New Approach for Identification of Suitable Vibration Attenuation Relationship for Underground Blasts

For construction and mining activities, excavation in hard or weathered rock or even hard soil, is often performed with the help of blasting. As a safety practice the blasting operation has to be designed such that the existing structures in the vicinity are not adversely affected due to the blasting activity. Blast vibration attenuation relationship for the ground media becomes necessary for the design of blasting. In India, the relevant IS code specifies an attenuation relationship (Power expression) between the peak particle velocity, the charge weight and the distance of the monitoring point from the blast. However, the empirical coefficients are provided in IS code for only two categories - hard rock and weathered rock / soil. Hence they result in uneconomical and sometimes unviable blasting design. The alternate option is to establish site specific attenuation relationship for the location and use the same for design of blasting operation. Industrial practice is to find the empirical constants for the same Power expression (as IS code) from the site trial blast data. For this purpose, the same dataset is used for parameter estimation as well as evaluation of the suitability of the estimated parameters. In this study it is demonstrated that evaluation of performance with a different dataset alters the conclusions. Further, expressions other than the commonly adopted Power expression might be more suitable for the relationship. In this article, two other expressions, namely, Reciprocal expression and Weibull model were identified which could be equally good. Exponents for scaled distance calculation, other than the popularly adopted 0.5, were found to be applicable in the case study. Trail blast data from a site was used to demonstrate the same. It was concluded that while developing site specific attenuation relationship, various expressions with different exponents may be examined for their suitability and this should be done with a fresh dataset. The best expression identified for the site should be finally adopted for further activities

VIBRACIJE TLA USLIJED MINIRANJA U BRANAMA I HIDROELEKTRANAMA

In this study, the safe charge per delay (kg) of explosives and Peak Particle Velocity (PPV, mm/s) are recorded for 140 blast events at various distances which relates to the dam and hydropower projects of Karoun III, Masjed – Soleiman, and Siah – Bisheh in Iran. Parameters of Scaled Distance (SD) are estimated carefully. For the prediction of PPV, empirical equations are used. The correlation coefficients resulting from these predictors in diverse sites, are different because of varying conditions in the geomechanical and blasting parameters at each site. Therefore, considering several initial blasts and analysing their results, a suitable relationship has been selected for each case study.U ovoj studiji razmatrano je sigurno punjenje eksploziva (kg) s odgodom i vršna brzina čestica (VBZ, mm/s) na 140 miniranja na različitim udaljenostima u sklopu projekata brane i hidroelektrane Karoun III, Masjed – Soleiman i Siah – Bisheh u Iranu. Parametri skalirane udaljenosti pažljivo su procijenjeni. Za predviđanje VBZ-a korištene su empirijske jednadžbe. Koeficijenti korelacije koji su proizišli iz tih procjena različiti su za različite lokacije zbog različitih geomehaničkih uvjeta i parametara miniranja na svakoj lokaciji. Stoga je, uzevši u obzir nekoliko početnih miniranja i analizirajući njihove rezultate, odabrana odgovarajuća veza za svaki pojedini slučaj studije

Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP) : a case study from an iron ore mine

Mine blasting can have a destructive effect on the environment. Among these effects, air overpressure (AOp) is a major concern. Therefore, a careful assessment of the AOp intensity should be conducted before any blasting operation in order to minimize the associated environmental detriment. Several empirical models have been established to predict and control AOp. However, the current empirical methods have many limitations, including low accuracy, poor generalizability, consideration only of linear relationships among influencing parameters, and investigation of only a few influencing parameters. Thus, the current research presents a hybrid model which combines an extreme gradient boosting algorithm (XGB) with grey wolf optimization (GWO) for accurately predicting AOp. Furthermore, an empirical model and gene expression programming (GEP) were used to assess the validity of the hybrid model (XGB-GWO). An analysis of 66 blastings with their corresponding AOp values and influential parameters was conducted to achieve the goals of this research. The efficiency of AOp prediction methods was evaluated in terms of mean absolute error (MAE), coefficient of determination (R 2 ), and root mean square error (RMSE). Based on the calculations, the XGB-GWO model has performed as well as the empirical and GEP models. Next, the most significant parameters for predicting AOp were determined using a sensitivity analysis. Based on the analysis results, stemming length and rock quality designation (RQD) were identified as two variables with the greatest influence. This study showed that the proposed XGB-GWO method was robust and applicable for predicting AOp driven by blasting operations

Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation. Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9 m, spacing of 4.4 m and charge per delay of 627 Kg. © 2020, Springer Nature Switzerland AG

Evaluation of Blasting Efficiency in Surface Mines

Drilling and blasting are the major unit operations in opencast mining. In spite of the best efforts to introduce mechanization in the opencast mines, blasting continue to dominate the production. Beside the production in open cast mining blasting and vibration also cause environmental problem. In bench blast design, not only the technical and economic aspects, such as block size, uniformity and cost, but also the elimination of environmental problems resulting from ground vibration, air blast and fly rock should be taken into consideration. The evaluation of ground vibration components plays an important role in the minimization of the environmental complaints. Odisha is rich in iron ore deposit and the mines invariably need blasting for loosen the rock mass. These are frequent complaints from the people surrounding the zone about adverse effect of blasting. This study is an attempt to evaluate same of those aspects. Two active iron ore mine have been considered for the analysis of ground vibration, air over pressure, flyrock as well as fragmentation parameters. There exist a few established approaches as USBM, Langefors-Kilstrom, Ambraseys-Hendron, Indian standard and CMRI to predict those. In this investigation the utility of those approaches are evaluated. It was observed that the two region Koira and Daitarido not confirm strongly to the five approaches. Artificial neural network is a technique that is gain wide acceptance even in heterogeneous condition. This study also finds that the prediction by ground vibration, air over pressure and fly rock by ANN would be better alternative. Model equation has also been developed with ANN approach. Mutual relations between stemming length, depth, fragmentation size, powder factor, explosive charge have also been determined

