improved by using the Bayesian optimization algorithm. Then, the prediction accuracy of different models is calculated by the random cross-validation method, and the feasibility and effectiveness of the rock burst intensity-grade prediction model is verified. In order to evaluate the generalization and engineering applicability of the proposed model, 20 groups of rock burst case data from the Maluping mine and Daxiangling tunnel are introduced to predict the rock burst intensity grade. The results show that the accuracy of the rock burst intensity-grade prediction model based on comprehensive weighting and BOA-SVM is as high as 93.30%, which is of higher accuracy and better effect than the ordinary model, and can provide warning information with a higher fault tolerance rate, which provides a new way of thinking for rock burst intensity-grade prediction.

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“…To mitigate the aforementioned challenge, this investigation introduces a unique prediction methodology -Cross Reconstruction Learning (CR), which is rooted in metric learning strategies [9] and conventional machine learning algorithms. This investigation deploys models (KNN, XGBoost, and Random Forest algorithms) that have evidenced efficacy in prior rockburst prediction research [10][11][12], integrating them with the proposed Cross Reconstruction technique to fabricate a novel hybrid prediction model. Assessment and comparison of assorted prediction schemes suggest that the proposed Cross Reconstruction strategy efficaciously amplifies the predictive precision of the model.…”

Section: Introductionmentioning

confidence: 99%

Rockburst classification based on cross reconstruction learning under small-sample condition

Cai,

Chen,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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Rockburst is a prevalent geological hazard in deep geotechnical engineering, and its accurate prognostication is vital for prevention measures. Consequently, this research proffers a pioneering classification prediction methodology, namely Cross Reconstruction Learning (CR), underpinned by conventional machine learning algorithms and metric learning strategies. Initially, this technique partitions and restructures the original dataset, where each sample feature intersects and reconfigures with features from other samples within the set. During this amalgamation, new samples are assigned labels based on the degree of divergence or congruity between two sets of sample labels, thereby forming a new set of samples. Subsequently, an array of machine learning algorithms is utilized to train and test this new dataset. Ultimately, employing a universal class voting mechanism and decoding test set results through probability assignment, the predicted labels are converted back into rock burst outcomes, thereby determining the final prediction classification. The proposed model was trained on a database encompassing 239 instance samples, and its performance was validated against the currently proficient models (KNN, XGBoost, and Random Forest algorithms) employed in rock burst prediction. The outcome revealed a decline in the performance metrics of all three machine learning algorithms when interfaced with the Cross Reconstruction learning method, particularly the KNN algorithm, owing to the doubled feature dimensions in the combined dataset. However, the metrics of ensemble models, XGBoost and Random Forest, exhibited a notable improvement compared to the original classification models. On comparing multiple performance metrics, it was discovered that the CR-XGBoost model outperformed others across all evaluations, thereby offering significant guidance for practical engineering applications.

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Rock Burst Intensity Classification Prediction Model Based on a Bayesian Hyperparameter Optimization Support Vector Machine

Cited by 8 publications

References 5 publications

Characterization of true triaxial rock bursts in sandstones with different water contents

Characterization of true triaxial rock bursts in sandstones with different water contents

Rock Burst Intensity-Grade Prediction Based on Comprehensive Weighting Method and Bayesian Optimization Algorithm–Improved-Support Vector Machine Model

Rockburst classification based on cross reconstruction learning under small-sample condition

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