Common ground in engineering geology, soil mechanics and rock mechanics: past, present and future

Engineering geology, together with soil mechanics and rock mechanics, is commonly considered to be one of the three fundamental scientific disciplines in ground engineering. Historically, the interrelation between these three disciplines has never been free of ambiguity. This, for instance, is highlighted by the fact that both Karl von Terzaghi, the founder of soil mechanics, and Leopold Müller, the founder of rock mechanics, considered themselves foremost as engineering geologists without, however, succeeding in establishing engineering geology as a free-standing discipline with autonomous intellectual merits, methods and procedures. This situation has changed recently as evidenced in Knill’s fundamental publication (2002) on Core Values in Engineering Geology and by the fact that the relevant three International Societies are currently in the process of moving together towards a “Federation of Geo-Engineering Societies”.An erratum to this article can be found at     

An applied artificial intelligence approach towards assessing building performance simulation tools

With the development of modern computer technology, a large amount of building energy simulation tools is available in the market. When choosing which simulation tool to use in a project, the user must consider the tool's accuracy and reliability, considering the building information they have at hand, which will serve as input for the tool. This paper presents an approach towards assessing building performance simulation results to actual measurements, using artificial neural networks (ANN) for predicting building energy performance. Training and testing of the ANN were carried out with energy consumption data acquired for 1 week in the case building called the Solar House. The predicted results show a good fitness with the mathematical model with a mean absolute error of 0.9%. Moreover, four building simulation tools were selected in this study in order to compare their results with the ANN predicted energy consumption: Energy_10, Green Building Studio web tool, eQuest and EnergyPlus. The results showed that the more detailed simulation tools have the best simulation performance in terms of heating and cooling electricity consumption within 3% of mean absolute error.     

Advances in statistical mechanics of rock masses and its engineering applications

To efficiently link the continuum mechanics for rocks with the structural statistics of rock masses,a theoretical and methodological system called the statistical mechanics of rock masses(SMRM)was developed in the past three decades.In SMRM,equivalent continuum models of stressestrain relationship,strength and failure probability for jointed rock masses were established,which were based on the geometric probability models characterising the rock mass structure.This follows the statistical physics,the continuum mechanics,the fracture mechanics and the weakest link hypothesis.A general constitutive model and complete stressestrain models under compressive and shear conditions were also developed as the derivatives of the SMRM theory.An SMRM calculation system was then developed to provide fast and precise solutions for parameter estimations of rock masses,such as full-direction rock quality designation(RQD),elastic modulus,Coulomb compressive strength,rock mass quality rating,and Poisson’s ratio and shear strength.The constitutive equations involved in SMRM were integrated into a FLAC3D based numerical module to apply for engineering rock masses.It is also capable of analysing the complete deformation of rock masses and active reinforcement of engineering rock masses.Examples of engineering applications of SMRM were presented,including a rock mass at QBT hydropower station in northwestern China,a dam slope of Zongo II hydropower station in D.R.Congo,an open-pit mine in Dexing,China,an underground powerhouse of Jinping I hydropower station in southwestern China,and a typical circular tunnel in Lanzhou-Chongqing railway,China.These applications verified the reliability of the SMRM and demonstrated its applicability to broad engineering issues associated with jointed rock masses.     

A generic method for rock mass classification

Rock mass classification (RMC) is of critical importance in support design and applications to mining, tunneling and other underground excavations. Although a number of techniques are available, there exists an uncertainty in application to complex underground works. In the present work, a generic rock mass rating (GRMR) system is developed. The proposed GRMR system refers to as most commonly used techniques, and two rock load equations are suggested in terms of GRMR, which are based on the fact that whether all the rock parameters considered by the system have an influence or only few of them are influencing. The GRMR method has been validated with the data obtained from three underground coal mines in India. Then, a semi-empirical model is developed for the GRMR method using artificial neural network (ANN), and it is validated by a comparative analysis of ANN model results with that by analytical GRMR method.     

