TECHNICAL | MECHANISED TUNNELLING
HARD ROCK TBM PERFORMANCE PREDICTION WITH REGRESSION MODELS
TBM performance in hard rock was investigated considering machine specifications and rock type (geological and geotechnical factors, including cementation and grain size), leading to development of a new model via regression analysis. Paper by Alireza Salimi 1,2, Jamal Rostami 3
, Christian Moormann 1
1 INSTITUTE OF GEOTECHNICAL ENGINEERING, UNIVERSITY OF STUTTGART, GERMANY 2 FELDHAUS BERGBAU GMBH & CO, MUNICH, GERMANY
3 DEPARTMENT OF MINING ENGINEERING, EARTH MECHANIC INSTITUTE, COLORADO SCHOOL OF MINES, US 4 SCHOOL OF GEOLOGY, COLLEGE OF SCIENCE, UNIVERSITY OF TEHRAN, TEHRAN, IRAN
Prediction of machine performance is a fundamental step for planning, cost estimation/control and selection of the type of tunnel boring machine (TBM) to use on a project. Penetration rate (PR) and machine utilisation (U) are the two principal measures of performance for given ground condition but accurate estimation of machine performance could still be a challenge, particularly in complex geological conditions, with different types of rock responding differently to cutting forces. Therefore, incorporating the effects of rock type in performance prediction models can improve estimates. This study developed models for predicting the
penetration rate of hard rock TBMs in different types of rock based on field penetration index (FPI), using multivariable regression analysis and machine learning (ML) algorithm, including classification and regression tree (CART). The proposed models offer estimated FPIs in different rock types, rock strength, and rock mass properties in the form of graphs (diagrams), which can be used to estimate TBM penetration rate. The models also exhibit sensitivity to rock mass parameters. The models, developed after analysis of a
comprehensive database of TBM performance in various rock types, can offer more accurate estimates of machine performance by incorporating many of the key parameters available in typical geotechnical reports and contract documents.
INTRODUCTION TBMs have been the predominant choice of tunnelling methods in various grounds, especially in hard rock applications with lengths of more than 1.5km–2km, due to achievement of higher excavation speed, lower cost,
16 | July 2024
improved safety, and their environmental friendliness compared to traditional drill and blast method. Estimating TBM performance is a key parameter for tunnel design, and also selection of the appropriate machine type and specification. In last two decades, many performance prediction
models have been offered by researchers to estimate penetration rate of hard rock machines in new projects, and can be categorised into two main groups: theoretical and empirical methods (Khademi Hamidi et al. 2010). Theoretical models analyse cutting forces acting
on a disc cutter to estimate the rate of penetration (ROP), based on force equilibrium equations. Laboratory cutting tests provide a basic understanding of rock fragmentation, and the force-penetration behaviour of rock is the basis for this class of performance prediction models. Their main disadvantage is not completely representing site parameters relative to rock mass conditions, in particular joints, as disc cutters would encounter in the field. Empirical models are primarily based on observation
of field performance. In cases where standard laboratory rock cutting facilities are not available, TBM performance may be predicted using formulas developed empirically. Currently, three different models are most recognised in TBM performance prediction and prognosis: Colorado School of Mines (CSM) (Rostami 1997); Norwegian University of Science and Technology (NTNU) (Bruland 1998); and, FPI (Nelson et al. 1983, Hassanpour et al. (2011, 2016). The CSM model represents a semi-theoretical approach to TBM performance, allowing calculation
, Jafar Hassanpour 4
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