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

Zhou, Jian; Yang, Peixi; Yong, Weixun; Khandelwal, Manoj; Huang, Shuai

doi:10.1007/s11600-024-01320-8

Artykuł - szczegóły

Tytuł artykułu

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

Autorzy

Zhou Jian , Yang Peixi , Yong Weixun , Khandelwal Manoj , Huang Shuai

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-024-01320-8

Warianty tytułu

Języki publikacji

Abstrakty

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.

Słowa kluczowe

tunnel boring machine lithology prediction particle swarm optimization algorithm Ant-Lion optimization algorithm Gray Wolf optimization algorithm random forest

maszyna do drążenia tuneli prognozowanie litologii algorytm optymalizacji roju cząstek algorytm optymalizacji Ant-Lion algorytm optymalizacji Gray Wolf

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2024

Tom

Vol. 72, no. 3

Strony

1847--1866

Opis fizyczny

Bibliogr. 100 poz.

Twórcy

autor

Zhou Jian

j.zhou@csu.edu.cn

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

autor

Yang Peixi

215511049@csu.edu.cn

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

autor

Yong Weixun

yongweixun@qq.com

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

autor

Khandelwal Manoj

mkhandelwal1@gmail.com

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

autor

Huang Shuai

235501020@csu.edu.cn

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

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