Predicting performance of impact hammers from rock quality designation and compressive strength properties in various rock masses


Tumaç D. , HOJJATI S.

TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY, vol.59, pp.38-47, 2016 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 59
  • Publication Date: 2016
  • Doi Number: 10.1016/j.tust.2016.06.008
  • Title of Journal : TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
  • Page Numbers: pp.38-47

Abstract

Predicting the performance of the impact hammers is one of the major subjects in determining the economics of the underground excavation projects in which they are utilized. Therefore, researchers have been attracted to developing performance prediction models for these machines. Physical and mechanical properties of rocks have been used to estimate the performance of impact hammers over the last few decades. In this study, the instantaneous breaking rate (IBR, m(3)/h) of an impact hammer used in construction of Levent-Hisarustu metro tunnel (Istanbul) is recorded in detail. Sixty rock samples are obtained from tunnel route during the excavation of which the machine is employed. Physical and mechanical property tests are performed on the obtained samples. A data set including uniaxial compressive strength (UCS), rock quality designation index (RQD), Brazilian tensile strength (BTS), density (rho), Schmidt hammer hardness (SHH), Shore scleroscope hardness (SSH), Cerchar abrasivity index (CAI), and IBR is formed. Regression analysis techniques are applied to the created data set in order to develop a performance prediction model. The investigation results in a model that can predict IBR based on UCS, RQD, and the output power of the impact hammer. The proposed model passes both F-test and t-test at 0.95 confidence level. The soundness of the model is successfully tested against two formerly developed models. Covering a wide range of application and requiring only two of the most common and versatile rock properties as input parameters are the other advantages of the suggested model. (C) 2016 Elsevier Ltd. All rights reserved.