Prediction of blast-induced flyrock in Indian limestone minesusing neural networks

Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA.
2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.     

矿井水文地质类型划分研究

结合某矿井的水文地质条件,分析了该矿井含水层、隔水层、充水条件、水力联系等情况,研究了其水文地质特征及矿井防治水工作,并根据相关规定,对矿井水文地质类型进行划分,指出该矿井水文地质类型属于中等类型,但亦应加强探放水以及水文地质观测工作,保证矿井生产的安全进行。     

A new look at the response surface approach for reliability analysis

Closed-form mechanical models to predict the behaviour of complex structural systems often are unavailable. Although reliability analysis of such systems can be carried out by Monte Carlo simulations, the large number of structural analyses required results in prohibitively high computational costs. By using polynomial approximations of actual limit states in the reliability analysis, the number of analyses required can be minimized. Such approximations are referred to as Response Surfaces. This paper briefly describes the response surface methodology and critically evaluates existing approaches for choosing the experimental points at which the structural analyses must be performed. Methods are investigated to incorporate information on probability distributions of random variables in selecting the experimental points and to ensure that the response surface fits the actual limit state in the region of maximum likelihood. A criterion for reduction in the number of experiments after the first iteration is suggested. Two numerical examples show the application of the approach.     

A new framework for evaluation of rock fragmentation in open pit mines

The main purpose of blasting in open pit mines is to produce the feed for crushing stage with the optimum dimensions from in situ rocks. The size distribution of muck pile indicates the efficiency of blasting pattern to reach the required optimum sizes. Nevertheless, there is no mature model to predict fragmentation distribution to date that can be used in various open pit mines. Therefore, a new framework to evaluate and predict fragmentation distribution is presented based on the image analysis approach. For this purpose, the data collected from Jajarm bauxite mine in Iran were used as the sources in this study. The image analysis process was performed by Split-Desktop software to find out fragmentation distribution, uniformity index and average size of the fragmented rocks. Then, two different approaches including the multivariate regression method and the decision-making trial and evaluation laboratory (DEMATEL) technique were incorporated to develop new models of the uniformity index and the average size to improve the Rosin-Rammler function. The performances of the proposed models were evaluated in four blasting operation sites. The results obtained indicate that the regression model possesses a better performance in prediction of the uniformity index and the average size and subsequently the fragmentation distribution in comparison with DEMATEL and conventional Rosin-Rammler models.     

A new classification system for evaluating rock penetrability

This paper presents a new classification system called the Rock Penetrability index (RPi). An evaluation model based on the fuzzy Delphi analytic hierarchy process (FDAHP) has been used for estimation of penetrability and drillability of rocks. For this purpose, five parameters of the rock material, including uniaxial compressive strength, Schimazek's F-abrasivity, mean Moh's hardness, texture and grain size and Young's modulus have been investigated and rated. In the RPi system, a number from 10.25 to 100 can be assigned to each rock, with higher values corresponding to greater ease of drilling and penetration into rock. Based on the RPi classification, rocks are classified into five modes from the view point of penetrability: very poor, poor, medium, good and very good.     

Fuzzy approach for preliminary design of weak rock slopes in lignite mines

Rock slope failure modes are either driven by structurally controlled mechanisms (planar, wedge, or toppling failure) or lean upon the highly jointed or weak rock mass (circular failure). Developing practical tools for preliminary slope design is a popular topic among geotechnical society. This study proposes a practical methodology to predict a safe overall slope angle for weak rock slopes of lignite mines. Rock mass rating (RMR) and slope mass rating (SMR) classification systems can be remarkably misleading for weak rocks. Fuzzy modification was proven to improve rock and slope quality predictions. In addition, structurally controlled failures can be estimated more precisely. Later, a popular slope performance chart of Bieniawski that is based on RMR was modified by the fuzzy approach, and the computer models of a weak rock mass failure in a Turkish lignite mine are presented. The modified methodology was proven to be better suited to the weak rock conditions.     

