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By E. Anelli

Based on experimental continuous cooling transformation (CCT) diagrams, an artificial neural network has been trained to predict the amount of microsturctural constituents and hardness resulting from and controlled cooling at rates from 150 to 0.015 K/s. It can be applied to quenching and normalizing processes of low-alloy steels with 0.05-0.30 C% and 0.4-2.0% Mn -with possible microalloying additions. Examples of the good predictive capabilities about 15 Vickers standard deviation for hardness and a few percent for volume fraction of phases) are reported' Keywords: Austenite decomposition, low. And medium-carbon steels, artificial neural networks

Jan 1, 2003

By Emad Farouz, Ehab S. Shatnawi

"The increasing demand for small deformations and high-capacity drilled shafts without enlarging drilledshaft diameters has motivated the development and increasing use of a base grouting (BG) technique. This technique has been demonstrated to improve drilled-shaft capacity, especially for shafts tipped into loose cohesionless soils. Nevertheless, there remains considerable uncertainty in predicting the performance of base-grouted shafts, particularly for shafts tipped into cohesive soils. To date, there are only two documented design methods for estimating the axial geotechnical resistance of base-grouted shafts: Tip Capacity Multiplier (TCM) and Axial Capacity Multiplier (ACM) methods. A comprehensive test program was carried out during the design phase of the Wisconsin Zoo Interchange project to help optimize the flyover ramp’s foundation design. The test program included installing and testing one base grouted shaft and one ungrouted shaft at two sites with different ground conditions. Based on test results, it was concluded that prediction of the TCM method is well correlated with the measured values for both cohesive and granular soils; however, both methods tend to underestimate the axial resistance. Results also highlighted the importance of the base-grouting criteria in taking full advantage of the base grouting.INTRODUCTIONBase-grouted drilled shafts (BGDS), also known as post-grouted drilled shafts, consist of conventionally installed drilled shafts that contain access grout tubes placed within the shaft during construction. After installing the drilled shaft and achieving the pre-established concrete strength, grout is injected at the shaft tip through the access grout tubes to improve soil behavior around the drilled-shaft base. The tip grout injection improves the overall shaft capacity and reduces settlement through Densifying the in situ soils, compressing any debris left by the drilling process, preloading the soil beneath the tip to mobilize the shaft base resistance within service limits, increasing the tip area, and increasing the side resistance in the lower portion of the shaft."

Jan 1, 2016

By T. G. Sitharam, P. B. Choudhury

Prediction of ground vibration (peak particle velocity, PPV) and frequency is an important task in geoscience. Till date, many empirical equations are derived from conducting trial blasts in the mines, to arrive at attenuation predictors. These predictors are usually site dependant and hold good for one particular type of rock/strata. These predictors cannot be used for geologically different rocks/strata. This paper presents the application of support vector machine (SVM) model, for predicting blast induced ground vibration and frequency, using a one comprehensive model for three different types of rock. Inputs to the SVM model formulation involve twenty one parameters consisting of geotechnical properties of the rock mass, blast design geometry and explosive properties, and the output of the formulated model is peak particle velocity and its associated frequency. The data has been acquired from eight limestone mine, five coal mine and two manganese mines. The comprehensive SVM model is formulated using 400 datasets and tested with 143 datasets. The performance indicators to the model not only justify its formulation but also the advantages it has over site specific predictor models. The SVM model not only predicts PPV, with higher correlation coefficients as compared to predictor equations, but also the associated frequency.

Jan 1, 2010

By M. Kamali

In this research, in order to predict the peak particle velocity (PPV)(as vibration indicator) caused by blasting projects in the excavations of the Karoun III power plant and dam, three techniques including statistical, empirical, and neural network were used and their results were interpreted and compared. First, multivariate regression analysis (MVRA) was used as statistical approach. Next, PPV was predicted using some widely used empirical models. Lastly, an artificial neural network was used. In the ANN model, maximum charge per delay, total charge per round, distance from blast site, direction of firing, blasthole length, number of blastholes, total delay in milliseconds, number of delay intervals, and average specific charge were taken into consideration as input parameters and consequently the PPV as output parameter. The results of the techniques were interpreted from two points of view. Firstly, the correlation between the observed data and predicted ones, secondly the total error between observed data and predicted ones. The MVRA had a satisfactory correlation but its error of estimation was comparatively very high. The empirical model had reliable correlation and a small error of estimation; in total the results of empirical method were more reliable than those of MVRA. Generally, the ANN approach showed very high correlation and a very small error. The results of this research indicated that the ANN model is the best predicting model for PPV in comparison with other approaches.