Prediction of Blast-Induced Ground Vibrations: A Comparison Between Empirical and Artificial-Neural-Network Approaches

Ground vibrations are a critical factor in the rock blasting process. The instantaneous load application exerted by the gas pressure during the detonation process acts on the blasthole walls creating dynamic stresses in the adjacent rock. This triggers different sorts of stress waves, mainly divided into two categories: body and surface waves. The first comprises the P and the S waves, while the second comprises Rayleigh waves. These waves spread concentrically starting at the blast location and move along the ground surface and its interior, being attenuated as they reach further distances. In most cases, and accepting the hypothesis that the attenuation of the vibrational waves is proportional to the distance and inverse to the energy released during the blast, the vibration from a large blast can be perceived from far away. In any case, the ground vibrations can affect pit slopes’ stability, and they can also damage man-made structures. Therefore, ground vibrations need to be predicted, monitored, and controlled to minimize the vibration-caused disturbance to nearby or far elements. The assessment of vibrations produced by blasting has traditionally relied on maximum charge weight per delay scaling laws. These two-parameter or three-parameter models depend on a curve fit to measured data. In this approach (scaled laws), the ground vibration waveforms are not used in the vibration level estimation, neither are other blast design parameters, such as burden, spacing, hole diameter, explosive density, uniaxial compressive strength of the rock, Young’s modulus, subdrilling, stemming, and charge length, to name a few. To provide a more comprehensive approach to ground vibration modeling, including the aforementioned variables, artificial neural networks (ANN) have been employed in several studies worldwide with promising results. The present thesis uses ANN applied to ground vibration modeling, considering the blasting parameters in the input, unlike the empirical approaches, using data from an open-pit gold mine in La Libertad region, Peru. The results from this study are then compared against the traditional scaled distance approach. Two datasets were used, the first was comprised of 178 shots and the second, 80 shots. The first dataset was collected at the La Arena community, and the second was collected at the La Ramada community. Both of these communities are the most populated in the direct area of influence of the mine. When comparing the measured and predicted PPV values using the scale-distance method in the La Arena community, the coefficient of determination () found was 0.1166, while the found when comparing the measured and predicted PPV values using the optimum trained artificial network was 0.5915. Following the same comparison, the value found in the La Ramada community was 0.1035 using the scaled distance method, and the found using the optimum trained artificial network was 0.5139

Support Vector Machines for the Estimation of Specific Charge in Tunnel Blasting

Mine tunnels, short transportation tunnels, and hydro-power plan underground spaces excavations are carried out based on Drilling and Blasting (D&B) method. Determination of specific charge in tunnel D&B, according to the involved parameters, is very significant to present an appropriate D&B design. Suitable explosive charge selection and distribution lead to reduced undesirable effects of D&B such as inappropriate pull rate, over-break, under-break, unauthorized ground vibration, air blast, and fly rock. So far, different models are presented to estimate specific charge in tunnel blasting. In this study, 332 data sets, including geomechanical characteristics, D&B, and specific charge are gathered from 33 tunnels. The data are related to three dams and hydropower plans in Iran (Gotvand, Masjed-Solayman, and Siah-Bishe). Specific charge is modeled in inclined hole cut drilling pattern. In this regard, Support Vector Machine (SVM) algorithm based on polynomial Kernel function is used as a tool for modeling. Rock Quality Designation (RQD) index, Uniaxial Compressive Strength (UCS), tunnel cross-section area, maximum depth of blast hole, and blast hole coupling ratio are considered as independent input variables and the specific charge is considered as a dependent output variable. The modeling results confirm the acceptable performance of SVM in specific charge estimation with minimum error

Prediction of fly-rock using gene expression programming and teaching– learning-based optimization algorithm

Peer ReviewedObjectius de Desenvolupament Sostenible::12 - Producció i Consum ResponsablesObjectius de Desenvolupament Sostenible::12 - Producció i Consum Responsables::12.2 - Per a 2030, assolir la gestió sostenible i l’ús eficient dels recursos naturalsPostprint (published version

A Neural Network Approach for the Prediction of Ground Vibrations Induced Due to Blasting

This project presents the application of neural networks as well as statistical techniques for prediction of ground vibration by major influencing parameters of blast design. The predictions by artificial neural network (ANN) is compared with the predictions of conventional statistical relation. Ground vibrations and frequency induced due to blasting were monitored at Indian Detonators Limited Rourkela (IDL), Balphimali Bauxite mine (UAIL) and Dunguri Limestone mine (ACC). The neural network was trained by the data sets recorded at the various mine sites. From the analysis it was observed that the correlation coefficient determined for PPV and frequency by ANN was higher than the correlation coefficient of statistical analysis. The correlation coefficient determined for PPV and frequency by ANN for Balphimali Bauxite mine (UAIL) was 0.9563 and 0.9721 respectively and correlation coefficient determined for PPV and frequency by ANN for IDL was 0.9053 and 0.9136 while correlation coefficient determined for PPV and frequency by ANN for Dunguri Limestone mine (ACC) was 0.9322 and 0.9301. The difference in correlation coefficient of PPV and frequency in different mines is due to different number of input parameters for the neural network and number of datasets used for the training of network. The number of datasets and input parameters were more for Balphimali Bauxite mine (UAIL), thus it showed higher correlation coefficient between the recorded and predicted data by ANN than other mines