Development and application of an in-situ indentation testing system for the prediction of tunnel boring machine performance. (November 2021)

• Record Type: Journal Article
• Authors: Zhang, Xiao-Ping; Xie, Wei-Qiang; Liu, Quan-Sheng; Yang, Xin-Mei; Tang, Shao-Hui; Wu, Jian
• Is Part Of: International journal of rock mechanics and mining sciences. Volume 147(2021)
• Publication Date: 2021-11
• Subjects: TBM performance -- In-situ indentation testing -- Indentation indices -- TBM operating Parameters -- Field penetration index (FPI); Rock mechanics -- Periodicals; Soil mechanics -- Periodicals; Mining engineering -- Periodicals; Roches, Mécanique des -- Périodiques; Sols, Mécanique des -- Périodiques; Technique minière -- Périodiques; 624.151305
• DOI: 10.1016/j.ijrmms.2021.104899
• Languages: English
• ISSNs: 1365-1609

Abstract

Abstract: 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.