Jan 1, 2010

By M.P. ROY, ASHISH K. VISHWAKARMA, A.K. MISHRA, P.K. SINGH, VIVEK K. HIMANSHU

Principal component analysis (PCA) is one of the dimension reduction techniques widely used to classify data based on features. Ground vibration is a major hazard arising from rock blasting operations, and accurate prediction of the vibration is necessary for designing controlled blasting parameters. Most of the widely accepted predictors of ground vibration consider the maximum explosive charge weight per delay (MCPD) and distance as the parameters responsible for ground vibration, but mining sites with bigger production targets have varying geometrical parameters to suit the excavator utility. Accordingly, other blast design parameters will also have impacts on ground-vibration intensity. A blast vibration predictor considering all the blast design parameters would be complicated, and some of the parameters will have overlapping contributions to ground-vibration intensity. Data classification using PCA is a feasible solution, and regression analysis may be performed to develop predictors using the classified input data. In this study, PCA was used along with multivariate logarithmic regression to predict ground vibration, and the performance of this technique to predict ground vibration was compared with the performances of various existing empirical predictors. The evaluation suggests that the predictor with logarithmic regression followed by PCA performs better than the existing empirical predictors.

Nov 1, 2022

By M. P. Roy, P. K. Singh, A. K. Mishra, Ashish K. Vishwakarma, Vivek K. Himanshu

Ground vibration is one of the major hazards produced by rock-blasting operation. The accurate prediction of vibration is necessary for designing controlled blasting parameters. The existing vibration predictors consider maximum explosive charge weight per delay and distance as the parameters responsible for ground vibration. These predictors are based on the assumption that the geometrical parameters of the blast will be constant for a site. However, the mining sites with bigger production targets have varying geometrical parameters to suit the excavator utility. Accordingly, the other blast design parameters will also have an impact on ground vibration intensity. A principal component analysis is a dimension reduction technique. This technique along with multivariate logarithmic regression has been used in this paper to predict the ground vibration. The technique has classified the blast design parameters into four principal components. The regression with the scores from these principal components has been carried out. The evaluation of the model performance of predictors along with the existing empirical predictors has been carried out using R2 and RMSE values. The evaluation suggests that the predictor with logarithmic regression followed by principal component analysis gives better performance with respect to the existing empirical predictors.

Jul 30, 2022

By Kerry Burns

Contrary to active subsidence, which occurs concurrently with mining operations, or is completed within a few days following coal extraction, delayed subsidence may take many years to appear at the surface after coal mines are abandoned. There are two principal morphological types of delayed subsidence: troughs, which are shallow depressions, and sinks, which are steep-sided crown pits. Both types are damaging to surface structures, and a variety of methods have been introduced to deal with the problem, ranging from subsidence insurance to site restitution. In planning insurance or restitution measures, a predictive model is of value in estimating the magnitude of the problem and the size of long-term budgetary commitments. Only one model is known, which was developed for the Bureau of Mines by GAI Consultants of Monroeville (l,2,3). The GAI model is presented in qualitative terms. This report develops a formal basis for the model and tests a numerical implementation on one of the best-described study areas, Allleqheny County in Pennsylvania.

Jan 1, 1982

By Rose CW

Typical rehabilitation programmes for spoil piles and waste dumps in open cut coal mines involve the spreading of topsoil to assist vegetation growth. In the early years of rehabilitation before a protective vegetation cover has been established, this topsoil is susceptible to erosion during rainfall events. Since the long term success of rehabil- itation procedures is sensitive to adequate topsoil maintenance and since, on many mine sites, topsoil is a scarce resource, erosion losses of topsoil (particularly once fert- ilised) can severely restrict rehabilitation Measures.

