Effects of structural planes on rockburst position with respect to tunnel cross-sections: a case study involving a railway tunnel in China

Hu, Lei; Feng, Xia‐Ting; Xiao, Yuzhou; Wang, Rui; Feng, Guang-Liang; Yao, Zhibin; Niu, Wen-jing; Zhang, Wei

doi:10.1007/s10064-019-01593-0

Cited by 73 publications

(19 citation statements)

References 32 publications

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“…2016;Poeck 2016;Khademian 2019;He 2016;Manouchehriana 2018;Mitri 1999;Jiang 2010;Sharan 2007) in the rockburst prediction and its combination with other techniques is also a research topic that is investigated by many researchers, but it's main use focuses on the establishing of the burst prone areas and still there is not a universally accepted methodology of simulating accurately dynamic phenomena. Other research studies focus on the simulation of seismic waves generated from fault slips or from the failing rock and the associated damage that is caused in an underground excavation (Qinghua 2016;Banadaki 2012;Qiu 2019;Gao 2019;MJ Raffaldi 2017;Cho 2004;Hu 2019;He 2016). According to Kaiser (1996) numerical modeling for the rockburst prediction is based mostly on static approaches due to the complexity of the phenomenon and the difficulty to realistically simulate the dynamic procedures that are involved during a rockburst.…”

Section: Rockburst Prediction Methodsmentioning

confidence: 99%

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Papadopoulos

Benardos

2021

Geotech Geol Eng

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One of the main challenges that deep mining faces is the occurrence of rockburst phenomena. Rockburst risk assessment with the use of machine learning is currently gaining increased attention, due to the fact that outperforms the widely used empirical approaches. However, the limited and imbalanced instance records, combined with the multiparametric nature of the phenomenon, can lead to unstable estimations. This study focuses on the enhancement of the prediction performance of five machine learning algorithms, including Decision Trees, Naïve Bayes, K-Nearest Neighbor, Random Forest and Logistic Regression, by utilizing the oversampling technique SMOTE (Synthetic Minority Oversampling TEchnique).The initial database consists of 249 rockburst incidents, from which approximately 70% was used as the training set and the remaining 30% as the test set. Parametric analyses were conducted regarding different indicator combinations, such as the maximum tangential stress, the rock's uniaxial compressive and tensile strength, the stress coefficient, two brittleness coefficients and the elastic energy index. The models were trained with the original dataset and afterwards a gradual increase of the database with synthetic instances was made until the obtainment of a balanced dataset. Subsequently the creation of synthetic instances was continued until the real incidents used for training and the synthetic incidents were of the same amount. The results from the following analysis show that SMOTE technique has a considerable effect in the evaluation metrics of the models, even after the balancing of the dataset, and can be a valuable asset for the rockburst prediction.

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Section: Rockburst Prediction Methodsmentioning

confidence: 99%

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Papadopoulos

Benardos

2021

Geotech Geol Eng

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“…10 , 11 Additionally, it had also been found that the structural plane was fully related to the occurrence of rockburst at Jinping II Hydropower Station. 12 , 13 However, the fracture evolution pattern and the mechanical characteristics of fault-slip rockburst had not been clearly analyzed by the above-mentioned studies. 8 − 13 Therefore, we learn that the strength of geological structural plane will influence the safety of underground engineering.…”

Section: Introductionmentioning

confidence: 99%

Experimental Study on the Spatial–Temporal Failure Characteristics of Red Sandstone with a Cemented Structural Surface under Compression

Gao

Shan

et al. 2022

ACS Omega

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A geological structural surface frequently appears in underground engineering and it significantly affects the stability of engineering structures. To elucidate the spatial–temporal failure characteristics of discontinuities between rocks, red sandstone with a firmly cemented discontinuity was investigated under uniaxial compression. The results revealed that adding a strong binder to rock discontinuities can improve the shear strength and stability of rock mass. The stress curve indicated that red sandstone specimens had elastic–brittle failure characteristics without obvious plastic deformation. The acoustic emission ring counts did not have large fluctuations, and its values were smaller (less than 2.5 × 10 4 /s) at elastic deformation stage, which was termed as the “quiet period”. The acoustic emission ring counts initially exhibited an obvious jump increase phenomenon at the forthcoming brittle failure stage before stress curve produced an obvious turning point, and then, it entered the “relatively quiet period”. Based on the acoustic emission index (rise angle and average frequency), at the initial destruction stage of rocks, the types of internal fractures were simple and clearly (tensile cracks or shear cracks), and the fracture location was also relatively scattered inside specimens. Subsequently, the internal fracture developed into mixed tensile and shear cracks, and then, the fracture location exhibited a progressive evolution trend in the vertical space of specimens.

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“…Ma et al [14] reported the occurrence of a rockburst, recording approximately 40 microtremors in two minutes, the distribution of which was consistent with the direction of the structural plane obtained by the on-the-spot investigation. In recent years, early warning methods based on microseismic monitoring have produced promising results and successful predictions, such as for the Access Tunnel of Shuangjiangkou Hydropower Station, Sichuan-Tibet Railway Tunnel, and Hanjiang-to-Weihe River Diversion Project [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Review on Early Warning Methods for Rockbursts in Tunnel Engineering Based on Microseismic Monitoring

et al. 2021

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Due to the different geological conditions and construction methods associated with different projects, rockbursts in deep-buried tunnels often present different precursor characteristics, bringing major challenges to the early warning of rockbursts. To adapt to the complexity of engineering, it is necessary to review the latest advancements in rockburst early warning and to discuss general early warning methods. In this article, first, microseismic monitoring and localization methods applicable under tunneling construction are reviewed. Based on the latest engineering examples and research progress, the microseismic evolution characteristics of the rockburst formation process are summarized, and the formation process and mechanism of structure-type and delayed rockbursts are analyzed. The different methods for predicting the risk and level of rockbursts using microseismic indices are reviewed, and the implementation methods and application cases for predicting potential rockburst areas and rockburst probability based on a mechanical model are expounded. Finally, combined with the new practice in early warning methods, development directions for the early warning of rockbursts are put forward.

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Effects of structural planes on rockburst position with respect to tunnel cross-sections: a case study involving a railway tunnel in China

Cited by 73 publications

References 32 publications

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Experimental Study on the Spatial–Temporal Failure Characteristics of Red Sandstone with a Cemented Structural Surface under Compression

Review on Early Warning Methods for Rockbursts in Tunnel Engineering Based on Microseismic Monitoring

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