Prediction of the Rock Mass Diggability Index by Using Fuzzy Clustering-Based,ANN and Multiple Regression Methods

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools.     

Neuro-fuzzy modeling based genetic algorithms for identification of geochemical anomalies in mining geochemistry

A genetic algorithm (GA)-based neuro-fuzzy approach is used for identification of geochemical anomalies by implementing a Takagi, Sugeno and Kang (TSK) type fuzzy inference system in a 5-layered feed-forward adaptive artificial neural network. This paper investigates the effectiveness of GA-based neuro-fuzzy for separating zone dispersed mineralization (ZDM) from blind mineralization, and its application for identification of geochemical anomalies in the arid landscape of the Lut metallogenic province in eastern Iran. Other classification algorithms such as metallometry, zonality, criteria, and back-propagation artificial neural network classifiers are also used for comparison. The genetic operators are carefully designed to optimize the artificial neural network, avoiding premature convergence and permutation problems. The results show that the GA-based hybrid neuro-fuzzy model can provide accurate results in comparison with those results obtained by other techniques. Neuro-fuzzy and GA-based neuro-fuzzy techniques appear to be well-suited for routine exploration geochemistry applications. In conjunction with statistics and conventional mathematical methods, hybrid approaches can be developed and may prove a step forward in the practice of applied geochemistry.     

危岩稳定性计算新体系

地质灾害防治研究中,危岩的研究相对较为薄弱,稳定性计算模型体系不够完善。本文在五个常用计算模型的基础上,增加了后缘有贯通陡倾裂隙的滑移式、双结构面滑移式、后缘无陡倾裂隙且形似悬臂梁的坠落式、上缘岩体抗拉强度控制的坠落式四个新的计算模型,形成了危岩稳定性计算模型新体系。依托工程实例对后缘有贯通陡倾裂隙的滑移式危岩新计算模型的稳定性进行运用验证,即:基于刚体力学的极限平衡法计算求出不同工况下危岩单体的稳定性系数,再基于FLAC3D运用摩尔-库伦模型对该危岩单体进行数值模拟,计算求出其在两种不同工况的稳定系数,两种方法相互印证其可靠性。     

The effective failure modes and stability of slopes in rock mass with two discontinuity sets

Summary The stability of rock slopes in discontinuous rock mass associated with the construction of power plants, highways and open pits is always of paramount importance during the lifetime of these structures. The likely forms of instabilities observed in the excavation of rock slopes and some mathematical methods for the stability analyses are well documented in literature. Since most of the mathematical approaches used are based on the limiting-equilibrium concept, there seems a need to check the validity of these approaches under some controlled conditions. In this paper, the authors describe methods for the stability of a blocky column and discontinuous rock slopes derived on the basis of dynamic equilibrium equations and compare the results calculated according to the developed method with those of experiments on model blocky columns and model slopes in the laboratory. Test results confirm that the limiting equilibrium approache is valid and an effective way of dealing with the stability problems in discontinuous rock mass as long as the likely forms of instability are properly treated in these approaches.     

基于ABAQUS-ANFIS-MCS的岩质边坡可靠性分析

针对岩质边坡工程稳定性分析中参数的不确定性,基于ABAQUA建立了平面破坏型边坡有限元分析模型。并用该模型进行了边坡稳定状态的数值模拟,以获得进行ANFIS分析的数据。同时基于自适应神经模糊推理系统建立了岩体力学参数与边坡抗滑力和下滑力的映射模型,分析得到抗滑力和下滑力的统计特征。根据蒙特卡罗模拟方法用MATLAB语言编写了求解边坡的破坏概率和可靠度的计算程序,对湖南雪峰水泥原料矿山的露天矿边坡进行可靠度分析。研究结果表明,该方法具有避免编写冗长的有限元计算程序、节省机时、计算精度高的优点。     

基于灰色系统理论的改进SMR法初探

综合灰色系统理论与传统的边坡岩体质量分级方法(SMR法),提出改进SMR法。传统的岩体质量分级方法中定量指标取值离散性很大,造成质量分级结果阶梯变化。灰色系统理论的灰度特征对解决这类小样本、离散性的问题有很好的适用性。首先对传统质量分级方法的评价指标进行灰类划分,确定各指标所占权重,再构建评价指标的三角白化权函数,并基于最大隶属度准则对边坡岩体进行质量分级。最后结合工程边坡实例,与一般工程RMR(岩体质量分级)与SMR法比较,改进SMR法的评价结果更加吻合工程现状,且质量分级稳定性高,表明其应用于边坡岩体质量分级是科学和准确的。     

