Noise And Vibration Control For Surface Mines

Noise control (acoustic), Noise (environmental), Vibration, Quarrying, Coal mining, Mining, Blasting, Legislation, Earth-moving equipment, Mining equipment

This report summarizes the Bureau of Mines noise-control research program from 1972 to 1982. Each segment of the mining industry--under- ground coal, underground hardrock, surface mining, and processing plants--has different noise-control problems because of vast differences in working procedures, equipment, and workplace design. The Bureau has identified the most serious noise problems in each segment and has developed strategies for attacking these problems. This publication points out the need for noise control in the mining industry, discusses Federal regulations governing worker exposure to noise, and describes the Bureau's overall approach to mining noise- control research. It traces the history of noise overexposure in each segment of the mining industry and discusses the major noise sources. It provides detailed information on noise-control research efforts in the Bureau's major areas of emphasis, including the results of these efforts. Finally, the report discusses the Bureau's future role in research on mining noise control, emphasizing the need to expend more effort on long term in-house investigations into the noise problems that have been identified in past programs as the most serious ones.

Noise control (acoustic), Noise (environmental), Vibration, Vibration control, Extraction (minerals), Quarrying, Mineral extraction equipment, Blasting, Vibration measurement

Physico-mechanical rock properties are significant in all operational mining activities. This book evaluates rock properties by using empirical equations and soft computing techniques. It predicts various physico-mechanical properties such as uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density, P-wave velocity (Vp), tensile strength (TS), Young's modulus (E), and percentage porosity (n) using multiple regression and artificial neural network (MLP and RBF) techniques, taking drill bit speed, penetration rate, drill bit diameter, and equivalent sound level produced during drilling as input parameters.