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ABSTRACT Rock masses are usually in wet or saturated condition in the nature. Interaction of rocks with water leads to a reduction in rock mechanical properties, and quantifying this effect has been always a problem in rock engineering projects. Despite the fact that a considerable number of research has been carried out on this topic, they are not thorough and general. In this paper, a new rock classification system is presented in order to evaluate wet rock properties.
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock (0.47)
ABSTRACT Because of the inherent high nonlinearity of rock material, associated with the extreme complicity of mechanical and geological conditions, it is usually very difficult to model the response of rocks to engineering activities mathematically and physically. Recently, with integrated applications of artificial intelligence, system science, rock mechanics and engineering geology, an interesting alternative methodology, intelligent analysis methods, for recognition of rock mechanics models was proposed. This paper reviews the new developments of this kind of method and gives some prospects for further works.
ABSTRACT The prior knowledge of the rock mass behavior along a projected roadway is fundamental for planning activities and safety measures at a construction site. However, pre-investigations are often costly and time-consuming. To generate high resolution images of geotechnically important structures and changes in the rock mass, the Integrated Seismic Imaging System (ISIS) was developed at the GFZ. Seismic measurements offer detailed information on the rock mass, especially if the data acquisition takes place on-site during tunneling. However, to be of importance for the decision making on-site, the data needs to be processed and interpreted within a small timeframe. To meet this requirement the interpretation process needed to be automated. In the ONSITE project, a first step towards automating this process has been done by developing adapted routines with self-learning algorithms for rock mass classification based on seismic measurements. For the classification, the widely known RMR and RQD have been used so that a general idea about the rock mass behavior and not only single parameters can be gained from the results Based on the RMR, two rock mass classes were determined along seven seismic profiles from the Faido adit that belongs to the Gotthard base tunnel. The boundary between those classes was at 60 RMR which separates "fair" from "good" rock in the classification scheme. The RQD was separated into 3 classes, based on the number of occurrences, with either values in the range "excellent" (RQD>90), "good" (RQD 75 to 90) or "lower" (RQD<75). Both classification approaches using SVMs showed good training and testing accuracies, though the RQD was not as sensitive to the seismic velocities as had been expected.
- Geology > Rock Type (0.73)
- Geology > Geological Subdiscipline > Geomechanics (0.49)
- Geophysics > Seismic Surveying > Seismic Modeling (0.38)
- Geophysics > Seismic Surveying > Borehole Seismic Surveying (0.36)
- Geophysics > Seismic Surveying > Seismic Processing (0.35)
ABSTRACT Deformation modulus of a rock mass is one of the most important geomechanical parameters for a design and successful execution of rock engineering projects such as tunnels, dams, powerhouses. Field tests including plate loading, plate jacking, radial jacking, flat jack, etc, are the most commonly used methods to measure the deformation modulus of rock masses directly. However, these methods usually require extensive and difficult procedures and hence they are time consuming and costly. Thus, developing empirical equations to properly estimate the modulus of rock mass deformation on the basis of rock mass properties are of practical and economical advantageous. In this paper, at first, it is aimed at establishing an empirical predictive model for determination of the modulus of deformation based on GSI system using simple regression model. Then, the in situ plate loading test data and the rock mass properties are used to apply bivariate correlation analysis to determine the independent variables that affect on the modulus of deformation. Next, the results are used to develop a new empirical predictive model for determination of the rock mass deformation modulus with the aid of multiple regression analysis. This study includes a comprehensive credibility assessment of the prediction performances of some existing empirical equations as well. Subsequently, the results of the existing empirical equations were compared with that obtained by the equations proposed in this paper. Finally, it is concluded that the new empirical equation proposed in this paper provides more accurate results compared with the existing empirical equations.
- North America (0.46)
- Europe (0.46)
ABSTRACT In geological investigations, drill hole information can come from core and chip samples, the monitoring of drill performance and various forms of down-hole testing such as geophysical logging. Fusion of some of these forms of measurements is possible and can improve the geological understanding. In this paper we conduct fusion of drill monitoring data and geochemical assays of drill hole samples using Multiple Task Gaussian Processes (MTGPs). MTGPs are a popular statistical supervised learning and fusion technique in the machine learning community. The proposed algorithm autonomously learns the intrinsic interconnections between rock strength parameters and geochemistry and uses these interconnections to improve the quality of the geological model. We demonstrate the principles of our approach by fusing drill monitoring data from closely spaced blast hole drilling at an open pit iron ore mine and assay results from more widely spaced exploration drill holes. The drill monitoring data are represented by a parameter we call the Adjusted Penetration Rate (APR) and we observe a strong correlation between APR and iron grade from the assays. Fusion allows a more detailed geological model to be obtained.
