Development of a Universal Blast-Induced Ground Vibration Prediction Model for Jharia Coalfields

Development of a Universal Blast-Induced Ground Vibration Prediction Model for Jharia Coalfields

Dayamoy Garai Arvind Kumar Mishra Shankar Kumar Hemant Agrawal

Central mine planning and design institute limited, Ranchi 834008, India

Indian Institute of Technology (ISM), Dhanbad, 826004, Jharkhand, India

Deputy Manager, Coal India Limited, India

Corresponding Author Email: 
hemant.ism@gmail.com
Page: 
46-55
|
DOI: 
https://doi.org/10.18280/mmc_c.790205
Received: 
13 May 2018
| |
Accepted: 
20 June 2018
| | Citation

OPEN ACCESS

Abstract: 

Mineral commodities are the backbone of every nation as they contribute to the Gross domestic product (GDP), industries (as raw materials), and foreign exchange. Coal production in India is 724.71 MT (Million Tonnes) in year 2015. The production target has been increased by the government of India to 1.5 billion tonnes by 2022. Opencast mining in 2015 contributed to 639.234 MT (88%) of total coal production in India. This increased demand of coal shall compel the mine owner to operate under greater stress to increase the production rate. So, there will be situations arising that the mine owners may have to extract coal near human habitats. Eight of the major mines were selected and necessary care was taken that all the geological areas occurring in the Jharia coalfield region was covered. Various blast design parameters such burden, hole depth, spacing, charge per delay(CPD), and scaled distance were selected for the study of ground vibration. Data mining methods such as random forest and artificial neural networks(ANN) has been used for the prediction of peak particle velocity(PPV). The model formed by the two methods was compared and validated by selecting another two mines. The results obtained by random forest was superior to ANN. Also, a line at 95% confidence interval for the predicted PPV values is drawn to ensure greater safety. With 95% confidence and universal blast induced ground vibration prediction model for jharia coalfield has been developed. This model will help the blasting engineers operating in jharia coalfields in precise prediction and better control over blast induced ground vibration. The concept of this study can be used for generation of blast induced ground vibration prediction models for different coalfields also.

Keywords: 

ground vibration, random forest, artificial neural network, coal mines, scaled distance

1. Introduction
2. Experimental Sites
3. Working Steps of Random Forest
4. Analysis of Results
5. Conclusion
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