Modeling violent rock failures in tunneling and shaft boring based on energy balance calculations

Khademian, Zoheir; Ozbay, U.

doi:10.1016/j.tust.2019.04.018

Cited by 15 publications

(6 citation statements)

References 26 publications

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“…The use of numerical modeling (Vatcher 2014;Tianwei 2015;Board 2007;Vardar 2019;Khademian. 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).…”

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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“…W e is strain energy stored in the model. W d is the dissipative energy, which is the released energy from the surrounding rock after excavation and can be considered a measure of overall damage in rockmass [50,51]. E k is the kinetic energy of particles.…”

Section: Energy Balance Calculationsmentioning

confidence: 99%

“…E d is the energy dissipated by local damping. The sum of both E d and E k , which is the radiated energy from surrounding rock as a result of unstable failure after excavation, can be a measure of rockburst intensity [50,51].…”

Section: Energy Balance Calculationsmentioning

confidence: 99%

Study on Evolution Mechanism of Structure-Type Rockburst: Insights from Discrete Element Modeling

Zhang

et al. 2021

Sustainability

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Taking the “11.28” rockburst occurred in the Jinping II Hydropower Station as the engineering background, the evolution mechanism of structure-type rockburst was studied in detail based on the particle flow code. The results indicate that the failure mechanism of structure-type rockburst includes a tensile fracture induced by tangential compressive stress and a shear fracture caused by shear stress due to overburdened loadings and shear slip on the structural plane. In addition, it is found that the differences between structure-type rockburst and strainburst mainly include (a) the distribution of the local concentrated stress zone after excavation, (b) the evolution mechanism, and (c) the failure locations. Finally, the influence of four factors on the structure-type rockburst are explored. The results show that (1) when the friction coefficient is greater than 0.5, the effect of structural plane is weakened, and the rock near excavation tends to be intact, the structural-type rockburst intensity decreases; (2) the dissipated and radiated energy in structural-type rockburst reduces with rockmass heterogeneity m; (3) the lateral pressure coefficient has a significant effect on the intensity of deep rock failure, specifically in the form of the rapid growth in dissipative energy; (4) and the structural-type rockburst is more pronounced at a structural plane length near 90 mm.

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“…Statistically, 70% of deep roadways should be repaired because of the large deformations of surrounding rock masses, thus considerably increasing costs 4 . In situ stress causes the accumulation of initial strain energy in underground rock masses, 5 and the deformation and failure of underground rock masses can be essentially attributed to the transfer and dissipation of the strain energy 6,7 . Therefore, the strain energy method has been widely used in the fields of rock mechanics and engineering.…”

Section: Introductionmentioning

confidence: 99%

Transfer and dissipation of strain energy in surrounding rock of deep roadway considering strain softening and dilatancy

Liu

Lü

et al. 2020

Energy Science & Engineering

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In this study, the transfer and dissipation of strain energy in the surrounding rock of a deep roadway were analyzed, considering the objective strain softening and dilatancy behaviors. The strain energy increment was decomposed, and its variation was analyzed based on the incremental plastic flow theory; then, a numerical simulation was conducted for verification and further analysis. The results were verified by the field monitoring data of a coal mine gateway. The results show that in the elastic stage, the volumetric elastic strain energy density Uev decreases, while the shear elastic strain energy density Ues increases. In the plastic stage, both Uev and Ues decrease. The volumetric plastic strain energy density Upv is negative, and its absolute value increases, leading to strain energy accumulation. In contrast, the shear plastic strain energy density Ups is positive and increases, leading to strain energy dissipation. Considering strain softening, the elastic strain energy decreases, the plastic strain energy increases, and the region of strain energy dissipation expands. Considering dilatancy, the plastic strain energy varies more significantly, and the effect of strain softening is amplified. The strain energy is transferred from the deep part to the shallow part of the elastic zone and then to the plastic zone. The preexisting and input strain energies in the plastic zone are transformed into considerable amounts of plastic strain energy and then dissipated. Thereafter, a significant plastic strain appears, leading to the large deformation of the surrounding rock.

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Modeling violent rock failures in tunneling and shaft boring based on energy balance calculations

Cited by 15 publications

References 26 publications

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Enhancing Machine Learning Algorithms to Assess Rock Burst Phenomena

Study on Evolution Mechanism of Structure-Type Rockburst: Insights from Discrete Element Modeling

Transfer and dissipation of strain energy in surrounding rock of deep roadway considering strain softening and dilatancy

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