Use of the punch test for estimating TBM performance

doi:10.1016/S0886-7798(98)00083-2

Tunnelling and Underground Space Technology

Volume 13, Issue 4, October–December 1998, Pages 403-408

This paper is dedicated to the memory of Arne Reinert, colleague, geologist, musician, and good friend, without whose dedicated labor this paper would not have been possible.

https://doi.org/10.1016/S0886-7798(98)00083-2 Get rights and content

Abstract

This paper discusses the history of the punch test as a laboratory tool for estimating Raise Boring Machine and Tunnel Boring Machine penetration rates. Included are discussions of some of the methods that have been used to evaluate the test results for this purpose and how the test has been used to evaluate certain rock characteristics such as brittleness, bedding or cleavage, and porosity, which have been observed to affect machine performance. Also presented are some examples as to how the test has been used in an expanded form to evaluate the effect of certain machine parameters such as cutter spacing and geometry on excavation rates. Based on this, it is concluded that the punch test has proven itself to be a powerful laboratory tool for evaluating rock boreability and that it has the potential of becoming a stand alone test for this purpose.

References (3)

H.J. Handewith
Predicting the economic success of continuous tunnelling and hard rock

There are more references available in the full text version of this article.

Cited by (72)

A novel workflow including denoising and hybrid deep learning model for shield tunneling construction parameter prediction
2024, Engineering Applications of Artificial Intelligence
Deep Learning (DL) has shown mind-blowing potential in geotechnical engineering and has been widely used in shield tunneling as an efficient soft computing method. Considering that the data acquisition system for monitoring the shield machine is severely affected by noise and the high dimension feature, this study proposes a workflow for predicting the tunnelling performance of the shield machine. The workflow mainly consists of two parts: Data pre-processing and DL-based predictor. The data processing adopts an improved Variational Modal Decomposition (VMD) algorithm for denoising the raw data. The algorithm combines the permutation entropy of the decomposed modal functions with the Flower Pollination Algorithm (FPA) to determine the optimal parameters of VMD. The denoised data is achieved through a joint model of Attention Mechanism and Hybrid Deep Learning (Att-HDL) model to realize the prediction of the penetration rate. The Att-HDL model mainly consists of two stages: compressing the features of input data and predicting the penetration rate. The work of this study provides a decision support for the adjustment of operating parameters in shield tunnelling. The proposed workflow shows satisfactory results in tunnelling, and when comparing five predictors, the Att-HDL model achieves the best performance with a coefficient of determination of 0.965.
Fast perception of rock mass strength and integrity in TBM tunnelling using in-situ penetration test
2023, Tunnelling and Underground Space Technology
Tunnel boring machine (TBM) is the preferred method in the construction of long-distance and deep-buried tunnel due to its advantages in safety and high efficiency. However, the TBM method is very sensitive to the condition of the surrounding rock. The strength and integrity characteristics of rock mass have great influence on the safe and efficient tunnelling. To provide a fast perception method for these characteristics during the TBM tunnelling process, an in-situ penetration testing system is proposed in the present study. The development and testing procedure of the system are elaborated. The system is applied to a TBM-excavated water conveyance tunnel to continuously perceive the strength and integrity characteristics of the surrounding rock. Firstly, the force-penetration (F-P) responses of the surrounding rock under penetration are qualitatively studied. Then, series of penetration indices are summarized to quantify the characteristics of the F-P responses. Finally, the quantitative relationships between the rock strength and the penetration indices are studied. The results indicate that the in-situ penetration testing system can perceive the rock mass strength and integrity characteristics qualitatively and quantitatively. On the qualitative aspect, the F-P responses can be categorized into four types (Type I to Type IV), which correspond to the level of rock mass strength and integrity. On the quantitative aspect, a single penetration index cannot represent the complete information of the F-P response. The descriptive classification of the rock mass strength (class I to III) is established through multiple penetration indices. The in-situ penetration testing system can be used as a method to quickly perceive the strength and integrity characteristics of the surrounding rock in TBM tunnelling.
Suggestion of new models for predicting performance of raise boring machines based on indentation tests
2023, Tunnelling and Underground Space Technology
Raise Boring Machines (RBMs) as the fast, safe, and efficient machines are used for drilling/excavation of vertical and/or inclined shafts in the underground structures. Proper selection and accurate performance prediction of these machines are very important issues affecting the cost estimation and planning of the projects. However, due to lack of reliable models, performance prediction of these machines has been seriously limited. This study aims at proposing new models for performance prediction of RBMs based on indentation and full-scale linear cutting tests, as well as physical–mechanical property tests, performed on rock samples. Operational and performance data of RBMs and rock samples with different geological origins are collected from different raise boring projects. Then, indentation tests are performed on different core samples by using a real-life tungsten carbide insert of the button cutters used on the analyzed RBMs. Full-scale linear cutting tests were previously performed on the block samples by using a 305 mm (12 in.) diameter button (kerf) cutter. The study indicates that velocity of S-wave and Cerchar abrasivity index can be used to estimate brittleness index of rocks, which is also obtained from the indentation tests. It also shows the feasibility of using power function as relationship between the indentation force and penetration values for performance prediction of RBMs. It is shown that the results of indentation tests can be used in both empirical and deterministic predictions of RBM performance. However, the proposed models should be improved by additional rock types, operational-performance parameters of different RBMs, indentation and cutting test results of different rocks.