A review of techniques, advances and outstanding issues in numerical modelling for rock mechanics and rock engineering

The purpose of this review paper is to present the techniques, advances, problems and likely future developments in numerical modelling for rock mechanics. Such modelling is essential for studying the fundamental processes occurring in rocks and for rock engineering design. The review begins by explaining the special nature of rock masses and the consequential difficulties when attempting to model their inherent characteristics of discontinuousness, anisotropy, inhomogeneity and inelasticity. The rock engineering design backdrop to the review is also presented. The different types of numerical models are outlined in Section 2, together with a discussion on how to obtain the necessary parameters for the models. There is also discussion on the value that is obtained from the modelling, especially the enhanced understanding of those mechanisms initiated by engineering perturbations. In Section 3, the largest section, states-of-the-art and advances associated with the main methods are presented in detail. In many cases, for the model to adequately represent the rock reality, it is necessary to incorporate couplings between the thermal, hydraulic and mechanical processes. The physical processes and the equations characterizing the coupled behaviour are included in Section 4, with an illustrative example and discussion on the likely future development of coupled models. Finally, in Section 5, the advances and outstanding issues in the subject are listed and in Section 6 there are specific recommendations concerning quality control, enhancing confidence in the models, and the potential future developments.     

Overhanging rock slope by design: An integrated approach using rock mass strength characterisation,large-scale numerical modelling and limit equilibrium methods

Overhanging rock slopes(steeper than 90°) are typically avoided in rock engineering design, particularly where the scale of the slope exceeds the scale of fracturing present in the rock mass. This paper highlights an integrated approach of designing overhanging rock slopes where the relative dimensions of the slope exceed the scale of fracturing and the rock mass failure needs to be considered rather than kinematic release of individual blocks. The key to the method is a simplified limit equilibrium(LE) tool that was used for the support design and analysis of a multi-faceted overhanging rock slope. The overhanging slopes required complex geometries with constantly changing orientations. The overhanging rock varied in height from 30 m to 66 m. Geomechanical modelling combined with discrete fracture network(DFN)representation of the rock mass was used to validate the rock mass strength assumptions and the failure mechanism assumed in the LE model. The advantage of the simplified LE method is that buttress and support design iterations(along with sensitivity analysis of design parameters) can be completed for various cross-sections along the proposed overhanging rock sections in an efficient manner, compared to the more time-intensive, sophisticated methods that were used for the initial validation. The method described presents the development of this design tool and assumptions made for a specific overhanging rock slope design. Other locations will have different geological conditions that can control the potential behaviour of rock slopes, however, the approach presented can be applied as a general guiding design principle for overhanging rock cut slope.     

A comparative study on the application of various artificial neural networks to simultaneous prediction of rock fragmentation and backbreak

In blasting operation, the aim is to achieve proper fragmentation and to avoid undesirable events such as backbreak. Therefore, predicting rock fragmentation and backbreak is very important to arrive at a technically and economically successful outcome. Since many parameters affect the blasting results in a complicated mechanism, employment of robust methods such as artificial neural network may be very useful. In this regard, this paper attends to simultaneous prediction of rock fragmentation and backbreak in the blasting operation of Tehran Cement Company limestone mines in Iran. Back propagation neural network (BPNN) and radial basis function neural network (RBFNN) are adopted for the simulation. Also, regression analysis is performed between independent and dependent variables. For the BPNN modeling, a network with architecture 6-10-2 is found to be optimum whereas for the RBFNN, architecture 6-36-2 with spread factor of 0.79 provides maximum prediction aptitude. Performance comparison of the developed models is fulfilled using value account for (VAF), root mean square error (RMSE), determination coefficient (R²) and maximum relative error (MRE). As such, it is observed that the BPNN model is the most preferable model providing maximum accuracy and minimum error. Also, sensitivity analysis shows that inputs burden and stemming are the most effective parameters on the outputs fragmentation and backbreak, respectively. On the other hand, for both of the outputs, specific charge is the least effective parameter.     

Revisiting rock classification to estimate rock mass properties

This paper presents the results of ongoing research carried out by the author exploring methods to provide a more robust estimate of rock mass properties specifically for use in tunnel design. Data from various large-scale rock mass failures are introduced, including coal pillars. The damage-initiation,spalling-limit approach is compared to the coal pillar database. New comparisons of estimating the geological strength index(GSI) and relationships to estimate the Hoeke Brown failure criterion parameters, mb, s and a, are presented.     

