Evaluation of the efficiency of wall-shaping blasts in tunnel driving

Abstract

The growing use of self-propelled equipment in mines has made it necessary to enlarge tunnel cross-sections substantially (by a factor of 1.5-2). Combined with the movement towards mining at ever greater depths and in more complex geomechanical situations, these developments make particularly important the proper wall shaping of mines.Although some positive experience has been gained in the USSR and other countries with this method under various mining conditions, the extent of wall-shaping blasts, especially in nonferrous metal mines, remains limited to 10-15%. The main obstacle to a wider practical application of this method (in addition to secondary factors such as the design of specialized tunneling machines, devices for exact blast-hole marking, etc.) is the lack of adequate cost-benefit analyses and evaluations of all the factors that determine the efficiency of this method of mine-wall shaping.

Intelligent Quality Evaluation System for Vertical Shaft Blasting and its Application

“…The single-factor fuzzy evaluation is to the degree of affiliation of the evaluation object to each element of the alternative set Vj (j=1,2,3,4,5) from a single factor Ui(i=1, 2,3,4,5) in the factor set U. Let the degree of affiliation of the judging object to the j-th element Vj in the alternative set be rij when judging by the i-th factor ui in the factor set, then the result of judging by the i-th factor ui can be expressed as a fuzzy set:…”

“…And through sensitivity analysis, it was found that the uniaxial compressive strength of the rock, the inclination of the working face and joint have the greatest influence on blasting, which in turn influences the transportation cost. Shapiro V Y [3] studied a method for evaluating the effect of roadway shaping in drifting blsting based on the multi-criteria optimization principle, by calculating and analyzing the relevant parameters form a theoretical point of view. D. Jahed Armaghani et al [4] improved the traditional empirical method to predict blasting fly-rock in mine stopes and used an Artificial Neural Network (ANN) and Adaptive Neural Fuzzy Inference System (ANFIS) to predict and evaluate blasting fly-rock V. N. Tyupin and Rubashkin [5] determined the stress of the rock by blast energy and obtained the theoretical equation for calculating stress based on the size of the fracture zone and fissured zone, the physical and mechanical properties of the rock and the explosive characteristics of the explosive.…”