Research on the relationship between key risk factors of university emergencies based on ISM-MICMAC.

In order to accurately analyze the interaction among the risk factors of university emergencies, firstly, based on the key performance indicator (KPI) concept, by initially extracting risk factors, identifying initial risk factors, and correcting initial risk factors, and the risk factors of emergencies in universities were divided into three categories: human factors, environmental factors, and management factors, and 18 key risk factors of emergencies in universities, including psychological problems, weak legal awareness and lack of safety awareness, were subdivided and determined. Secondly, the interpretive structure model (ISM) was used to construct a multi-layer hierarchical model of the relationship between university emergencies risk factors, clarified the hierarchical affiliation between the risk factors of university emergencies, divided the risk factors of university emergencies into surface direct factors, middle indirect factors and deep fundamental factors, and analyzed the coupling effect of various risk factors on university emergencies. Thirdly, combined with the matrix impacts cross-reference multiplication applied to a classification (MICMAC), the driving force and dependence force of each risk factor of university emergencies were obtained, and the risk factors of university emergencies were divided into spontaneous factors, independent factors, dependent factors, linkage factors, etc.

AdaBoost model for rockburst intensity prediction considering class differences and quantitative characterization of misclassification difference.

With the resource development gradually into the deep, rock explosion phenomenon is more and more frequent. The suddenness and harmfulness of rockbursts threaten the safe development of underground resources. In order to more accurately predict the possible intensities of rockbursts in specific rock conditions and stress environments, the rock mechanical and stress parameters between different intensities of rockbursts are further explored, an AdaBoost model considering the differences in the hierarchy is established, and a Flash Hill-Climbing method is proposed to optimize the hyperparameters in the classification model.

Scientometric analysis on the review research evolution of tailings dam failure disasters.

As the most severe damage form of tailings ponds, dam failure causes a serious threat and damage to the surrounding lives and environment. Therefore, based on the systematic collection and consultation of relevant data at home and abroad, the literature source analysis on tailings dam failure disasters is conducted using the CiteSpace scientometric tool. The research on tailings dam failure disasters can be classified into two stages: the preliminary germination stage and rapid development stage.

Deformation failure and acoustic emission characteristics of continuous graded waste rock cemented backfill under uniaxial compression.

In order to study the effect of backfill aggregate particle size on the compressive strength and failure mode of cemented backfill, uniaxial compression tests were carried out on seven kinds of cemented backfills with different particle size gradations. By analyzing the AE characteristics during the failure process of the backfill, the damage evolution mechanism of the cemented backfill with different particle size gradations was discussed. The test results show that with the increase of the Talbot gradation index n, the compressive strength of the backfill specimens first increases and then decreases, and the failure mode gradually changes from shear failure to tensile failure.

Multi-hazard risk characterization and collaborative control oriented to space in non-coal underground mines.

In order to realize accurate risk assessment and collaborative control of multi-hazard risk in non-coal underground mines, a space-oriented risk characterization and collaborative control model of multi-hazard risk in non-coal underground mines is proposed. Statistical analysis of non-coal underground mine accidents from 2000 to 2022, revealing the characteristics of non-coal underground mine accidents and 5 risk types were identified, including cage fall accident, powered haulage accident, fire accident, mine water inrush accident, and roof fall and rib spalling accident. A multi-hazard risk analysis and assessment framework for non-coal underground mines based on the inherent risk of the system, the vulnerability of the disaster-bearing body and the adaptability of the disaster-bearing area is proposed.

Ensemble stacking rockburst prediction model based on Yeo-Johnson, K-means SMOTE, and optimal rockburst feature dimension determination.

Rockburst forecasting plays a crucial role in prevention and control of rockburst disaster. To improve the accuracy of rockburst prediction at the data structure and algorithm levels, the Yeo-Johnson transform, K-means SMOTE oversampling, and optimal rockburst feature dimension determination are used to optimize the data structure. At the algorithm optimization level, ensemble stacking rockburst prediction is performed based on the data structure optimization.

Uncertainty prediction of mining safety production situation.

In order to explore the occurrence and development law of mining safety production accidents, analyze its future change trends, and aim at the ambiguity, non-stationarity, and randomness of mining safety production accidents, an uncertainty prediction model for mining safety production situation is proposed. Firstly, the time series effect evaluation function is introduced to determine the optimal time granularity, which is used as the window width of fuzzy information granulation (FIG), and the time series of mining safety production situation is mapped to Low, R, and Up three granular parameter sequences, according to the triangular fuzzy number; then, the mean value of the intrinsic mode function (IMF) is maintained in the normal dynamic filtering range. After the ensemble empirical mode decomposition (EEMD), the three non-stationary granulation parameter sequences of Low, R, and Up are decomposed into the intrinsic mode function components representing the detail information and the trend components representing the overall change, and then the sub-sequences are reconstructed according to the sample entropy to highlight the correlation among the sub-sequences; finally, the cloud model language rules of mining safety production situation prediction are created.

Fuzzy risk prediction of roof fall and rib spalling: based on FFTA-DFCE and risk matrix methods.

The working conditions of underground mining are complex and variable, and roof fall and rib spalling are one of the main types of accidents that can occur. Building an integrated model to evaluate the risk of roof fall and rib spalling is the foundation of mine safety. On the basis of the inherent attributes of event risk, the fuzzy evaluation set and probability of basic events are obtained by using the fuzzy fault tree analysis method based on the sample's fuzzy information.