Prediction of blast-induced ground vibration using artificial neural network (original) (raw)
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A Neural Network Approach for the Prediction of Ground Vibrations Induced Due to Blasting
2016
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 coeff...
Evaluation of blast-induced ground vibration predictors
Soil Dynamics and Earthquake Engineering, 2007
The present paper mainly deals with the prediction of blast-induced ground vibration level at a Magnesite Mine in tecto-dynamically vulnerable hilly terrain in Himalayan region in India. The ground vibration was monitored to calculate the safe charge of explosive to avoid the continuous complaints from the nearby villagers. The safe charge of explosive and peak particle velocity (PPV) were recorded for 75 blast events (150 blast data sets) at various distances. These data sets were used and analysed by the widely used vibration predictors. From the four predictors, vibration levels were calculated and compared with new monitored 20 blast data sets. Again, the same data sets were used to validate and test the three-layer feed-forward back-propagation neural network to predict the PPV values. The same 20 data sets were used to compare the results by the artificial neural network (ANN). Among all the predictors, a very poor correlation was found, whereas ANN provides very near prediction with high degree of correlation.
Application of soft computing to predict blast-induced ground vibration
Engineering with Computers, 2011
In this study, an attempt has been made to evaluate and predict the blast-induced ground vibration by incorporating explosive charge per delay and distance from the blast face to the monitoring point using artificial neural network (ANN) technique. A three-layer feed-forward back-propagation neural network with 2-5-1 architecture was trained and tested using 130 experimental and monitored blast records from the surface coal mines of Singareni Collieries Company Limited, Kothagudem, Andhra Pradesh, India. Twenty new blast data sets were used for the validation and comparison of the peak particle velocity (PPV) by ANN and conventional vibration predictors. Results were compared based on coefficient of determination and mean absolute error between monitored and predicted values of PPV.
Development of a blast-induced vibration prediction model using an artificial neural network
Journal of the Southern African Institute of Mining and Metallurgy, 2019
In an opencast coal mine explosives are used for fragmentation of coal and overburden. If the explosive energy is not fully utilized it causes blast-induced ground vibration, which may damage nearby structures. Ground vibration is expressed as peak particle velocity (PPV). During different stages of mine planning and operation, it is necessary to use a ground vibration prediction model for blasthole design. Selection of the modelling technique is crucial. Mathematical and statistical modelling techniques have limited application because of the lack of explicit knowledge about the complex mine blasting system. Vogiatzi (2002) highlighted the problem of multicollinearity in case of statistical modeling techniques. Mutalib et al. (2013) stated that mathematical models are unable to capture the nonlinear relationship between several blasting-related parameters due to the complexity of the model input data. However, the difficulty involved in modelling complex blast vibration problems can be removed by adopting an alternative soft computing modelling approach. One of the soft computing techniques is the artificial neural network (ANN). Ragam and Nimaje (2018) developed an ANN model for predicting PPV using six input variables. Kosti et al. (2013) stated that the conventional predictors fail to provide acceptable prediction accuracy. They showed that a neural network model with four mine blast parameters as input could make significantly more accurate on-site predictions. Sayadi et al., (2013), using a database from Teheran Cement Company limestone mines, found that a neural network resulted in maximum accuracy and minimum error. Khandelwal and Singh (2009) developed an ANN model using 150 data records from an Indian coal mine with site-specific rock characteristics and geomining setting. Khandewal and Singh (2007) built a ground vibration prediction model for a magnesite mine using four prediction variables with 20 data records. Kamali and Ataei (2010) predicted PPV in the structure of the Karoun III power plant and dam using an ANN. El Hafiz et al. (2010) evaluated ground vibration predictors using data from a single-station seismograph at a limestone quarry in Egypt. ANN prediction models have been built for one Indian coal mine and one limestone mine. Using the findings of these initial studies, it is essential to enhance the application of ANN in various mines in different Indian coal mining
Prediction of Blast Induced Ground Vibration and Frequency Using an Artificial Intelligent Technique
Noise & Vibration Worldwide, 2004
When an explosive detonates, the sudden change generates waves in the surrounding media resulting in ground vibrations. As the vibration passes surface structures, it induces vibration in those structures. The vibrations generated in blasting are transmitted through the ground as seismic body waves and surface waves. These shock waves can cause severe damage to the nearby structures or surrounding rock mass. There are different methods to determine the peak particle velocity and the corrsponding frequency such as by USBM predictor methods and multivariate regression analysis etc. These methods are cumbersome and time consuming. In the present investigation, Artificial Neural Network (ANN) technique was used for the prediction of peak particle velocity (ppv) and the concerned frequency. Two different neural networks were designed for the prediction of ppv and frequency. For both the networks ten input neurons, one hidden layer with five neurons and one output neuron was used. The num...
