Prediction of the performance of impact hammer by adaptive neuro-fuzzy inference system modelling

K. Kucuk, C.O. O. Aksoy, Hakan Basarir, T. Onargan, M. Genis, V. Ozacar

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Impact type excavators are widely used for excavations, performed in weak-laminated-foliated-anisotropic rocks. Therefore the prediction of the performance of impact hammer is very important in many mining and civil engineering projects.This paper describes the construction of adaptive neuro-fuzzy inference system model for predicting the performance of impact hammer type excavator by considering rock and excavating machine properties such as block punch strength index, geological strength index system and impact hammer power. Extensive field and laboratory studies were conducted in the tunnel construction route of the second stage of Izmir Metro Project, which excavated in laminated-foliated flysch rocks. The results of the constructed adaptive neuro-fuzzy inference system and traditional multiple regression models were compared. Although the prediction performance of traditional multiple regression model is high, it is seen that adaptive neuro-fuzzy inference model exhibits better prediction performance according to statistical performance indicators. By means of the developed model, the performance of impact type excavators can be predicted in terms of net excavation based on the selected rock and machine properties. © 2010 Elsevier Ltd.
Original languageEnglish
Pages (from-to)38-45
Number of pages8
JournalTunnelling and Underground Space Technology
Volume26
Issue number1
DOIs
Publication statusPublished - 2011
Externally publishedYes

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