- Geology > Geological Subdiscipline > Geochemistry (0.55)
- Geology > Geological Subdiscipline > Geomechanics (0.51)
ABSTRACT In this paper, the application of Artificial Neural Networks (ANNs) as a basis for new generation of rock failure criteria is illustrated. As an example, a typical series of the results of triaxial compression tests on Indiana limestone were fitted using an ANN. In order to evaluate the relative accuracy of the trained ANN, two well-known conventional criteria of Mohr-Coulomb and Hoek-Brown were also used to fit the data. It was observed that the ANN-based criterion can give more accurate predictions of strength for both brittle and ductile failure modes. Subsequently, the explicit formulation of the ANN-based criterion and equations for instantaneous values of an equivalent Mohr-Coulomb criterion were derived. Finally, the formulas were incorporated in numerical simulations of triaxial compression tests and circular tunnels in anisotropic in-situ stress fields. The accurate results of these simulations showed that ANN-based failure criteria can be successfully implemented in numerical analyses.
ABSTRACT The global stability of underground rock cavern excavated in a Mohr-Coulomb material is investigated by means of Universal Distinct Element Code (UDEC) in this paper. The following stochastic variables are considered: the friction angle, the cohesion, the deformation modulus of the rock mass and the in-situ stress ratio. The overburden thickness, the unit weight of rock materials, the Poisson's ratio and the joint strength are assumed as deterministic. The cavern width and height are also assumed as uncertain variables in order to optimize the shape of the rock cavern. Most cavern shapes are horse-shoe or bullet-head shaped. It is widely argued that the use of a flat-arch cavern would make the best use of cavern space. Thus for this study, the initial cavern width and height are set as 30m and 18 m, respectively. The influences of the flattening process on cavern stability can be investigated through incremental increases in the cavern width and reduction in the cavern height through six design levels to assess the changes of safety factor and probability of failure. For each configuration of the six levels, the probability of failure is determined by Monte Carlo simulation incorporated by neural network results. The configuration satisfying the critical safety factor and the expected performance level with the flattest cavern roof can be termed as the optimal design. It is also suggested that the critical factor of safety and the targeted performance level be used together, as complementary measures of acceptable design.
- Government > Military > Army (0.69)
- Government > Regional Government > North America Government > United States Government (0.47)
Influence of Confinement Dependent Failure Processes on Rock Mass Strength at Depth
Valley, Benoît (MIRARCO/Geomechanics Research Center) | Kim, Bo-Hyun (MIRARCO/Geomechanics Research Center) | Suorineni, Fidelis T. (MIRARCO/Geomechanics Research Center) | Bahrani, Navid (MIRARCO/Geomechanics Research Center) | Bewick, Rob P. (MIRARCO/Geomechanics Research Center) | Kaiser, Peter K. (CEMI – Center for Excellence in Mining Innovation)
ABSTRACT Changes of failure mechanism with increasing confinement, from tensile to shear dominated failure, is widely observed in the rupture of samples in laboratory and in rock masses in situ. However, common failure criteria typically consider only shear mechanisms. A hybrid criteria based on a sigmoid function is introduced to account for a transition from tensile to shear dominated failure with increasing confinement. When evaluated by fitting to an extensive laboratory database the sigmoid criteria does not provide a better fit compared to the Hoek-Brown failure envelope, but provides insight into rock strength controlling factors that have significant consequences with respect to the interpretation of laboratory test results. It also leads to a differentiated approach for design by considering two types of behaviour process:in the inner shell, i.e. the direct vicinity of openings, the failure mode is dominated by tensile cracking leading to spalling and related geometric dilation processes and in the outer shell, i.e. remote from excavations, where confinement promotes interlock, we suggest that rock masses could be significantly stronger than predicted by standard approaches.
- Geology > Rock Type (1.00)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
ABSTRACT The uniaxial compressive strength and modulus of elasticity of intact rocks are vital parameters in designing rock engineering projects. Determination of these mechanical properties from laboratory tests requires high-quality core samples which are sometimes difficult to obtain. Therefore, the predictive models are often employed for the indirect estimation of these parameters. In this study, artificial neural networks technique is selected for predicting the uniaxial compressive strength and modulus of elasticity of intact rocks at the same time. The optimum ANN architecture has been found to be 7 neurons in input layer, two hidden layers with 25 and 15 neurons, respectively and two neurons in output layer. For this purpose 126 data sets including uniaxial compressive strength, modulus of elasticity, Schmidt hammer, point load index, sound velocity, physical properties, and tensile strength tests were applied. Several univariate and multiple regression models were developed as well. Some indices have been employed so as to control prediction performance of models. As a result, performance indices reveled that the artificial neural network model exhibited high prediction capacity and can be efficiently used to estimate mechanical properties of rock. In the final stage the canonical correlation method was utilized to study the relationship between desired parameters and engineering index tests.
ABSTRACT Nowadays, acoustic emission testing based on the Kaiser Effect is increasingly used for estimating in-situ stress in laboratories. The present paper proposes an automatic method based on pattern recognition methods, which accurately determines Kaiser Effect by combining the results of different common methods. The proposed method uses the acoustic emission parameters obtained from uniaxial compressive tests on pre-loaded sandstone samples. In the proposed new method, previous maximum stresses in samples can be determined directly without analysis of waveform signals. Our results show that pattern recognition methods can be used for determining the point of Kaiser Effect. The developed method has a desirable level of precision for determining the Kaiser Effect point.