Predicting advance rate and cutter life in TBM tunnelling using evolutionary algorithm

ABSTRACT

Tunnelling with a Tunnel Boring Machine (TBM) offers several advantages over conventional tunnelling methods when covering long distances, particularly regarding time, safety, and environmental impact. Prior to starting a TBM project, performance analysis is a crucial stage necessary to inform the initial investment decision and project cost estimation. However, this analysis requires extensive fieldwork and laboratory studies. The Advance Rate (AR), calculated from the Utilization Factor (U) – the ratio of excavation time to total project time – is a widely accepted measure of TBM performance. Total project time encompasses various parameters, including boring time, cutter inspection and replacement times, and constant factors like re-gripping, service, and maintenance times. In this study, we employed analytical methods, statistical analysis, and genetic programming approaches to evaluate the AR and thus enhance TBM performance analysis. Gamma tests were conducted to identify interrelated parameters in TBM operations. The results from these tests showed that the Applied Force per Cutter (FN) and penetration values (together forming the Field Penetration Index or FPI) are useful in estimating cutter consumption. While FN can be determined through laboratory tests, its field determination is challenging. We have developed several charts and a function derived from genetic programming to estimate the AR of a TBM. The study demonstrated that this function, formulated using genetic programming, possesses strong predictive capability and can significantly improve TBM performance analysis.

KEYWORDS:

• TBM
• cutter life
• TBM performance prediction
• field penetration index

Acknowledgments

The authors thank The Scientific and Technological Research Council of Turkiye (TUBITAK) for funding the current study (1059B192100471). Leticia Mooney is also acknowledged for her professional editorial suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Türkiye Bilimsel ve Teknolojik Araştırma Kurumu [1059B192100471].