基于天然红层材料的软岩成型模拟研究

岩石模拟是岩土工程模型试验、地质岩芯模拟试验等研究的核心。但目前基于人工材料的模拟制备受限于现有相似理论与技术手段,成岩结果与实际岩性差异较大,特别是软岩的成型模拟问题尤为突出。以天然红层材料为原料,改进传统的成岩模拟系统,考虑成岩过程中温度、孔隙流体压力及上覆压力的分阶段影响,模拟红层软岩从松散岩土颗粒到岩石的形成过程,得到工程标准尺寸软岩岩芯。通过与天然红层软岩进行成岩过程、物理性质、化学性质及力学性质的对比研究表明,以天然红层为原料的软岩岩芯与天然红层软岩性质相似。该研究突破了人工材料配制、3D打印等方法一般只能满足某一方面性质的局限,为大量不同功能需求的软岩岩芯研究提供了新的制作思路与方法。     

Fuzzy set approaches to classification of rock masses

Rock mass classification is analogous to multi-feature pattern recognition problem. The objective is to assign a rock mass to one of the pre-defined classes using a given set of criteria. This process involves a number of subjective uncertainties stemming from: (a) qualitative (linguistic) criteria; (b) sharp class boundaries; (c) fixed rating (or weight) scales; and (d) variable input reliability. Fuzzy set theory enables a soft approach to account for these uncertainties by allowing the expert to participate in this process in several ways. Hence, this study was designed to investigate the earlier fuzzy rock mass classification attempts and to devise improved methodologies to utilize the theory more accurately and efficiently. As in the earlier studies, the Rock Mass Rating (RMR) system was adopted as a reference conventional classification system because of its simple linear aggregation.

The proposed classification approach is based on the concept of partial fuzzy sets representing the variable importance or recognition power of each criterion in the universal domain of rock mass quality. The method enables one to evaluate rock mass quality using any set of criteria, and it is easy to implement. To reduce uncertainties due to project- and lithology-dependent variations, partial membership functions were formulated considering shallow (<200 m) tunneling in granitic rock masses. This facilitated a detailed expression of the variations in the classification power of each criterion along the corresponding universal domains. The binary relationship tables generated using these functions were processed not to derive a single class but rather to plot criterion contribution trends (stacked area graphs) and belief surface contours, which proved to be very satisfactory in difficult decision situations. Four input scenarios were selected to demonstrate the efficiency of the proposed approach in different situations and with reference to the earlier approaches.     

A coupled hydromechanical system defined through rock mass classification schemes

Changes in the hydraulic conductivity field, resulting from the redistribution of stresses in fractured rock masses, are difficult to characterize due to complex nature of the coupled hydromechanical processes. A methodology is developed to predict the distributed hydraulic conductivity field based on the original undisturbed parameters of hydraulic conductivity, Rock Mass Rating (RMR), Rock Quality Designation (RQD), and additionally the induced strains. The most obvious advantage of the methodology is that these required parameters are minimal and are readily available in practice. The incorporation of RMR and RQD, both of which have been applied to design in rock engineering for decades, enables the stress-dependent hydraulic conductivity field to be represented for a whole spectrum of rock masses. Knowledge of the RQD, together with the original hydraulic conductivity, is applied to determine the effective porosity for the fractured media. When RQD approaches zero, the rock mass is highly fractured, and fracture permeability will be relatively high. When RQD approaches 100, the degree of fracturing is minimal, and secondary porosity and secondary permeability will be low. These values bound the possible ranges in hydraulic behaviour of the secondary porosity within the system. RMR may also be applied to determine the scale effect of elastic modulus. As RMR approaches 100, the ‘softening’ effect of fractures is a minimum and results in the smallest strain-induced change in the hydraulic conductivity because the induced strain is uniformly distributed between fractures and matrix. When RMR approaches zero, the laboratory modulus must be reduced significantly in order to represent the rock mass. This results in the largest possible change in the hydraulic conductivity because the induced strain is applied entirely to the fracture system. These values of RMR bound the possible ranges in mechanical behaviour of the system. The mechanical system is coupled with the hydraulic system by two empirical parameters, RQD and RMR. The methodology has been applied to a circular underground excavation and to qualitatively explain the in situ experimental results of the macropermeability test in the drift at Stripa. Copyright © 1999 John Wiley & Sons, Ltd.     