Journal of sound and vibration, 2006
This paper presents the application of neural network for the prediction of ground vibration and frequency by all possible influencing parameters of rock mass, explosive characteristics and blast design. To investigate the appropriateness of this approach, the predictions by ANN is also compared with conventional statistical relation. Network is trained by 150 dataset with 458 epochs and tested it by 20 dataset. The correlation coefficient determined by ANN is 0.9994 and 0.9868 for peak particle velocity (PPV) and frequency while correlation coefficient by statistical analysis is 0.4971 and 0.0356.
Application of an Expert System for the Assessment of Blast Vibration
Geotechnical and Geological Engineering, 2012
The purpose of this article is to evaluate and predict the blast induced ground vibration using different conventional vibration predictors and artificial neural network (ANN) at a surface coal mine of India. Ground Vibration is a seismic wave that spread out from the blast hole when detonated in a confined manner. 128 blast vibrations were recorded and monitored in and around the surface coal mine at different strategic and vulnerable locations. Among these, 103 blast vibrations data sets were used for the training of the ANN network as well as to determine site constants of various conventional vibration predictors, whereas rest 25 blast vibration data sets were used for the validation and comparison by ANN and empirical formulas. Two types of ANN model based on two parameters (maximum charge per delay and distance between blast face to monitoring point) and multiple parameters (burden, spacing, charge length, maximum charge per delay and distance between blast face to monitoring point) were used in the present study to predict the peak particle velocity. Finally, it is found that the ANN model based on multiple input parameters have better prediction capability over two input parameters ANN model and conventional vibration predictors.
Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration
Applied Sciences
Rock blasting is one of the most common and cost-effective excavation techniques. However, rock blasting has various negative environmental effects, such as air overpressure, fly rock, and ground vibration. Ground vibration is the most hazardous of these inevitable impacts since it has a negative impact not only on the environment of the surrounding area but also on the human population and the rock itself. The PPV is the most critical base parameter practice for understanding, evaluating, and predicting ground vibration in terms of vibration velocity. This study aims to predict the blast-induced ground vibration of the Mikurahana quarry, using Bayesian neural network (BNN) and four machine learning techniques, namely, gradient boosting, k-neighbors, decision tree, and random forest. The proposed models were developed using eight input parameters, one output, and one hundred blasting datasets. The assessment of the suitability of one model in comparison to the others was conducted b...
Archives of Mining Sciences, 2023
In this paper, an attempt was made to find out two empirical relationships incorporating linear multivariate regression (LMR) and gene expression programming (GEP) for predicting the blast-induced ground vibration (BIGV) at the Sarcheshmeh copper mine in south of Iran. For this purpose, five types of effective parameters in the blasting operation including the distance from the blasting block, the burden, the spacing, the specific charge, and the charge per delay were considered as the input data while the output parameter was the BIGV. The correlation coefficient and root mean squared error for the LMR were 0.70 and 3.18 respectively, while the values for the GEP were 0.91 and 2.67 respectively. Also, for evaluating the validation of these two methods, a feed-forward artificial neural network (ANN) with a 5-20-1 structure has been used for predicting the BIGV. Comparisons of these parameters revealed that both methods successfully suggested two empirical relationships for predicting the BIGV in the case study. However, the GEP was found to be more reliable and more reasonable.
MMEX, 2020
The present study compares three different techniques (decision tree, artificial neural network and multivariate regression analysis) for predicting blast-induced ground vibrations in some Indian tunnelling projects. The performance of these models was also compared to site-specific conventional predictor equations. A database consisting of 137 vibration records was randomly divided into training and testing sets for model generation. Eight input parameters (total charge, tunnel cross-section, maximum charge per delay, number of holes, hole diameter, distance from blasting face, hole depth and charge per hole) were selected for model development using bivariate correlation analysis. Results indicated that the decision tree is best suited for predicting vibrations. The decision tree further suggested that the intensity of near-field ground vibrations is mainly affected by total charge fired in a round, whereas the intensity of far-field vibrations is governed by maximum charge per delay and charge per hole. Conventional ground vibration predictors and machine learning techniques such as neural networks do not depict the relationship between input and output parameters. However, the present study substantiates that the decision tree can be a good tool for precise prediction of ground vibrations. Further, the decision tree can classify and relate different blast design parameters for refining blast designs to control ground vibrations on sites.