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Artificial neural network as a tool for backbreak prediction

- Monjezi, Masoud, Hashemi Rizi, S, Majd, Vahdi, Khandelwal, Manoj

  • Authors: Monjezi, Masoud , Hashemi Rizi, S , Majd, Vahdi , Khandelwal, Manoj
  • Date: 2014
  • Type: Text , Journal article
  • Relation: Geotechnical and Geological Engineering Vol. 32, no. 1 (2014), p. 21-30
  • Full Text: false
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  • Description: Backbreak is one of the destructive side effects of the blasting operation. Reducing of this event is very important for economic of a mining project. Involvement of various parameters has made the backbreak analyzing difficult. Currently there is no any specific method to predict or control the phenomenon considering all the effective parameters. In this paper, artificial neural network (ANN) as a powerful tool for solving such complicated problems is used to predict backbreak in blasting operation of the Sangan iron mine, Iran. Network training was fulfilled using a collected database of the practiced operation including blast design details and rock condition. Trying various types of the networks, a network with two hidden layers was found to be optimum. Performance of the ANN model was compared with statistical analysis using datasets which were kept apart from the original database. According to the obtained results, for the ANN model there existed a higher correlation (R2 = 0.868) and lesser error (RMSE = 0.495) between the predicted and measured backbreak as compared to the regression model. Also, sensitivity analysis revealed that the inputs rock factor and number of rows are the most and the least sensitive parameters on the output backbreak, respectively. © 2013 Springer Science+Business Media Dordrecht.

Application of an expert system for the assessment of blast vibration

- Khandelwal, Manoj

  • Authors: Khandelwal, Manoj
  • Date: 2012
  • Type: Text , Journal article
  • Relation: Geotechnical and Geological Engineering Vol. 30, no. 1 (2012), p. 205-217
  • Full Text: false
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  • Description: 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. © 2011 Springer Science+Business Media B.V.

Behaviour of brittle material in multiple loading rates under uniaxial compression

- Khandelwal, Manoj, Ranjith, Pathegama

  • Authors: Khandelwal, Manoj , Ranjith, Pathegama
  • Date: 2013
  • Type: Text , Journal article
  • Relation: Geotechnical and Geological Engineering Vol. 31, no. 4 (2013), p. 1305-1315
  • Full Text: false
  • Reviewed:
  • Description: It is of great importance to investigate the effect of loading rate on the behaviour of brittle material such as concrete and rock because engineering structures are subjected to multiple loading conditions. Although material behaviour under single loading mode has been extensively studied, very limited research has been conducted to investigate the performance of brittle materials subjected to varying loading conditions. This paper presents an experimental study of the effects of single and multiple strain rates (ε) on cement mortar samples. The first set of samples was loaded at constant strain rates until failure. For the remaining samples, the first strain rate (0.005 mm/s) was applied to the sample up to a predetermined load, and then the second strain was initiated immediately by using the specially-designed gear system in place in the compression rig. As expected, the increase in strain rate showed an increase in peak strength of the sample with reduced ultimate strain. For multiple strain modes, it was observed that the highest peak strength occurred when the second strain was applied at 50 % of the peak strength of the first strain. © 2013 Springer Science+Business Media Dordrecht.
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