Microseismic Monitoring to Characterize Structure-Type Rockbursts: A Case Study of a TBM-Excavated Tunnel

Liu, Quansheng; Wu, Jian; Zhang, Xiaoping; Tang, Liexian; Bi, Chen; Li, Weiwei; Xu, Jinlin

doi:10.1007/s00603-020-02111-5

Cited by 52 publications

(18 citation statements)

References 56 publications

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“…In view of this, the smooth blasting technique, as a protective controlled blasting method, has been increasingly employed to minimize the magnitude of overbreak or underbreak and thus to reduce the blastinginduced cracking in the surrounding rock masses [5,6]. Although the blasting quality has been significantly improved by using smooth blasting, it still inevitably caused some overbreak or underbreak which might adversely affect the safety, stability, and budget-related concerns [7][8][9]. Consequently, effective identification of the influential factors, especially the dominant ones, becomes quite constructive to evaluate the quality of blasting, and thus to further improve the smooth blast design with arbitrary geological conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels

Zou

Wang

et al. 2020

Advances in Civil Engineering

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The quality of smooth blasting including the volume of over-/underbreak and blasting-induced damage of surrounding rocks has been extensively considered to be highly correlated to both the cost and advancement rate of rock tunnelling excavated by the drill-blast method. A general control strategy for smooth blasting is too difficult to be available due to the uncertainties and complexity of rock masses, as well as the varying blasting operations. As prerequisite for the evaluation of the blasting quality, effective identification of the influential factors affecting smooth blasting usually plays a significant role in the improvement of smooth blasting design. Compared to the expensive and time-consuming experiments including physical modelling and field tests, numerical modelling, as a cost-efficient approach, offers an attractive alternative to investigate the influential factors in terms of weight, which might be more applicable and reliable for the optimization of smooth blasting parameters. In this case, the dominant factors and secondary factors can be quantitatively identified. Considering the dominant factors often orient the development of things; in this work, a numerical-based approach was proposed to quantitatively identify the dominant factors influencing the quality of smooth blasting. Proposed 3-dimensional blasting modelling was based on LS-DYNA to simulate the occurrence of smooth blasting in rock masses, and the erosion algorithm was also employed to determine the fracturing of jointed rocks. The orthogonal experimental design method was utilized to optimize the experimental arrangement. Seven factors with 4 levels including the perimeter hole spacing, line of least resistance, charge concentration, charging explosive, type of rock mass, detonation velocity, and drilling deviation were taken into account. The geological setting and project background of a real rock tunnel served for the Chengdu-Chongqing high-speed railway were selected as the site conditions to perform the numerical investigation. Calculated area and distance of overbreak as the observed parameters indicating the quality of smooth blasting were utilized to determine sensitivities of factors based on the range analysis of orthogonal experiments. The results suggested that the type of rock mass has the greatest influence on the blasting quality, whereas the charge concentration and detonation velocity can be considered as the secondary factors under the specific site conditions. The proposed numerical approach for assessing influential factors of quality of smooth blasting under specified geological conditions is expected to improve the parameter design and operation of smooth blasting in practical applications.

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Section: Introductionmentioning

confidence: 99%

Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels

Zou

Wang

et al. 2020

Advances in Civil Engineering

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“…All data in the database is collected from a diversion tunnel project in northwestern China. The tunnel has a length of 41.832km and its maximum burial depth is 2268m, which is excavated by the combination of tunnel boring machine (TBM) and drillingblasting method (DBM) Liu et al 2020). Specifically, 32.842km is excavated by TBM and the rest is constructed by DBM.…”

Section: Data Sourcementioning

confidence: 99%

“…In the ESG microseismic monitoring system, a single-velocity model is adopted to locate the microseismic events. In order to determine the P-wave velocity, a new method combing the true reflection tomography (TRT) technique is put forward, which has been detailed introduced in Liu et al (2020). China, 2009).…”

Section: Data Sourcementioning

confidence: 99%

“…Besides, it is also widely applied to slope stability analysis (Xu et al 2011), water inrush prediction (Cheng et al 2018) and gas dynamic disaster warning (Zhao and Tan 2010). For different types of rockburst, the microseismic characteristics have been studied deeply (Feng et al 2019a;Liu et al 2020). The temporal and spatial evolution laws of source parameters can better reflect the generating process of rockburst.…”

Section: Introductionmentioning

confidence: 99%

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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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“…For the structural design and vibration reduction optimization of the cutterhead system. Liu, 14 Hasanpour et al, 15,16 and Afrasiabi et al 17–23 were devoted to studying various performance parameters that affect the work of TBM, such as rock parameters and machine parameters to establish prediction theoretical models of TBM performance. Methods such as “penalty factor” and “pso-ann hybrid model” were proposed to estimate the performance of TBM, which enriched the theoretical model of intelligent prediction.…”

Section: Introductionmentioning

confidence: 99%

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

Ling

Cheng

Tong

et al. 2021

Advances in Mechanical Engineering

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Tunnel boring machine (TBM) is a large-scale tunnel engineering equipment, which has unparalleled advantages in safety and work efficiency. The cutter is subjected to complex and variable random impact loads, resulting in damage to bearings, cutter rings, and cutter shafts. Therefore, based on the discrete element simulation platform and experiment, this paper established the cutter rock simulation model based on the experimental data, and analyzed the rock breaking process, cutter load magnitude, and variation law. The research results show that when the cutter pole diameter decreases from 200 mm, 100 mm, and 0 mm in sequence, the rolling force increases accordingly, and the maximum increase of the rolling force is 56%. Meanwhile the lateral force decreases and the maximum average decrease value is 26%. Moreover, the rolling force showed a downward trend with the increase of the cutter spacing. When the cutter spacing is increased from 80 mm to 120 mm, the average rolling force decreases by 51%, and the maximum value rises slightly, increasing by 2%. And, the lateral force tends to increase, with an increase of 17% and the maximum lateral force value increase was 29%. The results can provide load input reference for TBM cutter layout, cutterhead structure design, and performance evaluation.

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Microseismic Monitoring to Characterize Structure-Type Rockbursts: A Case Study of a TBM-Excavated Tunnel

Cited by 52 publications

References 56 publications

Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels

Numerical Investigation on Influential Factors for Quality of Smooth Blasting in Rock Tunnels

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

Research on rock breaking mechanism and load characteristics of TBM cutter based on discrete element method

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