Rockburst prediction model based on comprehensive weight and extension methods and its engineering application

Zhang, Lewen; Zhang, Xiangyu; Wu, Jing; Zhao, Dukun; Fu, Hao

doi:10.1007/s10064-020-01861-4

Cited by 34 publications

(16 citation statements)

References 36 publications

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“…Based on the MFICM proposed in this study, five sections of a diversion tunnel of Jinping II hydropower station are analyzed. The index measured data and prediction results of each section of a diversion tunnel are shown in Table 11 (Zhang et al 2020). It can be seen from Table 11 that the prediction results of the MFICM exhibit extremely good agreement with the actual situations and better accuracy than other methods.…”

Section: Risk Prediction Of Rock Burst Of the Diversion Tunnel In Jinping II Hydropower Stationmentioning

confidence: 95%

A novel comprehensive quantitative method for various geological disaster evaluations in underground engineering: multidimensional finite interval cloud model (MFICM)

Gao

Zhou

et al. 2021

Environ Earth Sci

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The risk prediction of geological disasters exhibits extreme uncertainty and complexity due to the random distribution of evaluation indexes within a finite interval. To improve the accuracy of disaster prediction results, a new evaluation method is in this study suggested to perform a risk evaluation of geological disasters based on multidimensional finite interval cloud model (MFICM) and combination weighting. The MFICM with a transformation between qualitative concept and quantitative data depicts uncertainties and actual distribution features of indexes in the finite 2 interval. Analytic hierarchy process (AHP) and principal component analysis (PCA) are adopted to determine the subjective and objective weights of evaluation indexes, respectively, and the combination weight is calculated by a linear method to reduce the influence of subjective factors.The numerical characteristic parameters of each indexes belonging to various risk levels are first calculated based on established evaluation index system. Subsequently, a multi-dimensional finite interval cloud is generated from a forward cloud generator using MATLAB software. Finally, the comprehensive certainty degrees relative to different levels for each sample are determined combined with combination weight, which achieves a mapping of uncertainty between semantic variables and index values. The proposed method is applied to engineering cases regarding three geological disasters, i.e., water inrush, rock burst and collapse. The obtained results with accuracy and results comparison with reference methods show that the MFICM is verified to be practical and universal for the risk evaluation of geological disasters, which improves and enriches the theoretical framework of geotechnical engineering disaster risk evaluation.

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Section: Risk Prediction Of Rock Burst Of the Diversion Tunnel In Jinping II Hydropower Stationmentioning

confidence: 95%

A novel comprehensive quantitative method for various geological disaster evaluations in underground engineering: multidimensional finite interval cloud model (MFICM)

Gao

Zhou

et al. 2021

Environ Earth Sci

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show abstract

“…Fig. 8 Rock burst conditions of the diversion tunnels at BK6+754 (a) and at AK9+485~490 (b) of Jinping II hydropower station (Zhang et al 2020).…”

Section: Risk Prediction Of Rock Burst Of the Diversion Tunnel In Jinping II Hydropower Stationmentioning

confidence: 99%

A Novel Comprehensive Quantitative Method for Various Geological Disaster Evaluations in Underground Engineering: Multidimensional Finite Interval Cloud Model (MFICM)

Gao

Zhou

et al. 2021

Preprint

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The risk prediction of geological disasters exhibits extreme uncertainty and complexity due to the random distribution of evaluation indexes within a finite interval. To improve the accuracy of disaster prediction results, a new evaluation method is in this study suggested to perform a risk evaluation of geological disasters based on multidimensional finite interval cloud model (MFICM) and combination weighting. The MFICM with a transformation between qualitative concept and quantitative data depicts uncertainties and actual distribution features of indexes in the finite interval. Analytic hierarchy process (AHP) and principal component analysis (PCA) are adopted to determine the subjective and objective weights of evaluation indexes, respectively, and the combination weight is calculated by a linear method to reduce the influence of subjective factors. The numerical characteristic parameters of each indexes belonging to various risk levels are first calculated based on established evaluation index system. Subsequently, a multi-dimensional finite interval cloud is generated from a forward cloud generator using MATLAB software. Finally, the comprehensive certainty degrees relative to different levels for each sample are determined combined with combination weight, which achieves a mapping of uncertainty between semantic variables and index values. The proposed method is applied to engineering cases regarding three geological disasters, i.e., water inrush, rock burst and collapse. The obtained results with accuracy and results comparison with reference methods show that the MFICM is verified to be practical and universal for the risk evaluation of geological disasters, which improves and enriches the theoretical framework of geotechnical engineering disaster risk evaluation.

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“…The above models have achieved favorable prediction results in many projects. Considering the difficulty in obtaining rock mass parameters quickly and accurately during excavation, these models are tough to conduct real-time rockburst prediction, which are more applied to long-term prediction in engineering investigation stage (Zhang et al 2020;Zhou et al 2020).…”

Section: Introductionmentioning

confidence: 99%

A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring

et al. 2021

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Rockburst is a kind of complex and catastrophic dynamic geological disaster in the development and utilization of underground space, which seriously threatens the safety of personnel and environment. Due to the suddenness in time and randomness in space, the prediction of rockburst becomes a great challenge. Microseismic monitoring is capable to continuously capture rock microfracture signals in real time, which offers an effective means for rockburst prediction. With the explosive growth of monitoring data, the conventional manual forecasting methods are laborious and time-consuming. Therefore, artificial intelligence was introduced to improve the prediction efficiency. A novel tree-based algorithm was proposed. Its basic idea was to automatically recognize precursory microseismic sequences for the real-time prediction of rockburst intensity.The database consisting of 1500 microseismic events was analyzed. In order to establish precursory microseismic sequences, dimensionality reduction of the database was first implemented by t-SNE algorithm. Then, k-means clustering algorithm was employed for labelling 1500 microseismic events. Before that, canopy algorithm was adopted to determine the number of clusters. Finally, 300 precursory microseismic sequences were formed by grouping rule. They were further partitioned into two parts through stratified sampling: 70% for training and 30% for validation. The validation results indicated that the precursor tree with pruning achieved higher prediction accuracy of 98.9% than one without pruning on the validation set. And the increase was separately 12.2%, 9.2% and 28.6% on the whole validation set and each classes (low/moderate rockburst). In comparison with low rockburst, moderate rockburst was minority class. The improved accuracy on moderate rockburst suggested that pruning can enhance the recognition ability of precursor tree for minority class. Additionally, two extra rockburst cases were collected from a diversion tunnel in northwestern China, which provided a complete workflow about how to apply the built precursor tree model to achieve field rockburst warning in engineering practice. The tree-based algorithm served as a new and promising way for the real-time rockburst prediction, which successfully integrated field microseismic monitoring and artificial intelligence.

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Rockburst prediction model based on comprehensive weight and extension methods and its engineering application

Cited by 34 publications

References 36 publications

A novel comprehensive quantitative method for various geological disaster evaluations in underground engineering: multidimensional finite interval cloud model (MFICM)

A novel comprehensive quantitative method for various geological disaster evaluations in underground engineering: multidimensional finite interval cloud model (MFICM)

A Novel Comprehensive Quantitative Method for Various Geological Disaster Evaluations in Underground Engineering: Multidimensional Finite Interval Cloud Model (MFICM)

A novel tree-based algorithm for real-time prediction of rockburst risk using field microseismic monitoring

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