Jan 1, 1983

By C. Mick Lownds, Dale S. Preece, Ali Bhuiyan

FDM (Fracture Density Model) is a three-dimensional mechanistic model of blast induced rock fragmentation. FDM uses several computational mechanics algorithms to simulate the effect of blasting in different rock types. Earlier, FDM was successfully exercised to simulate 29 small scale shots from the United States Bureau of Mines (USBM), and 6 full-scale bench blasts at the Bararp dimensional stone quarry in Sweden. Excellent agreement was obtained for those shots, and the results were documented in earlier publications. One of the drawbacks of previous ver- sions of FDM was its inability to predict the fines, specifically in Bararp quarry simulations. Ac- curate predictions of fines can be very useful in reducing energy consumptions during down- stream comminution processes and improve the mine-mill operation efficiencies. In this paper, we propose a newer algorithm that can predict the fines as well as the coarse fragments in Bararp with reasonable accuracy. The model was also applied to an open-pit Copper mine with heavily jointed rocks, and it produced very good fragmentation predictions for the full range of fragment sizes. Comparisons with the sieved fragmentation data (for both Bararp stone quarry and the open- pit Copper mines) show great potential for this updated version of FDM in modeling the entire range of fragment size distributions in mining and quarrying applications.

Feb 6, 2023

By Charles S. Robinson

INTRODUCTION Geology, the science of the earth, is the fundamental science in the construction of a tunnel. The approximate site of a tunnel is determined by its purpose. The geology of the tunnel site will determine the cost--or feasibility of a tunnel--the specific alignment to accomplish the purpose of the tunnel, the design of the tunnel (within the limits of the purpose of the tunnel), and the methods of construction. The more accurately the geology of a proposed tunnel can be depicted, the more accurately can the cost of a tunnel be estimated. The factors that should be defined for tunnel design and construction are the physical characteristics of the material in which the tunnel is to be constructed. The physical characteristics of the material are determined by the genesis of the material, and subsequent geologic processes that have affected the material. The geologic materials in which tunnels are constructed can be broadly classified as unconsolidated and consolidated. The unconsolidated materials include chiefly deposits of recent geologic time that occur near the surface. Because these deposits occur near the surface, and tunnels constructed in them will be at shallow depth, methods of physical exploration such as geophysical methods, trenching, and drilling will give more data at relatively low cost--therefore the physical exploration is generally, or should be, quite extensive--and the

Jan 1, 1997

By R. K. Sinha, B. S. Choudhary, Desh Deepak, A. K. Mishra, Hemant Agrawal, Shankar Kumar

There are several methods (empirical, statistical, and machine learning tools) to calculate blast-induced ground vibration. Still, due to complications in the blasting procedure, under varying blasting loads and strata conditions, it is not easy to predict and simulate blast vibration precisely. Therefore, a study was conducted to simulate the field blast data in Ansys software using explicit dynamics. For this study, monitoring of ground vibration for 20 single hole blasts was carried out. The density of emulsion explosives was in the range of 0.9 to 1.15 g/cc. In the laboratory, rock properties were determined from the rock sample collected from the mine site before blasting. The in-hole velocity of detonation (VoD) and the density of the emulsion explosive were also determined. These parameters were used for numerical analysis to know the ground vibrations at specified points. The results of ground vibration predicted by the model are validated using field data.

By R. K. Sinha, B. S. Choudhary, Desh Deepak, A. K. Mishra, Hemant Agrawal, Shankar Kumar

There are several methods (empirical, statistical, and machine learning tools) to calculate blast-induced ground vibration. Still, due to complications in the blasting procedure, under varying blasting loads and strata conditions, it is not easy to predict and simulate blast vibration precisely. Therefore, a study was conducted to simulate the field blast data in Ansys software using explicit dynamics. For this study, monitoring of ground vibration for 20 single hole blasts was carried out. The density of emulsion explosives was in the range of 0.9 to 1.15 g/cc. In the laboratory, rock properties were determined from the rock sample collected from the mine site before blasting. The in-hole velocity of detonation (VoD) and the density of the emulsion explosive were also determined. These parameters were used for numerical analysis to know the ground vibrations at specified points. The results of ground vibration predicted by the model are validated using field data.

By R. K. Sinha, B. S. Choudhary, Desh Deepak, A. K. Mishra, Hemant Agrawal, Shankar Kumar

Despite several methods (empirical, statistical and machine-learning tools) to calculate blast-induced ground vibration, it is not easy to predict and simulate blast vibration precisely under varying blasting loads and strata conditions due to complications in the blasting procedure. In this study, field blast data were simulated in Ansys software using explicit dynamics, and ground vibrations for 20 single-hole blasts were monitored. The densities of emulsion explosives were in the range of 0.9 to 1.15 g/cm3. In the laboratory, rock properties were determined from rock samples collected from the mine site before blasting. The in-hole velocity of detonation (VoD) and density of the emulsion explosive were also determined and used in numerical analysis to know the ground vibrations at specific points. The results of ground vibrations predicted by the model were validated using field data.

