Analytical design method for a truss-bolt system for reinforcement of fractured coal mine roofs --illustrated with a case study

摘要

This paper presents an analytical design method for the truss-bolt system in reinforcing underground fractured rock roofs in coal mines. The analytical design method is based on the mechanical analysis of the fractured rock roof with reinforcement by inclined roof bolts and a horizontal tie-rod. The mechanical analysis for the system includes a non-linear bending model for the laterally inclined roof bolts and three upper and lower bounds. The lateral resistance of the inclined roof bolts in a truss-bolt-supported roadway is examined using classical theory of a non-linear beam in bending. The paper analyses the arching action by lateral behavior of the inclined roof bolts in reinforcing the fractured roof. Based on mechanical models, the design formula concerning the lateral bolt forces, tensions in the tie-rod in the truss system, as well as the reinforcement behavior have been derived. In order to ensure that the roof truss-bolt system reinforces the coal roof effectively, a lower bound of pre-tightening forces must be applied on the tie-rod for stabilizing the fractured roof by arching action. The pre-tightening forces exerted via the tie-rod also cannot be greater than its upper bound, since the excessive tightening force will cause localized failure in the rock near the bolt tail at the abutment of the fractured roof beam. The analytical formulas for both lower and upper bounds for truss pre-tightening forces are put forward in this paper. Furthermore, the paper also presents analytical equations for designing the axial forces and dimensions for bolts in this kind of system. Parametric studies and optimization analysis are also carried out on the basis of the proposed approach. A flow chart and procedures are presented to show the analytical design method for this truss-bolt system. Case studies are presented to show how the method can be used in actual coal mining tunnels. The predicted results compare well with those measured in the actual case studies.