A new approach to rock slope stability – a probability classification (SSPC)

The newly developed system presented in this paper is based on a three-step approach and on the probabilistic assessment of independently different failure mechanisms in a slope. First, the scheme classifies rock mass parameters in one or more exposures and allowance is made for weathering and excavation disturbance. This gives values for the parameters of importance to the mechanical behaviour of a slope in an imaginary, unweathered and undisturbed 'reference' rock mass. The third step is the assessment of the stability of the existing slope or any new slope in the reference rock mass, taking into account both method of excavation and future weathering. From the large quantity of data obtained in the field, the Slope Stability Probability Classification (SSPC) system has been proposed, based on the probabilities of different failure mechanisms occurring. Developed during 4 years of research in Falset, Tarragona province, Spain, it has been used with good results in Austria, South Africa, New Zealand and the Dutch Antilles.     

A new practical method for prediction of geomechanical failure-time

Predicting the failure-time of geo-hazards is an important rock mechanics problem. We first evaluated the validity of the INVerse-velocity (INV) method to predict failure-time of rock mass and landslides. This method utilizes rates of displacement or strain to predict the actual failure-time (T_f), so the value of total displacement or strain before “failure” is not crucial. Second, we developed a new method for computing failure-time predictions based on the SLOpe (gradient) to predict T_f, termed the SLO method. Finally, a simple conceptualised model representing “safe” and “unsafe” predictions was proposed.To validate these hypotheses, prediction of rock mass failure in the Asamushi and Vaiont landslides (in situ studies) was conducted. Furthermore, laboratory conditions were incorporated into the research, which include predictions using circumferential strain and axial strain from uniaxial compression creep test on Shikotsu welded tuff (SWT), and predictions of failure-time for Inada granite under Brazilian creep tests. It was found that the SLO method is better than the INV method; SLO gave safe predictions in all the cases. In contrast, INV tends to give unsafe predictions (predicted failure-time T_fp>T_f). Our findings reveal that predictions using circumferential strain are better than those made using axial strain for SWT, and notably, given failure with very short tertiary creep, the methods tend to show limited reliability. However, the SLO method could find extensive application in predicting failure-time of geo-hazards, for instance, roof wall failure in mines, etc.     

Ground-level ozone prediction by support vector machine approach with a cost-sensitive classification scheme

For ground-level ozone (O(3)) prediction, a predictive model, with reliable performance not only on non-polluted days but, more importantly, on polluted days, is favored by public authorities to issue alerts, so that concerned citizens and industrial organizations could take precautions to avoid exposure and reduce harmful emissions. However, the class imbalance problem, i.e., in some collected field data, number of O(3) polluted days are much smaller than that of non-polluted days, will deteriorate the model performance on minority class-O(3) polluted days. Despite support vector machine (SVM) obtaining promising results in air quality prediction, in this study, a cost-sensitive classification scheme is proposed for the standard support vector classification model (S-SVC) in order to investigate whether the class imbalance plagues S-SVC. The S-SVC with such scheme is named as CS-SVC. Experiments on imbalanced data sets collected from two air quality monitoring sites in Hong Kong show that 1) S-SVC is still sensitive to class imbalance problem; 2) compared with S-SVC, CS-SVC effectively avoids class imbalance problem with lower percentage of false negative on O(3) polluted days but with higher percentage of false positive on non-polluted days; 3) compared with both S-SVC and CS-SVC, support vector regression model (SVR), after converting its output to binary one, only has similar performance with S-SVC, which indicates class imbalance problem also impairs the regressor model. From point of protecting public health, CS-SVC, which less likely misses to forecast O(3) polluted days, is recommended here.     

A new management approach for the remediation of polluted surface water outfalls to improve river water quality

Comprehensive strategies are required by water companies to effectively manage the remediation of polluted surface water outfalls (PSWOs). This paper describes the approach adopted by Thames Water since 1997. The elimination of diffuse pollution sources by identifying and rectifying domestic and commercial foul to surface water drainage misconnections has resulted in sustainable improvements to the local environment across the Thames region.     

A new classification system for the joints used in lattice shells

A new classification system for the joints in lattice shells is proposed. The stiffness and moment capacity of the joints together with the overall structural behavior of the lattice shells are considered in order to establish the classification system. According to this new system, joints in lattice shells can be classified into unique categories: rigid, semi-rigid or pinned. The rigid joints have both high bending stiffness and moment capacity; the semi-rigid joints have both moderate bending stiffness and moment capacity, and the pinned joints have either low bending stiffness or low moment capacity. Determination coefficients α and β are defined, based on the stiffness and moment capacity of the joints, and these are used to establish clear boundaries between the different categories. Some numerical examples are included to demonstrate the validity of the classification system. With the help of the classification system, an efficient process for practical engineering design is proposed, which can help designers choose the appropriate analysis method for lattice shells with different joints.     

