Prediction of Forward Tunnel Face Score of Rock Mass Classification for Stability by Applying Machine Learning to Drilling Data

Hayashi, Hisashi; Miyanaka, Miku; Gomi, Haruka; Tatsumi, Junichi; Kawabe, Nobuyuki; Shinji, Masato

doi:10.1007/978-3-030-32029-4_23

Information Technology in Geo-Engineering

2019

DOI: 10.1007/978-3-030-32029-4_23

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Prediction of Forward Tunnel Face Score of Rock Mass Classification for Stability by Applying Machine Learning to Drilling Data

Hisashi Hayashi

Miku Miyanaka

Haruka Gomi³

et al.

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“…structural defect identification (e.g Ding et al 2019),. tunnel face stability (e.g Hayashi et al 2019),. rockburst prediction (e.g.…”

mentioning

confidence: 99%

Machine learning to inform tunnelling operations: recent advances and future trends

Sheil

Suryasentana

Mooney

et al. 2020

Proceedings of the Institution of Civil Engineers - Smart Infra

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The proliferation of data collected by modern tunnel-boring machines (TBMs) presents a substantial opportunity for the application of machine learning (ML) to support the decision-making process on-site with timely and meaningful information. The observational method is now well established in geotechnical engineering and has a proven potential to save time and money relative to conventional design. ML advances the traditional observational method by employing data analysis and pattern recognition techniques, predicated on the assumption of the presence of enough data to describe the physics of the modelled system. This paper presents a comprehensive review of recent advances and applications of ML to inform tunnelling construction operations with a view to increasing their potential for uptake by industry practitioners. This review has identified four main applications of ML to inform tunnelling – namely, TBM performance prediction, tunnelling-induced settlement prediction, geological forecasting and cutterhead design optimisation. The paper concludes by summarising research trends and suggesting directions for future research for ML in the tunnelling space.

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“…structural defect identification (e.g Ding et al 2019),. tunnel face stability (e.g Hayashi et al 2019),. rockburst prediction (e.g.…”

mentioning

confidence: 99%

Machine learning to inform tunnelling operations: recent advances and future trends

Sheil

Suryasentana

Mooney

et al. 2020

Proceedings of the Institution of Civil Engineers - Smart Infra

View full text Add to dashboard Cite

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Risk Assessment of Building Damage Induced by Tunnelling Through a Gene Expression Programming Model

Namazi¹,

Hajihassani

Gilani

et al. 2022

Geotech Geol Eng

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A gene expression programming (GEP) model has been used for predicting the risk of building damage due to ground movement induced by tunnelling. A critical displacement parameter, namely deflection ratio, and building major affecting factors have been identified from the previous empirical method. A general GEP model is trained and evaluated using the actual collected data from Karaj Railway in Iran, and published case histories from other projects around the world. The general model results are compared with the actual field measurements for the training of the model purpose. The trained network is used to estimate the risk of damage for a few case histories. It is shown that not only a GEP model learns a relationship between the risk of building damage with appropriate ground movement and building parameters, but it can also develop the learning to predict damage for very different geological and tunnel and building geometric conditions.

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Prediction of Forward Tunnel Face Score of Rock Mass Classification for Stability by Applying Machine Learning to Drilling Data

Cited by 2 publications

References 0 publications

Machine learning to inform tunnelling operations: recent advances and future trends

Machine learning to inform tunnelling operations: recent advances and future trends

Risk Assessment of Building Damage Induced by Tunnelling Through a Gene Expression Programming Model

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