Prediction of daily suspended sediment load using wavelet and neurofuzzy combined model

This study investigated the prediction of suspended sediment load in a gauging station in the USA by neuro-fuzzy, conjunction of wavelet analysis and neuro-fuzzy as well as conventional sediment rating curve models. In the proposed wavelet analysis and neuro-fuzzy model, observed time series of river discharge and suspended sediment load were decomposed at different scales by wavelet analysis. Then, total effective time series of discharge and suspended sediment load were imposed as inputs to the neuro-fuzzy model for prediction of suspended sediment load in one day ahead. Results showed that the wavelet analysis and neuro-fuzzy model performance was better in prediction rather than the neuro-fuzzy and sediment rating curve models. The wavelet analysis and neuro-fuzzy model produced reasonable predictions for the extreme values. Furthermore, the cumulative suspended sediment load estimated by this technique was closer to the actual data than the others one. Also, the model could be employed to simulate hysteresis phenomenon, while sediment rating curve method is incapable in this event.     

A Hybrid Neuro-Fuzzy Model for Mineral Potential Mapping

A GIS-based hybrid neuro-fuzzy approach to mineral potential mapping implements a Takagi–Sugeno type fuzzy inference system in a four-layered feed-forward adaptive neural network. In this approach, each unique combination of predictor patterns is considered a feature vector whose components are derived by knowledge-based ordinal encoding of the constituent predictor patterns. A subset of feature vectors with a known output target vector (i.e., unique conditions known to be associated with either a mineralized or a barren location), extracted from a set of all feature vectors, is used for the training of an adaptive neuro-fuzzy inference system. Training involves iterative adjustment of parameters of the adaptive neuro-fuzzy inference system using a hybrid learning procedure for mapping each training vector to its output target vector with minimum sum of squared error. The trained adaptive neuro-fuzzy inference system is used to process all feature vectors. The output for each feature vector is a value that indicates the extent to which a feature vector belongs to the mineralized class or the barren class. These values are used to generate a favorability map. The procedure is applied to regional-scale base metal potential mapping in a study area located in the Aravalli metallogenic province (western India). The adaptive neuro-fuzzy inference system demarcates high favorability zones occupying 9.75% of the study area and identifies 96% of the known base metal deposits. This result is significant both in terms of reduction in search area and the percentage of deposits identified.     

A study of soft computing models for prediction of longitudinal wave velocity

Genetic algorithm (GA) and support vector machine (SVM) optimization techniques are applied widely in the area of geophysics, civil, biology, mining, and geo-mechanics. Due to its versatility, it is being applied widely in almost every field of engineering. In this paper, the important features of GA and SVM are discussed as well as prediction of longitudinal wave velocity and its advantages over other conventional prediction methods. Longitudinal wave measurement is an indicator of peak particle velocity (PPV) during blasting and is an important parameter to be determined to minimize the damage caused by ground vibrations. The dynamic wave velocity and physico-mechanical properties of rock significantly affect the fracture propagation in rock. GA and SVM models are designed to predict the longitudinal wave velocity induced by ground vibrations. Chaos optimization algorithm has been used in SVM to find the optimal parameters of the model to increase the learning and prediction efficiency. GA model also has been developed and has used an objective function to be minimized. A parametric study for selecting the optimized parameters of GA model was done to select the best value. The mean absolute percentage error for the predicted wave velocity (V) value has been found to be the least (0.258 %) for GA as compared to values obtained by multivariate regression analysis (MVRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and SVM.     

3D-DIC系统在岩石力学试验中的应用

介绍了全场3D-DIC系统,在试样周围均匀布置三组3D采集元,利用散斑图像的采集,尽可能在较大范围内捕捉试样表面的变形并进行分析。为了验证3D-DIC系统测量结果的可靠性,以江持安山岩为试验材料,进行了单轴压缩条件下的恒定应变速率和交替应变速率试验。通过3D-DIC系统的测量结果与应变片测量结果的对比发现,两者吻合度较好,表明3D-DIC系统能够满足岩石力学试验非接触式、可视化变形测量的需求。另外以田下凝灰岩为试验材料,通过图像分析得到了加载过程中岩石表面的全场变形信息,发现岩石变形的不均匀性和应变局部化现象,表明利用3D-DIC系统对岩石破裂过程变形测量比传统变形测量方法更具有优越性,为岩石力学试验研究提供了一种新的、便捷的测试方法。     