A new classification system for the joints used in lattice shells

A new classification system for the joints in lattice shells is proposed. The stiffness and moment capacity of the joints together with the overall structural behavior of the lattice shells are considered in order to establish the classification system. According to this new system, joints in lattice shells can be classified into unique categories: rigid, semi-rigid or pinned. The rigid joints have both high bending stiffness and moment capacity; the semi-rigid joints have both moderate bending stiffness and moment capacity, and the pinned joints have either low bending stiffness or low moment capacity. Determination coefficients α and β are defined, based on the stiffness and moment capacity of the joints, and these are used to establish clear boundaries between the different categories. Some numerical examples are included to demonstrate the validity of the classification system. With the help of the classification system, an efficient process for practical engineering design is proposed, which can help designers choose the appropriate analysis method for lattice shells with different joints.     

An operational approach to ground control in deep mines

As mines go deeper and get larger,mine designs become more fragile largely due to the response of the rock mass to mining.Ground control and rock support become important levers in the mine construction schedule,production performance,and excavation health.For example,in cave mines,the production footprint together with associated mine infrastructure are significant investments in a modern caving operation.This investment must be protected and maintained to reduce the risk of ground-related production disruptions.It is necessary to preserve the health of these excavations and their maintenance through an effective rock support design.Rock support thus becomes a strategic element in asset management.This article focuses on support design for brittle ground when displacements induced by stress-fracturing consume much of the support’s capacity.It deals with the functionality of the support in deforming ground.Several interlinked concepts are important when assessing excavation health.Designs must not only account for load equilibrium but also for deformation compatibility and capacity consumption.Most importantly,the support’s displacement capacity is being consumed when the rock mass is deformed after support installation.Hence,it is necessary to design for the support capacity remaining at the time when the support is needed.If support capacity can be consumed,it can also be restored by means of preventive support maintenance(PSM).This concept for cost-effective ground control is introduced and illustrated on operational evidence.Furthermore,how design can impact construction costs and schedule are discussed.Support is installed to provide a safe environment and preserve an operationally functional excavation.It also must assure senior management that investments in high quality support and its maintenance will substantially reduce delays and with it,costs.It is demonstrated that the use of‘gabion-like’support systems can achieve these goals.A technical summary of the‘gabion panel’support system design is presented.     

A geomatics-based road surface temperature prediction model

A GIS-based model for the prediction of road surface temperature is presented that has the ability to explain up to 74% of the spatial variation in road surface temperature in the West Midlands, UK. The approach combines basic spatial data sets with a synergy of surveying techniques to produce a geographical parameter database that drives the spatial component of a road weather prediction model. By measuring and modelling geographical parameters such as altitude, landuse, road construction and the sky-view factor, pre-existing components of road ice prediction systems can be united to provide a dynamic road ice prediction system.     

A new model for TBM performance prediction in blocky rock conditions

The term “blocky rock conditions” is generally associated with face instabilities in blocky/jointed rock masses. These events are generally promoted by unfavorable rock mass structural conditions, in terms of joint frequency and orientation, and acting stresses. As a result, rock blocks are formed and then detach from the excavation face which becomes “blocky”, with a markedly irregular and uneven profile. This condition may have a paramount effect on TBM tunneling, leading to a high maintenance frequency and a low TBM advancement rate. Based on the TBM performance data recorded during excavation of tunnels in blocky rock conditions, a TBM performance prediction model has been developed. The model is based on the Field Penetration Index for blocky rock conditions, FPI_blocky, which was previously introduced to analyze the TBM performance in blocky grounds at the Lötschberg Base Tunnel. Through a multivariate regression analysis, a new expression has been introduced to predict the FPI_blocky based on the volumetric joint count (J_v) and the intact rock uniaxial compressive strength (UCS). An attempt has also been made to quantify the downtimes that may occur in blocky rock conditions and to estimate a reliable value of TBM daily advance.