Investigations on Impact of Blasting in Tunnels

2012 ◽  
pp. 238-248
Author(s):  
Kaushik Dey ◽  
V. M. S. R. Murthy
Keyword(s):  
Statistical Model ◽  
Vibration Monitoring ◽  
Rock Damage ◽  
Multivariate Statistical ◽  
Engineering Controls ◽  
Blast Design ◽  
Metal Mines ◽  
Multivariate Statistical Model ◽  
Smooth Blasting ◽  
First Time

Blasting with longer advance per round leaves an impact both visible (in the form of overbreak) and invisible (cracks) in the surrounding rockmass, however, a number of controlled-blasting techniques, that is line drilling, pre-splitting, and smooth blasting, have been developed to minimise this problem. These techniques require additional drilling, controlled charging, and detonation, and thus, are not preferred in regular development activities. Investigations have been carried out in five different horizontal development drivages of metal mines to assess the blasting impact using burn cut and arrive at the blast-induced rock damage (BIRD) model. Vibration monitoring close to the blast was carried out using accelerometers for the first time in India to develop vibration predictors and overbreak threshold levels for individual sites. This paper reports the development of the overbreak predictive model (BIRD) for burn cut blasting in hard rock drivages by combining the relevant rock, blast design, and explosive parameters. A multivariate statistical model has been developed and validated and the same can find ready application in tunnels and mines for exercising suitable engineering controls both in blast design and explosive selection for reduced basting impacts.

Investigations on Impact of Blasting in Tunnels

2010 ◽  
Vol 1 (2) ◽  
pp. 59-71 ◽  
Author(s):  
Kaushik Dey ◽  
V. M. S. R. Murthy
Keyword(s):  
Hard Rock ◽  
Vibration Monitoring ◽  
Multivariate Statistical ◽  
Engineering Controls ◽  
Blast Design ◽  
Metal Mines ◽  
Multivariate Statistical Model ◽  
Selection For ◽  
Smooth Blasting ◽  
First Time

Blasting with longer advance per round leaves an impact both visible (in the form of overbreak) and invisible (cracks) in the surrounding rockmass, however, a number of controlled-blasting techniques, that is line drilling, pre-splitting, and smooth blasting, have been developed to minimise this problem. These techniques require additional drilling, controlled charging, and detonation, and thus, are not preferred in regular development activities. Investigations have been carried out in five different horizontal development drivages of metal mines to assess the blasting impact using burn cut and arrive at the blast-induced rock damage (BIRD) model. Vibration monitoring close to the blast was carried out using accelerometers for the first time in India to develop vibration predictors and overbreak threshold levels for individual sites. This paper reports the development of the overbreak predictive model (BIRD) for burn cut blasting in hard rock drivages by combining the relevant rock, blast design, and explosive parameters. A multivariate statistical model has been developed and validated and the same can find ready application in tunnels and mines for exercising suitable engineering controls both in blast design and explosive selection for reduced basting impacts.

Multivariate Statistical Model for Predicting Occurrence and Location of Broken Rails

2003 ◽  
Vol 1825 (1) ◽  
pp. 48-55 ◽  
Author(s):  
C. Tyler Dick ◽  
Christopher P. L. Barkan ◽  
Edward R. Chapman ◽  
Mark P. Stehly
Keyword(s):  
Statistical Model ◽  
Service Failure ◽  
Quantitative Prediction ◽  
Strongly Correlated ◽  
Santa Fe ◽  
Multivariate Statistical ◽  
Hazardous Materials Transportation ◽  
Service Failures ◽  
Multivariate Statistical Model ◽  
Improve Service Quality

Broken rails are the leading cause of major accidents on U.S. railroads and frequently cause delays. A multivariate statistical model was developed to improve the prediction of broken-rail incidences (i.e., service failures). Improving the prediction of conditions that cause broken rails can assist railroads in allocating inspection, detection, and preventive resources more efficiently, to enhance safety, reduce the risk of hazardous materials transportation, improve service quality, and maximize rail assets. The service failure prediction model (SFPM) uses a combination of engineering and traffic data commonly recorded by major railroads. A Burlington Northern Santa Fe Railway database was developed in which the locations of approximately 1,800 service failures over 2 years were recorded. The data on each location were supplemented with information on other engineering and traffic volume parameters. A complementary database with the same parameters was developed for a randomly selected set of locations at which service failures had not occurred. The combined databases were analyzed using multivariate statistical methods to identify the variables and their combinations most strongly correlated with service failures. SFPM accuracy in predicting service failures at specific locations exceeded 85%. Although further validation is necessary, SFPM is promising in the quantitative prediction of broken rails, thereby improving a railroad’s ability to manage its assets and risks.

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