Numerical modeling of thermally-induced fractures in a large rock salt mass

Numerical modeling of thermally-induced fractures is a concern for many geo-structures including deep underground energy storage caverns. In this paper, we present the numerical simulation of a large-scale cooling experiment performed in an underground rock salt mine. The theory of fracture mechanics was embedded in the extended finite element code used. The results provide reliable information on fracture location and fracture geometry. Moreover, the timing of the fracture onset, as well as the stress redistribution due to fracture propagation, is highlighted. The conclusions of this numerical approach can be used to improve the design of rock salt caverns in order to guarantee their integrity in terms of both their tightness and stability.     

人工智能新技术在智能建筑中的应用研究

本文分析了人工智能新技术的应用,指出当前引入人工智能技术实现建筑系统智能控制与管理已成为可能。     

Study of scale effect on intact rock strength using particle flow modeling

Based on the extensive review of the UCS versus specimen size test data and the various empirical relations between the UCS and the specimen size, a new expression is proposed to describe the dependence of the UCS on specimen volume. The proposed new relation can fit the UCS versus specimen size test data of different rocks very well. Then, a numerical study of the scale effect on UCS is conducted using a numerical model in which the intact rock is represented by particles bonded to each other at contact points, with the contact bonds having both normal and shear strength components. The bond can break if the normal or shear contact stress exceeds the corresponding bond strength. To simulate the initial micro-fractures (flaws or cracks) in the rock, the smooth-joint contact model is used. The fractures are considered to be randomly orientated and located disks. The size and number of fractures are described by an exponential expression derived using fractal theory. The numerical model is calibrated using the test stress–strain curves of 80 mm×40 mm×40 mm prism Yamaguchi marble samples. Then, the calibrated model is used to predict the UCS of Yamaguchi marble samples at different sizes. The predicted UCS values are in good agreement with the experimental values. The numerical simulations show that to capture the scale effect on UCS of intact rock, initial fractures with sizes increasing faster with the specimen size must be considered in the modeling.     

Application of autocorrelation analysis for interpreting acoustic emission in rock

The statistical properties of acoustic emission from rock samples were studied as a function of applied uniaxial load. It was found that the parameters of the autocorrelation function of the acoustic emission event series change significantly near failure. An increase in the values of the autocorrelation coefficients and a tendency to a linear decrease with time were observed. We propose that the increasing autocorrelation of the acoustic emission series is an evidence of the increased affect that the individual acoustic emission sources have on one another. This mutual effect of acoustic events arises as a result of the redistribution of stress in the sample during the fracturing process at higher loads (more than 95% of ultimate strength). The results support the possibility of using autocorrelation analysis as a failure warning sign or even to predict the sample's total failure. Different rock materials and various loading patterns were used to generalise the results obtained.     

Effects of confinement on rock mass modulus: A synthetic rock mass modelling (SRM) study

The main objective of this paper is to examine the influence of the applied confining stress on the rock mass modulus of moderately jointed rocks (well interlocked undisturbed rock mass with blocks formed by three or less intersecting joints). A synthetic rock mass modelling (SRM) approach is employed to determine the mechanical properties of the rock mass. In this approach, the intact body of rock is represented by the discrete element method (DEM)-Voronoi grains with the ability of simulating the initiation and propagation of microcracks within the intact part of the model. The geometry of the pre-existing joints is generated by employing discrete fracture network (DFN) modelling based on field joint data collected from the Brockville Tunnel using LiDAR scanning. The geometrical characteristics of the simulated joints at a representative sample size are first validated against the field data, and then used to measure the rock quality designation (RQD), joint spacing, areal fracture intensity (P₂₁), and block volumes. These geometrical quantities are used to quantitatively determine a representative range of the geological strength index (GSI). The results show that estimating the GSI using the RQD tends to make a closer estimate of the degree of blockiness that leads to GSI values corresponding to those obtained from direct visual observations of the rock mass conditions in the field. The use of joint spacing and block volume in order to quantify the GSI value range for the studied rock mass suggests a lower range compared to that evaluated in situ. Based on numerical modelling results and laboratory data of rock testing reported in the literature, a semi-empirical equation is proposed that relates the rock mass modulus to confinement as a function of the areal fracture intensity and joint stiffness.     

