Rock-burst occurrence prediction based on optimized naïve bayes models

Rock-burst is a common failure in hard rock related projects in civil and mining construction and therefore, proper classification and prediction of this phenomenon is of interest. This research presents the development of optimized naïve Bayes models, in predicting rock-burst failures in underground projects. The naïve Bayes models were optimized using four weight optimization techniques including forward, backward, particle swarm optimization, and evolutionary. An evolutionary random forest model was developed to identify the most significant input parameters. The maximum tangential stress, elastic energy index, and uniaxial tensile stress were then selected by the feature selection technique (i.e., evolutionary random forest) to develop the optimized naïve Bayes models. The performance of the models was assessed using various criteria as well as a simple ranking system. The results of this research showed that particle swarm optimization was the most effective technique in improving the accuracy of the naïve Bayes model for rock-burst prediction (cumulative ranking = 21), while the backward technique was the worst weight optimization technique (cumulative ranking = 11). All the optimized naïve Bayes models identified the maximum tangential stress as the most significant parameter in predicting rock-burst failures. The results of this research demonstrate that particle swarm optimization technique may improve the accuracy of naïve Bayes algorithms in predicting rock-burst occurrence. © 2013 IEEE

The application of evidence-entropy weight gray incidence theory on the risk assessment of rockburst intensity in the Daxiangling tunnel

The risk assessment of rockburst intensity is significant for tunnel construction safety. First, the depth of the rockburst (X1), the uniaxial compressive strength of the rocks (X2), the brittleness coefficient of the rocks (X3), the stress coefficients of the rocks (X4), and the elastic energy index (X5) are adopted as the evidence body, and their essential certainty and reliability is determined using the entropy-gray correlation theory. Second, the synthetic certainty reliability of other samples is calculated based on the evidence theory. Relatively to the traditional gray extension model, it can improve the predictive accuracy and determine the certainty and reliability of different evidence bodies. The difference of importance between other evidence bodies can be reflected; and an interval scale can be taken into consideration in the evaluation process, so the proposed theory can reasonably predict the grade criterion which is interval form. Conclusion demonstrated that the suggested approach is entirely consistent with the actual investigation. The proposed model not only considers the unreliability or reliability of the problem but also solves some degrees of uncertainty and ambiguity of the datum; it enhances the predictive efficiency and provides a new way and thought for future risk assessment of rockburst intensity

Classification method of surrounding rock of plateau tunnel based on BP neural network

Due to the unique high-altitude geological conditions of the railway in the cold region, the problem of high ground stress in the construction process is very prominent. In constructing high ground stress tunnels, accurately evaluating the surrounding rock grades is important in rock mass engineering. Based on this, based on a plateau tunnel under construction, this paper selects the classification index of the surrounding rock, which can accurately reflect the geological characteristics of high ground stress tunnel around the geological environment elements of the surrounding rock of high ground stress tunnel. Based on the rapid classification method of surrounding rock of the BP neural network, the classification method of the surrounding rock suitable for high ground stress tunnel is constructed, and the tunnel engineering data is introduced into the BP neural network classification method of surrounding rock for training and testing. It is found that the classification results of surrounding rock obtained by the classification method of surrounding rock of high ground stress tunnel are in good agreement with the actual situation, which provides an important guarantee for the accurate and rapid determination of the surrounding rock grade of high ground stress tunnel and the safe and efficient construction of the tunnel

A new in situ test for the assessment of the rock-burst alarm threshold during tunnelling

Rock-burst is one of the most serious risks associated with hard rock tunnelling and mining at high depths. Monitoring of acoustic emissions emitted by the rock-mass during excavation and their interpretation now permits the early assessment of failure events and makes the safe management of the construction works possible. A reliable set-up of the alarm threshold is thus fundamental for the correct implementation of the procedures planned to minimise rock-burst related risk. This paper focuses on a novel in situ test specifically developed to provide an experimental basis for a more accurate assessment of the alarm threshold during tunnelling, representative of the local geomechanical conditions. The test, thanks to the compression induced by two flat jacks at the tunnel side wall, produces an artificial failure process during which acoustic emissions are measured and correlated to the mechanical response of the rock-mass, without the typical limitations of scale that characterised the laboratory experiments. The new methodology, named the Mules method, was successfully tested during the excavation of some stretches of the Brenner Base Tunnel in the Brixner granite, affected by mild spalling episodes. The case-history is fully described in the paper to illustrate the practical application of the proposed approach

