Assessment of rate of penetration of a tunnel boring machine in the longest railway tunnel of Turkey

Candan Gokceoglu

doi:10.1007/s42452-021-04903-y

Abstract

One of the most important issues in tunnels to be constructed with tunnel boring machines (TBMs) is to predict the excavation time. Excavation time directly affects tunnel costs and feasibility. For this reason, studies on the prediction of TBM performance have always been interesting for tunnel engineers. Therefore, the purpose of the study is to develop models to predict the rate of penetration (ROP) of TBMs. In accordance with the purpose of the study, a new database including 5334 cases is obtained from the longest railway tunnel of Turkey. Each case includes uniaxial compressive strength, Cerchar Abrasivity Index, α angle, weathering degree and water conditions as input or independent variables. Two multiple regression models and two ANN models are developed in the study. The performances of the ANN models are considerably better than those of the multiple regression equations. Before deep tunnel construction in a metamorphic rock medium, the ANN models developed in the study are reliable and can be used. In contrast, the performances of the multiple regression equations are promising, but they predict lower ROP values than the measured ROP values. Consequently, the prediction models for ROP are open to development depending on the new data and new prediction algorithms.

Highlights

Four decades ago, Robbins [45] stated “nothing has been more difficult than evaluating the rock mass characteristics and applying the evaluations to a formula predicting penetration rate”
One of the essential tasks in the excavation of tunnels with tunnel boring machines (TBMs) is the reliable estimation of the performance needed for planning, cost control and other decisionmaking regarding the feasibility of tunnelling projects [2]
According to results of the extensive review on the literature performed by Samaei et al [49], there is no comprehensive agreement on the quantitative or qualitative influence of various variables on the TBM performance assessment, but the degree of accuracy in its prediction has been improved in recent years through using various algorithms such as artificial neural networks (ANNs), support vector machine, fuzzy and neuro-fuzzy

Summary

Introduction

Four decades ago, Robbins [45] stated “nothing has been more difficult than evaluating the rock mass characteristics and applying the evaluations to a formula predicting penetration rate”. The prediction of rock mass characteristics and the rate of penetration of TBMs is still a challenging problem for tunnel engineers. The growth in the economy has led to enhanced engineering studies that result in a significant reduction in transportation time and aid in developing comfortable transportation choices [37]. In recent decades, mechanized tunnelling techniques, tunnel boring machines (TBMs), have been extensively applied to tunnel construction due to their high excavation rate and low total cost for the excavation of long tunnels [39]. TBM tunnelling has serious advantages for long tunnels if the geological and geotechnical characterizations of tunnel routes are described correctly and a suitable machine for ground conditions is selected

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Conclusion