Time-dependent drift degradation due to the progressive failure of rock bridges along discontinuities

An important element of time-dependent drift degradation is the progressive failure of intact segments along discontinuities, referred to as rock bridges. A fracture mechanics model is developed to simulate the time-dependent failure of rock bridges along discontinuities. The time dependence of the rock bridge failure process is modeled utilizing subcritical crack growth. The rock bridges give an effective cohesion to the discontinuities, and this cohesion is time-dependent due to the time-dependent failure of the rock bridges. The resulting first-order differential equation for joint cohesion is implemented into the UDEC distinct element numerical code to model time-dependent drift degradation. The model and its implementation into UDEC are validated using several simple examples, including a direct shear test and a rigid block on a slope. Two time-dependent drift degradation examples are then shown, one with and one without thermal loading. These examples used similar geometry, material parameters and in situ stresses as for the proposed underground drifts for the storage of nuclear waste at Yucca Mountain. Both with and without thermal loading, a large zone develops around the excavation where the joint cohesion and tensile strength drop to zero due to the failure of rock bridges. This in turn results in an excavation that is significantly less stable than if time dependence was not included. The results demonstrate the importance of time-dependence on the stability of underground excavations in hard rock.     

Application of artificial neural networks for predicting the cuttability of rocks by drag tools

Many models have previously been developed for predicting specific cutting energy (SE), being the measure of rock cuttability, from intact rock properties employing conventional multiple linear or nonlinear regression techniques. Artificial neural networks (ANN) also have a great potential in building such models. This paper is concerned with the application of ANN for the prediction of cuttability of rocks from their intact properties. For that purpose, data obtained from three different projects were subjected to statistical analyses using MATLAB. Principal components analysis together with the scatterplots of SE against intact rock properties were employed to select the predictors for SE models. Results of the principal components analysis have shown that the most of the variance in the data set can be explained by three principal components. Principal component with the highest variance is weighted mainly on the uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), static modulus of elasticity (Elasticity), and cone indenter hardness (CI), which were regarded as the independent variables driving the data set. Three predictive models for SE were developed employing above independent variables by multiple nonlinear regression with forward stepwise method and ANN, respectively. Neural networks were developed for two different numbers of hidden neurons in the hidden layer. Goodness of the fit measures revealed that ANN models fitted the data as accurately as multiple nonlinear regression model, indicating the usefulness of artificial neural networks in predicting rock cuttability.     

Application of microbially induced carbonate precipitation to form biocemented artificial sandstone

It is difficult to collect and characterise well-preserved samples of weakly-cemented granular rocks as conventional sampling techniques o ften result in destruction of the cementation.An alternative approach is to prepare synthetic geomaterials to match required specifications.This paper introduces microbially induced carbonate precipitation(MICP) as a method to reliably deliverartificially cemented specimens with customised properties,closely resembling those of so ft carbonate sandstones.The specimens are generated from materials with two highly different particle size distributions(PSDs) to access a range of achievable combinations of strengths and porosities.The MICP parameters are kept constant across all samples to obtain similar calcium carbonate characteristics(size of individual crystals,type,etc.),while injected volume is varied to achieve different cementation levels.Although uniform cementation of very coarse sands has been considered very difficult to achieve,the results show that both the fine and coarse sand specimens present high degrees of uniformity and a good degree of repeatability.The unconfined compressive strengths(UCSs)(less than 3000 kPa) and porosities(0.25-0.4) of the artificial specimens fall in the same range of values reported for natural rocks.The strength gain was greater in the fine sand than that in the coarse sand,as the void size in the latter was significantly larger compared to the calcium carbonate crystals' size,resulting in precipitation on less effective locations,away from contacts between particles.The strengths and porosities obtained for the two sands in this work fall within ranges reported in the literature for natural soft rocks,demonstrating the MICP technique is able to achieve realistic properties and may be used to produce a full range of properties by varying the grain sizes,and possibly the width of PSD.     

Interaction of shotcrete with rock and rock bolts—A numerical study

The shotcrete–rock interaction is very complex and is influenced by a number of factors. The influence of the following factors was investigated by a series of numerical analyses: the surface roughness of the opening, the rock strength and Young's modulus, the discontinuities, the extent and properties of the excavated disturbed zone, the mechanical properties of the interface between shotcrete and rock, and the thickness of the shotcrete lining and the rock bolts. The study was carried out as a sensitivity analysis. The results showed that the rock strength and the surface roughness had significant impact on the number of failures at the rock–shotcrete interface and in the shotcrete lining. Furthermore, the behaviour of the lining is sensitive to small amplitudes of the surface roughness. In all the cases investigated, a high interface strength was favourable. The results indicate that if a thick shotcrete lining is dependent on the bond strength. The benefit of using a thicker lining can be doubtful. The analyses showed that for an uneven surface the extent of the EDZ had a minor effect on the behaviour of the shotcrete lining. Furthermore, if rock bolts were installed at the apex of the protrusion instead of at the depression, the number of failures decreased both at the interface and in the lining.     