Rockburst in underground excavations: A review of mechanism, classification, and prediction methods

Technical challenges have always been part of underground mining activities, however, some of these challenges grow in complexity as mining occurs in deeper and deeper settings. One such challenge is rock mass stability and the risk of rockburst events. To overcome these challenges, and to limit the risks and impacts of events such as rockbursts, advanced solutions must be developed and best practices implemented. Rockbursts are common in underground mines and substantially threaten the safety of personnel and equipment, and can cause major disruptions in mine development and operations. Rockbursts consist of violent wall rock failures associated with high energy rock projections in response to the instantaneous stress release in rock mass under high strain conditions. Therefore, it is necessary to develop a good understanding of the conditions and mechanisms leading to a rockburst, and to improve risk assessment methods. The capacity to properly estimate the risks of rockburst occurrence is essential in underground operations. However, a limited number of studies have examined and compared yet different empirical methods of rockburst. The current understanding of this important hazard in the mining industry is summarized in this paper to provide the necessary perspective or tools to best assess the risks of rockburst occurrence in deep mines. The various classifications of rockbursts and their mechanisms are discussed. The paper also reviews the current empirical methods of rockburst prediction, which are mostly dependent on geomechanical parameters of the rock such as uniaxial compressive strength of the rock, as well as its tensile strength and elasticity modulus. At the end of this paper, some current achievements and limitations of empirical methods are discussed

Prediction of rockburst intensity grade in deep underground excavation using adaptive boosting classifier

Rockburst phenomenon is the primary cause of many fatalities and accidents during deep underground projects constructions. As a result, its prediction at the early design stages plays a significant role in improving safety.(e article describes a newly developed model to predict rockburst intensity grade using Adaptive Boosting (AdaBoost) classifier. A database including 165 rockburst case histories was collected from across the world to achieve a comprehensive representation, in which four key influencing factors such as maximum tangential stress of the excavation boundary, uniaxial compressive strength of rock, tensile rock strength, and elastic energy index were selected as the input variables, and the rockburst intensity grade was selected as the output. (e output of the AdaBoost model is evaluated using statistical parameters including accuracy and Cohen’s kappa index. (e applications for the aforementioned approach for predicting the rockburst intensity grade are compared and discussed. Finally, two real-world applications are used to verify the proposed AdaBoost model. It is found that the prediction results are consistent with the actual conditions of the subsequent construction

Assessment of risks of tunneling project in Iran using artificial bee colony algorithm

The soft computing techniques have been widely applied to model and analyze the complex and uncertain problems. This paper aims to develop a novel model for the risk assessment of tunneling projects using artificial bee colony algorithm. To this end, the risk of the second part of the Emamzade Hashem tunnel was assessed and analyzed in seven sections after testing geotechnical characteristics. Five geotechnical and hydrological properties of study zone are considered for the clustering of geological units in front of tunneling project including length of tunnel, uniaxial compressive strength, rock mass rating, tunneling index Q, density and underground water condition. These sections were classified in two low-risk and high-risk groups based on their geotechnical characteristics and using clustering technique. It was resulted that three sections with lithologies Durood Formation, Mobarak Formation, and Ruteh Formation are placed in the high risk group and the other sections with lithologies Baroot Formation, Elika Formation, Dacite tuff of Eocene, and Shear Tuff, and Lava Eocene are placed in the low risk group. In addition, the underground water condition and density with 0.722 and 1 Euclidean distances have the highest and lowest impacts in the high risk group, respectively. Therefore, comparing the obtained results of modelling and actual excavation data demonstrated that this technique can be applied as a powerful tool for modeling risks of tunnel and underground constructions

