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A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters

  • Research Article - Anthropogenic Geohazards
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Abstract

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924; PREA = 0.0.9999976; RECA = 0.999775; F1A = 0.999876; Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

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Funding

This research is partially supported by the Distinguished Youth Science Foundation of Hunan Province of China (2022JJ10073) and the Outstanding Youth Project of Hunan Provincial Department of Education (23B0008).

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Authors and Affiliations

  1. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Jian Zhou, Peixi Yang, Weixun Yong & Shuai Huang

  2. Institute of Innovation, Science and Sustainability, Federation University Australia, Ballarat, VIC, 3350, Australia

    Manoj Khandelwal

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  1. Jian Zhou
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Contributions

Jian Zhou involved in conceptualization, methodology, validation, investigation, visualization, writing—review and editing, supervision, and funding acquisition. Peixi Yang involved in methodology, formal analysis, validation, resources, visualization, software, and writing—original draft. Weixun Yong involved in formal analysis, visualization, writing—review and editing. Manoj Khandelwal involved in formal analysis and writing—review and editing. Shuai Huang involved in data curation, visualization, and writing and editing. All authors read and approved the final manuscript.

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Edited by Prof. Aderson Farias do Nascimento (CO-EDITOR-IN-CHIEF).

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Zhou, J., Yang, P., Yong, W. et al. A comparative analysis of hybrid RF models for efficient lithology prediction in hard rock tunneling using TBM working parameters. Acta Geophys. 72, 1847–1866 (2024). https://doi.org/10.1007/s11600-024-01320-8

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