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

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@Article{Kalayci-Sahinoglu27052024,

• author = "Ulku {Kalayci Sahinoglu} and Murat Karakus",
• title = "Predicting advance rate and cutter life in {TBM} tunnelling using evolutionary algorithm",
• journal = "International Journal of Mining, Reclamation and Environment",
• year = "2024",
• volume = "38",
• number = "5",
• pages = "390--406",
• keywords = "genetic algorithms, genetic programming, TBM, cutter life, TBM performance prediction, field penetration index",
• publisher = "Taylor \& Francis",
• DOI = "10.1080/17480930.2024.2305054",
• abstract = "Tunneling with a Tunnel Boring Machine (TBM) offers several advantages over conventional tunneling 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...",

}

Genetic Programming entries for Ulku Kalayci Sahinoglu Murat Karakus

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