In engineering applications of explosives for rock breaking, the released energy is expended mainly into heat, rock crushing and fracturing, seismic waves, air blast, and noise. Those parts of energy which pass into seismic waves and air wave create environmental problems since they are transmitted away from the blast site. In order to predict the environmental effects accurately, one must be able to predict the energy distribution at the source. The energy distribution at the source is significantly controlled by the blast constriction. Consequently, the accuracy of prediction and control of ground vibrations generated by blasting is affected by various matrices of blasting constriction.

Jan 1, 1986

By R. Nyirenda, B. Besa, N. Dzimunya

The accurate estimation of peak particle velocity (PPV) is crucial during the design of bench blasting operations in open pit mines, since the vibrations caused by blasting can significantly affect the integrity of nearby buildings and other structures. Conventional models used to predict blast-induced vibrations are not capable of capturing nonlinear relationships between the different blasting-related parameters. Soft computing techniques, i.e., techniques that are founded on the principles of artificial intelligence, effectively model these complexities. In this paper, we use the random forest (RF) algorithm to develop a model to predict blast-induced ground vibrations from bench blasting using 48 data records. The model was trained and tested using WEKA datamining software. To build this model, a feature selection process using several combinations of Attribute Evaluators and Search Methods under the WEKA Select Attributes tab was performed. The correlation coefficient of the actual data and RF model-predicted data was 0.95, and the weighted average of the relative absolute error (RAE) was 10.9%. The RF model performance was also compared to the equivalent-path-based (EPB) equation on the testing data-set, and it was seen that the RF model can effectively be used to predict PPV. The study also demonstrates that the EPB equation is a suitable empirical method for predicting PPV.

Mar 30, 2023

By Pavel A. Torres, Darwin Torres, Sandro Huaman

Given the limited efficiency of blast-induced ground vibrations prediction empirical models, due to the complex geological system from a Peruvian mine. An artificial neural network was built in order to get better results. More accurate and precise predictions allowed the generation of blasting domains for future designs and decision making.

Feb 1, 2020

By S. A. Suhler

The detection of hazardous geological anomalies through remote sensing techniques is the subject of current research efforts sponsored by the U.S. Bureau of Mines. In this review, hazardous geological anomalies are discussed with respect to the "view" presented along with potential operational concepts for each technique. The design and development of a borehole radar is described with emphasis on the potential range and resolution capability of the system under realistic underground conditions. Results of field tests at two coal mines and at a hard rock future subway location are presented.

Jan 1, 1978

By Lawrence W. Teufel

An anelastic strain recovery technique is presented as a method of determining the directions and ratio of the maximum and minimum horizontal in-situ stresses from oriented cores from deep wells. Predictions of hydraulic fracture azimuth inferred from principal stress directions determined from both elastic and time-dependent (anelastic) strain relaxation measurements of volcanic tuff are consistent with the observed orientations of hydraulic fractures produced in simulation and mineback experiments, underground at the Nevada Test Site. In addition, anelastic strain recovery measurements on oriented cores of shale and sandstone from well depths of about 1680 meters show consistent relaxation directions of the principal horizontal strains and have enabled a prediction of the principal horizontal stress directions and hydraulic fracture azimuth.

Jan 1, 1982

By Neil Styler

This paper presents an analysis of measurements of inter-burden deformations above six longwall faces. An attempt is made to demonstrate some correlation between the movements at the various sites, and to examine their importance with respect to predicting caving height, disruption of overlying seams, and disruption of aquifers. This analysis demonstrates some significant differences between predicted surface subsidence, and inter-burden deformation. In addition it is shown that the caving height above a longwall face is equal to 8 to 12 times the extraction height, with a zone of fractured rock extending to approximately 50 times the extraction height above the seam.

Jan 1, 1984

By Neil Styler

This paper presents an analysis of measurements of inter-burden de- formations above six longwall faces. An attempt is made to demonstrate some correlation between the movements at the various sites, and to examine their importance with respect to predicting caving height, disruption of overlying seams, and disruption of aquifers. This analysis demonstrates some significant differences between predicted surface subsidence, and inter-burden deformation. In addition it is shown that the caving height above a longwall face is equal to 8 to 12 times the extraction height, with a zone of fractured rock ex- tending to approximately 50 times the extraction height above the seam.

Jan 1, 1984