Application of non-linear regression analysis and artificial intelligence algorithms for performance prediction of hard rock TBMs

Prediction of machine performance is an essential step for planning, cost estimation and selection of excavation method to assure success of tunneling operation by hard rock TBMs. Penetration rate is a principal measure of TBM performance and is used to evaluate the feasibility of using a machine in a given ground condition and to predict TBM advance rate. In this study, a database of TBM field performance from two hard rock tunneling projects in Iran including Zagros lot 1B and 2 for a total length of 14.3 km has been used to assess applicability of various analysis methods for developing reliable predictive models. The first method used for this purpose was principal component analysis (PCA) which resulted in development of a set of new empirical equations. Also, two Soft computing techniques including adaptive neuro-fuzzy inference system (ANFIS) and support vector regression (SVR) have been employed for this purpose. As statistical indices, root mean square error (RMSE), correlation coefficient (R²), variance account for (VAF), and mean absolute percentage error (MAPE) were used to evaluate the efficiency of the developed artificial intelligence models for TBM performance prediction. The results of the analysis show that AI based methods can effectively be implemented for prediction of TBM performance. Moreover, it was concluded that performance of the SVR model is better than the ANFIS model. A high correlation was observed between predicted and measured TBM performance for the SVR model. This study shows the feasibility of using these systems and subsequent work is underway to expand the database of TBM field performance and use the aforementioned methods to develop a more comprehensive TBM performance prediction model.     

On applications of artificial intelligence to the control and safety problems of nuclear power plants

The feasibility of applying some artificial intelligence (Al) techniques to plant control and safety problems is investigated, primarily from the point of view of plant safety. It is observed that the control room of the plant operates at different information-flux levels and that the change from one level to another is of a quantum nature. A representation of this jump in terms of an analogy to the Reynold's number of fluid mechanics is described. Two Al techniques are described. In the first of these an attention-structuring device is constructed for simulating an ongoing and routine operation of the plant. The effect of the attention structure construction upon response to an unexpected disturbance is investigated using this device. The second technique, corresponding to a learn-ingfaulttree, is more applicable to special-incident situations.     

Reliability-based design in rock engineering: Application of Bayesian regression methods to rock strength data

Reliability-based design(RBD) is being adopted by geotechnical design codes worldwide, and it is therefore necessary that rock engineering practice evolves to embrace RBD. This paper examines the Hoek-Brown(He B) strength criterion within the RBD framework, and presents three distinct analyses using a Bayesian approach. Firstly, a compilation of intact compressive strength test data for six rock types is used to examine uncertainty and variability in the estimated He B parameters m and sc, and corresponding predicted axial strength. The results suggest that within-and between-rock type variabilities are so large that these parameters need to be determined from rock testing campaigns, rather than reference values being used. The second analysis uses an extensive set of compressive and tensile(both direct and indirect) strength data for a granodiorite, together with a new Bayesian regression model, to develop joint probability distributions of m and scsuitable for use in RBD. This analysis also shows how compressive and indirect tensile strength data may be robustly used to fit an He B criterion.The third analysis uses the granodiorite data to investigate the important matter of developing characteristic strength criteria. Using definitions from Eurocode 7, a formal Bayesian interpretation of characteristic strength is proposed and used to analyse strength data to generate a characteristic criterion.These criteria are presented in terms of characteristic parameters mkand s ck, the values of which are shown to depend on the testing regime used to obtain the strength data. The paper confirms that careful use of appropriate Bayesian statistical analysis allows the He B criterion to be brought within the framework of RBD. It also reveals that testing guidelines such as the International Society for Rock Mechanics and Rock Engineering(ISRM) suggested methods will require modification in order to support RBD. Importantly, the need to fully understand the implications of uncertainty in nonlinear strength criteria is identified.