预钻式原位岩体剪切测量系统研制与试验分析

预钻式岩体剪切测量系统可以测量矿山原位岩体的内摩擦角和黏聚力,与传统室内试验和现场测试相比,该测试方法可以最大限度保持岩体本质状态,方便快捷获取测试数据。首先阐述了系统整体设计思路,并成功研制出预钻式原位岩体剪切仪设备,搭建了数据采集存储软件平台;其次,基于剪切仪的工作原理,通过配套完整的测量系统试验装置,制定了试验数据处理方法和试验步骤;最后为验证系统方法的工程适用性,对测量系统进行了标定试验,并在混凝土平台进行了岩体侵入试验与剪切试验。剪切试验结果与室内直剪试验相比,内摩擦角小24.77%~41.33%,黏聚力小13.11%~32.84%。针对试验结果的差异性,从仪器设备和试验方式进行了分析,指出试验误差主要来源于设备剪切加载方式的变化和岩体直剪试验方法的不同。研究结果为矿山原位岩体力学参数的准确测量提供了新的思路和方法,也为后续系统的改进设计提供借鉴参考。      

A new linguistic quantifier knowledge-guided OWA approach for mineral prospectivity mapping: a case study of the Bavanat Region,Iran

The main purpose of this study is to introduce a geographic information system (GIS)-based, multi-criteria decision analysis method for selection of favourable environments for Besshi-type volcanic-hosted massive sulphide (VHMS) deposits. The approach integrates two multi-criteria decision methods (analytical hierarchy process and ordered weighted averaging) and theory of fuzzy sets, within a GIS environment, to solve the problem of big suggested areas and missing known ore deposits in favourable environment maps for time and cost reduction. We doubled the fuzzy linguistic variables’ significance as a method to apply the arrange weights that the analytical hierarchy process (AHP)-ordered weighted averaging (OWA) hybrid procedure depends on. Another aim of this work is to assist mineral deposit exploration by modelling existing uncertainty in decision-making. Both AHP and fuzzy logic methods are knowledge-based, and they are affected by decision maker judgments. We used data-driven OWA approach in a hybrid method for solving this problem. We applied a new knowledge-guided OWA approach on data with changing linguistic variables according to the mineral system for VHMS deposits. Additionally, we used a vector-based method combination, which increased the precision of results. Results of knowledge-guided OWA showed that all of the mines and discovered deposits have been predicted with 100% accuracy in half of the size of the suggested area. To summarize, results improved the selection of possible target sites and increased the accuracy of results as well as reducing the time and cost, which will be used for field exploration. Finally, the hybrid methods with a knowledge-guided OWA approach have delivered more reliable results compared to exclusively knowledge-driven or data-driven methods. The study proved that expert knowledge and processed data (information) are critical important keys to exploration, and both of them should be applied in hybrid methods for reaching reliable results in mineral prospectivity mapping.     

Empirical and numerical analyses of support requirements for a diversion tunnel at the Boztepe dam site, eastern Turkey

This paper presents the engineering geological properties and support design of a planned diversion tunnel at the Boztepe dam site that contains units of basalt and tuffites. Empirical, theoretical and numerical approaches were used and compared in this study focusing on tunnel design safety. Rock masses at the site were characterized using three empirical methods, namely rock mass rating (RMR), rock mass quality (Q) and geological strength index (GSI). The RMR, Q and GSI ratings were determined by using field data and the mechanical properties of intact rock samples were evaluated in the laboratory. Support requirements were proposed accordingly in terms of different rock mass classification systems. The convergence–confinement method was used as the theoretical approach. Support systems were also analyzed using a commercial software based on the finite element method (FEM). The parameters calculated by empirical methods were used as input parameters for the FEM analysis. The results from the two methods were compared with each other. This comparison suggests that a more reliable and safe design could be achieved by using a combination of empirical, analytical and numerical approaches.     

Landslide displacement prediction technique using improved neuro-fuzzy system

Landslides are one of the most destructive forms of natural hazards, which cause serious threat to life and properties. Landslide monitoring and perdition of future landslide behavior is an important aspect of disaster mitigation, as it helps to issue early warnings and adopt suitable control measures in time. This paper proposes a technique, landslide displacement prediction using recently proposed extreme learning adaptive neuro-fuzzy inference system (ELANFIS) with empirical mode decomposition (EMD) technique. ELANFIS reduces the computational complexity of conventional ANFIS by incorporating the theoretical idea of extreme learning machines (ELM). The nonlinear original landslide displacement series first converted into a limited number of intrinsic mode functions (IMFs) and one residue. Then, the decomposed data are predicted using ELANFIS algorithm. Final prediction is obtained by summation of outputs of all ELANFIS submodels. The performances of the proposed technique are tested in Baishuihe and Liangshuijing landslides. The results show that ELANFIS with EMD model outperforms state of art methods in terms of prediction accuracy and generalization performance.     