Research on Multifactor Analysis and Quantitative Evaluation Method of Rockburst Risk in Coal Mines

AbstractThe prevention of rockbursts is significant to ensure mining safety in deep coal mines. The multifactor analysis and a new quantitative evaluation method for rockbursts in coal mines are proposed in this study. In the aspect of rockburst analysis, a multifactor system of rockburst risk based on the material, stress, and large-scale geological structure is proposed. The factors influencing rockbursts in coal mines are analyzed by numerical simulations. Based on a standard mining model, three comparative models considering the rockburst tendency, high stress, and geological structure are established. The distribution of maximum principal stress and plastic zone during the mining process is compared. The reasons why these three types of factors are liable to trigger rockbursts lie in generating high-stress zones in surrounding rock masses. In the aspect of quantitative evaluation, the monitored microseismic signal is selected as the key indicator, and the daily frequency of microseisms is analyzed. A normal distribution function based on the daily frequency of microseisms is established. The interval of daily frequency of microseisms is set to judge whether the microseismic frequency is abnormal and then determine the rockburst risk of coal mines. Considering the results of multifactor analysis, it is proposed that the monitoring system combining microseisms with stress is the direction to accurately and quantitatively evaluate the rockburst risk in the future. This study makes specific explorations in the quantitative evaluation of rockburst risk in coal mines

Numerical modeling of unstable rock failure.

Rockburst is an unstable and violent rock failure and it is a hazardous problem in deep underground mines and civil tunnels; it imposes a great danger to safety of workers and investment. Many factors that influence rockburst damage have been identified. In many rockburst case histories, the presence of geological structures such as faults, shear zones, joints, and dykes has been observed near excavation boundaries but their role in rockburst occurrence is still not fully understood. A good understanding of the role of geological structures on rockburst damage is important to anticipate and control rockbursts and constitutes the focus of this thesis. In this research an explicit finite element tool (Abaqus-Explicit) is employed to study unstable rock failure and rockburst processes. First, uniaxial compression tests were simulated to confirm the suitability of the adopted numerical tool for simulating unstable rock failures. Two indicators namely Loading System Reaction Intensity (LSRI) and the maximum unit kinetic energy (KEmax) were proposed to distinguish between stable and unstable failures in laboratory testing conditions. Unstable rock failures under polyaxial unloading conditions were also simulated. The influences of loading system stiffness, specimen‘s height to width ratio, and intermediate principal stress on rock failure were investigated. Next, material heterogeneity (in terms of strength and deformability) was introduced into the models using Python scripting to enhance the efficiency of Abaqus for modeling geomaterials. Numerical simulation results showed that heterogeneous models resulted in more realistic failure modes than homogeneous models. The effect of material heterogeneity on rock failure intensity in unconfined and confined compression tests was investigated. It was observed that when two materials have the same peak strength, the heterogeneous model had more released energy than the homogeneous model due to differences in the failure mode. The tensile splitting failure mode of the heterogeneous model released more energy than the shear failure mode of the homogeneous model. Then, the role of geological weak planes on rockburst occurrence and damage near the boundary of tunnels was studied using 2D models. Initially, a tunnel without any adjacent weak plane was modeled. Then a fault with different lengths, inclinations, and distances to the tunnel was added to the models and its effect on rock failure was simulated. The velocity and the released kinetic energy of failed rocks, the failure zone around the tunnel, and the deformed mesh were studied to identify stable and unstable rock failures. The simulation results showed that the presence of a fault near a tunnel could induce rockburst if the fault is positioned and oriented in such a way that it promotes high stress and low local loading system stiffness. Finally, a rockburst that occurred in the Jinping II drainage tunnel in China with an observed nearby fault was simulated. The modeling results captured the field observation of rockburst damage and confirmed that the presence of weak planes in the vicinity of deep tunnels is a necessary condition for the occurrence of rockburst. The finding from this research constitutes a better understanding of unstable rock failures in both laboratory and in situ. The insights gained from this research can be useful for rockburst anticipation and control during mining and tunneling in highly stressed grounds.Doctor of Philosophy (PhD) in Natural Resources Engineerin