Abstract

This presentation introduces the trend and advances in mechanical rock excavation technology. The interaction between rock and cutting tool has been studied in theoretical and numerical models. With accumulation of rock cutting data, various prediction models of cutting performance have been proposed. Especially, empirical models using index properties and observed field performance data have been widely used. Lately, big data collected from TBM tunnel sites have been used with the techniques of machine learning and artificial intelligence. In addition, some considerations using dynamic properties of rock in numerical analysis have been made. While integrating the latest technology, there have been advances in efficient rock cutting and prediction of the performance.

Introduction

The research on mechanical rock excavation has rapidly increased since 2000’s according to Scopus search. The techniques to test and predict the performance of disc cutters and pick cutters have been widely discussed for diverse geological and geomechanical conditions. The design aspects of the cutterhead and its overall performance have been also studied by many researchers. However, there are still challenging issues in mechanical excavation or cutting of rock. Downtime control in mechanical excavation is crucial for successful completion of projects. Extended downtime is mainly caused by adverse geological conditions and unexpected wear of cutting tools. Therefore, face prediction for geology/water and rapid excavation methods in difficult grounds are key issues in TBM tunneling. In addition, prediction of cutter performance and life is importance to strategically plan for intervention. In this presentation, prediction models for rock cutting performance, implementation of dynamic rock properties in numerical analysis, and artificial intelligence methods are introduced.

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