Numerical and intelligent modeling of triaxial strength of anisotropic jointed rock specimens

The strength of anisotropic rock masses can be evaluated through either theoretical or experimental methods. The latter is more precise but also more expensive and time-consuming especially due to difficulties of preparing high-quality samples. Numerical methods, such as finite element method (FEM), finite difference method (FDM), distinct element method (DEM), etc. have been regarded as precise and low-cost theoretical approaches in different fields of rock engineering. On the other hand, applicability of intelligent approaches such as fuzzy systems, neural networks and decision trees in rock mechanics problems has been recognized through numerous published papers. In current study, it is aimed to theoretically evaluate the strength of anisotropic rocks with through-going discontinuity using numerical and intelligent methods. In order to do this, first, strength data of such rocks are collected from the literature. Then FlAC, a commercially well-known software for FDM analysis, is applied to simulate the situation of triaxial test on anisotropic jointed specimens. Reliability of this simulation in predicting the strength of jointed specimens has been verified by previous researches. Therefore, the few gaps of the experimental data are filled by numerical simulation to prevent unexpected learning errors. Furthermore, a sensitivity analysis is carried out based on the numerical process applied herein. Finally, two intelligent methods namely feed forward neural network and a newly developed fuzzy modeling approach are utilized to predict the strength of above-mentioned specimens. Comparison of the results with experimental data demonstrates that the intelligent models result in desirable prediction accuracy.     

The assessment of rock slope instability along the Khosh-Yeylagh Main Road (Iran) using a systems approach

The slope instability is connected to a large diversity of causative and triggering factors, ranging from inherent geological structure to the environmental conditions. Thus, assessment and prediction of slope failure hazard is a difficult and complex multi-parametric problem. In contrast to the analytic approaches, the systems approaches are able to consider infinite number of affecting parameters and assess the interactions of each couple of the parameters in the system. This paper presents a complete application of the rock engineering systems approach in prediction of the instability potential of rock slopes in 15 stations along a 20?km section of the Khosh-Yeylagh Main Road, Iran as the case study of the research. In this research, the main objective has been defining the principal causative and triggering factors responsible for the manifestation of slope instability phenomena, quantify their interactions, obtain their weighted coefficients, and calculate the slope instability index, which refers to the inherent potential instability of each slope of the examined region. The final results have been mapped to highlight the rock slopes susceptible to instability. Finally, as a preliminary validation on the utilization of systems approach in the study region, the stability of investigated rock slopes were analyzed using an empirical method and the results were compared. The comparisons showed a rather good coincidence between the given classes of two methods.     

Prediction of Compressional, Shear, and Stoneley Wave Velocities from Conventional Well Log Data Using a Committee Machine with Intelligent Systems

Measurement of compressional, shear, and Stoneley wave velocities, carried out by dipole sonic imager (DSI) logs, provides invaluable data in geophysical interpretation, geomechanical studies and hydrocarbon reservoir characterization. The presented study proposes an improved methodology for making a quantitative formulation between conventional well logs and sonic wave velocities. First, sonic wave velocities were predicted from conventional well logs using artificial neural network, fuzzy logic, and neuro-fuzzy algorithms. Subsequently, a committee machine with intelligent systems was constructed by virtue of hybrid genetic algorithm-pattern search technique while outputs of artificial neural network, fuzzy logic and neuro-fuzzy models were used as inputs of the committee machine. It is capable of improving the accuracy of final prediction through integrating the outputs of aforementioned intelligent systems. The hybrid genetic algorithm-pattern search tool, embodied in the structure of committee machine, assigns a weight factor to each individual intelligent system, indicating its involvement in overall prediction of DSI parameters. This methodology was implemented in Asmari formation, which is the major carbonate reservoir rock of Iranian oil field. A group of 1,640 data points was used to construct the intelligent model, and a group of 800 data points was employed to assess the reliability of the proposed model. The results showed that the committee machine with intelligent systems performed more effectively compared with individual intelligent systems performing alone.