DBPapers

TUNNEL DRIVAGE PERFORMANCE PREDICTION OF HYDRAULIC IMPACT HAMMERS USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)

AUTHOR/S: M. IPHAR
Sunday 1 August 2010 by Libadmin2010

10th International Multidisciplinary Scientific GeoConference - SGEM2010, www.sgem.org, SGEM2010 Conference Proceedings/ ISBN 10: 954-91818-1-2, June 20-26, 2010, Vol. 1, 611-618 pp

ABSTRACT

Hydraulic impact hammers are one of the mechanical excavators that can be
economically used in tunneling projects under favorable geologic conditions. However,
there is relatively less published material in the literature directed to their performance
prediction in terms of rock properties. In tunnel drivage projects, there is often the need
for accurate means of performance prediction of related mechanical excavators. A poor
prediction of machine performance can lead to very costly contractual claims. In this
study, the application of a relatively new soft computing method for data analysis called
adaptive neuro-fuzzy inference system (ANFIS) to predict net breaking rate of an
impact hammer is demonstrated. The prediction capabilities offered by ANFIS were
shown by using field data of a metro tunnel drivage project which appear in the
published literature. For this purpose, an ANFIS-based prediction model was
constructed and the obtained results were then compared to those of regression-based
prediction, in terms of various statistical performance indexes. The results suggest that
the proposed ANFIS-based prediction method outperforms the classical regressionbased
prediction method, and thus can be used to produce a more accurate and reliable
estimate of impact hammer performance from Schmidt hammer rebound values and
rock quality designation values obtained from the field.

Keywords: Hydraulic impact hammer, ANFIS, Rock excavation, Schmidt hammer, Rock quality designation