Discussion on the paper entitled “Estimating elasticity modulus and uniaxial compressive strength of sandstone using indentation test” by E. Mousavi, A. Cheshomi and M. Ashtari [Journal of Petroleum Science and Engineering 169 (2018) 157–166]
2022, Journal of Petroleum Science and Engineering
A discussion on the paper by Mousavi et al. (2018) is presented. The paper is focused on the estimation of elastic modulus (E) and uniaxial compressive strength (UCS) of sandstone using indentation test. The authors concluded that the thickness effect of samples on the indentation indices (critical transition force (CTF) and indentation modulus (IM)) was negligible. They proposed a dimensionless surface parameter (S) to eliminate the size effect on the E and UCS. On the basis of the test results and analysis method presented in the paper by Mousavi et al. (2018), three aspects of indentation test need to be clarified: the methods to prevent the movement of rock specimens, the results obtained from indentation test and the effect of specimen sizes on the CTF and IM. Meanwhile, the present discussion paper proposed some references for the methods to prevent the movement of rock specimens and the methods to eliminate the size effect in indentation test.
Spatio-temporal feature fusion for real-time prediction of TBM operating parameters: A deep learning approach
2021, Automation in Construction
This research provides a spatio-temporal approach to perform real-time forecasting for the tunnel boring machine (TBM) operating parameters. By extracting the real-time TBM operational data from the data acquisition system, a Long Short-Term Memory (LSTM) based deep learning model is trained for accurate prediction. A global sensitivity analysis (GSA) by adopting the Sobol method is performed for the model to quantify the contribution of input variables. The developed methodology can be a useful tool for TBM performance improvement and it enhances the state of knowledge on underground excavation. The result from the case study indicates that: (1) The proposed spatio-temporal method provides reliable real-time forecasting with mean absolute error (MAE) and root mean squared error (RMSE) of 1.261 mm and 1.955 mm, respectively, and (2) GSA results indicate that TBM's thrust and CHD torque are the 2 most influential spatial factors, while the historical data of penetration rate is critical for accurate forecasting. Further studies could focus on backward optimization to improve TBM's performance based on the prediction.
Development and application of an in-situ indentation testing system for the prediction of tunnel boring machine performance
2021, International Journal of Rock Mechanics and Mining Sciences
Citation Excerpt :
The statistical analysis between the TBM performance and the acquired parameters was also presented. Indentation test, also known as indentation hardness test, punch test and punch penetration test,32,51–57 was originally intended to provide a direct method for estimating the normal load on disc cutters in rock engineering. It is one of the most powerful method to understand the rock fragmentation mechanism.58,59
Prediction of the tunnel boring machine (TBM) performance is critically important for project scheduling and cost estimation. Various models have been proposed to estimate the TBM performance. However, most of these models used the parameters of rock specimens acquired from the laboratory tests, which differed from the in-situ conditions. It is difficult to apply these models to the engineering practice. In the present study, to overcome these limitations, an in-situ indentation testing system for the force-penetration response was proposed and applied to a TBM-excavated tunnel. A database that integrates 33 groups of in-situ indentation testing (235 tests in total) and the corresponding TBM operating parameters was established, including 12 indentation indices extracted from the force-penetration curve and 6 operating parameters recorded by the TBM. A series of correlation analysis was carried out to investigate the relationships between these indices/parameters. The analysis indicates that the 12 indentation indices are highly or moderately correlated with each other. A similar phenomenon was found in the TBM operating parameters. Consequently, four indentation indices and one operating parameter (field penetration index, FPI) were selected to establish the predictive models. Predictive models of the TBM performance (FPI) with high correlation coefficient (r = 0.960) were proposed using the multiple indices obtained from the in-situ indentation testing.

View all citing articles on Scopus

^☆: This paper was originally published in Canadian Tunnelling 1998. It is reprinted herein with the permission of the Tunnelling Association of Canada and the authors.

¹: Present address: G.L. Dollinger, Ph.D., Geologic Consultant, Underground Construction, 22 - 16th Ave. S.E., Bellevue, WA, U.S.A.

²: Present address: H.J Handewith, Tunnel Consultant, Seattle, WA, U.S.A.

³: Present address: C. D. Breeds, Ph.D., President, SubTerra, Inc., Kirkland, WA U.S.A.

View full text

Tunnel research and developmentUse of the punch test for estimating TBM performance☆

Abstract

Predicting the economic success of continuous tunnelling and hard rock

Tunnel research and development
Use of the punch test for estimating TBM performance☆