Based on the actual monitoring data of the acoustic emission (AE) ground pressure monitoring and positioning system, this paper introduces fractal theory and the multifractal detrended fluctuation analysis (MF-DFA) method to estimate the waveform multifractal spectrum of goaf rock acoustic emission signals and investigate multifractal time-varying response characteristics. The research results show that the wavelet hard thresholding method has the best noise reduction effect on the original signal, and the box counting dimension has a strong waveform identification effect. Before deformation damage occurs, fractal spectral width Δα shows an increase and then decrease and the fluctuation scale factor Δf(α) decreases and then increases. When damage occurs, fractal spectral width Δα decreases and then stabilizes and concentrates. Simultaneously, the fluctuation scale factor Δf(α) keeps decreasing until the lowest point, and then shows an increasing trend until it reaches a stable state. This study is of great significance to the stability evaluation and disaster early warning of mine goaf.

Rockburst is a complex destabilization phenomenon which is a combination of multiple factors, the study of rockburst for classification prediction can help prevent and control engineering geological hazards, reduce casualties and property damage. To achieve efficient and accurate rockburst classification prediction and solve the problem of rockburst propensity assessment, six evaluation factors are selected as the rock explosion prediction and evaluation system: tangential stress $\sigma _{\theta }$ , uniaxial compressive strength $\sigma _{\mathrm {c}}$ , uniaxial tensile strength $\sigma _{\mathrm {t}}$ , tangential stress to uniaxial compressive strength ratio $\sigma _{\mathrm {\theta }}/\sigma _{\mathrm {c}}$ (BCF), uniaxial compressive strength to tensile strength ratio $\mathrm {\sigma }_{\mathrm {c}}/\sigma _{\mathrm {t}}$ (SCF), and elastic deformation energy index $\mathrm {W}_{\mathrm {et}}$ in this study. Widely collected domestic and international groups of rock explosion evaluation data, and 420 sets of valid samples were obtained by data processing. Establish rockburst grading prediction evaluation models based on BP neural networks and support vector machines respectively, then establish BP-SVM prediction models based on arithmetic mean weights and standard deviation weights, analyzing and comparing the prediction rating results of 120 groups of samples among them. Accuracy, Precision, Recall, Specificity, and F1 Score metrics are selected to evaluate the performance of different models, the results show that several models can obtain effective prediction results, among which the standard deviation weight combination BP-SVM model proposed in this paper has the best prediction accuracy and the best effect, which is better than the traditional single machine learning method.

Rockburst is a typical engineering geological disaster under the condition of high geostress. The rockburst classification and prediction are of great significance for the prevention and control of engineering geological disasters under the high geostress environment, and can reduce or even avoid the loss of personnel, equipment, and property. Thus, the GA-XGB model for rockburst classification prediction is proposed in this paper. Specifically, the parameter search process of extreme gradient boosting (XGB) is optimized based on the genetic algorithm (GA), and 10-fold cross-validation is performed on the Dataset collected from literature research. There are 275 sets of complete data, including 6 parameters: σθ, σc, σt, σθ/σc, σc/σt and Wet. Afterward, the impact of different indicator parameter combinations on the performance of the proposed model is explored and compared to existing machine learning models (such as SVM and XGB). Finally, the classification evaluation indexes (precision, recall, and F1 score) are calculated and compared to those obtained by several other algorithms. The results indicate that the method proposed in this paper performs better than other machine algorithms such as XGB, SVM, DT, and Bayes.

Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and r of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.

Rock mass grading is a basic problem in the construction industry and underground engineering research. Because the index parameters that affect the rock mass quality are ambiguous and random, rock mass quality classification is often uncertain. Based on this issue, this paper selects the rock quality index RQD, rock uniaxial saturated compressive strength Rw, rock mass integrity coefficient Kv, structural surface strength coefficient Kf and groundwater seepage quantity ω as quantitative evaluation indicators to construct an evaluation system. Thirty sets of data collected in China are selected as learning samples. Through the related concepts and finite interval cloud model, the characteristic parameters of the measured data are obtained, and a cloud model is generated with a forward cloud generator to achieve the transformation between qualitative and quantitative concepts. Combined with the basic knowledge of rough set theory, the weight determination problem is transformed into an attribute importance problem. To avoid zero weights in the traditional rough set approach, this paper introduces a calculation method based on the conditional information entropy, and the weight calculation method is modified to obtain the comprehensive weights. According to the principle of the maximum membership degree, the classification of rock mass quality is performed, and the rock mass quality data are determined to have different levels of comprehensive membership. A rock mass quality evaluation method based on the coupled improved rough set“cloud model is established and successfully applied for a rock mass quality evaluation of the 0+000∼0+560 test section of the second stage of underground engineering at the Guangdong Pump Storage Power Station. The results show that the model is reliable and practical and provides a new approach for uncertainty analysis and evaluation in rock engineering practice.