Evaluation of tunnel seismic prediction (TSP) result using the Japanese highway rock mass classification system for Pahang-Selangor Raw Water Transfer Tunnel

Von, W. C.; Ismail, M. A. M.

doi:10.1063/1.5005677

Cited by 5 publications

(3 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…technology. Although Von and Ismail (2017) reported a prediction accuracy of R 2 = 0.85, they noted that the small datasets at the beginning of a project lead to less reliable predictions. Wei et al (2018) documented one of the most comprehensive applications of ML to a new 'Tunnel Look-ahead Imaging Prediction System' (TULIPS).…”

Section: Geological Forecastingmentioning

confidence: 99%

“…electromagnetic methods, electrical methods, seismic reflection methods, infrared detection methods), very few studies have explored the integration of machine learning algorithms to improve geophysical predictions. Both Alimoradi et al (2008) and Von and Ismail (2017) used an ANN to identify rock characteristics using ground parameters obtained using Tunnel Seismic Prediction (TSP)…”

Section: Geological Forecastingmentioning

confidence: 99%

See 1 more Smart Citation

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

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.

show abstract

Section: Geological Forecastingmentioning

confidence: 99%

Section: Geological Forecastingmentioning

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

show abstract

“…Accordingly, the relationship between rock mass rating and seismic wave velocity can be quantified, as demonstrated in Table 1 by previous studies. Many research studies (Von and Ismail, 2017;Zhou et al, 2017;Li et al, 2019a;Hou et al, 2019;Lu et al, 2020) have proven that the seismic wave velocity information acquired by TSP can reflect the geological conditions in front of the tunnel face. To be specific, a portion of studies (Montalvo et al, 2015;Von and Ismail, 2017;Zhou et al, 2017;Bu et al, 2018;Li et al, 2019b;Hou et al, 2019;Shan et al, 2019;Lu et al, 2020) have revealed the link between the rock quality index and seismic wave, in particular engineering cases.…”

Section: Introductionmentioning

confidence: 99%

Predicting rock mass rating ahead of the tunnel face with Bayesian estimation

Li,

Chen,

Tang

et al. 2024

Front. Earth Sci.

View full text Add to dashboard Cite

The rock mass rating (RMR) system plays a crucial role in geomechanics assessments for tunnel projects. However, conventional methods combining empirical and geostatistical approaches often yield inaccuracies, particularly in areas with weak strata such as faults and karst caves. To address these uncertainties and errors inherent in empirical techniques, we propose a progressive RMR prediction strategy based on the Bayesian framework. This strategy incorporates three key components: 1) Variogram modeling: utilizing observational data from the excavation face, we construct and update a variogram model to capture the spatial variability of RMR. 2) TSP-RMR statistic model: we integrate a TSP-RMR statistical model into the Bayesian sequential update process. 3) Bayesian maximum entropy (BME) integration: the BME method combines geological information obtained from tunnel surface excavation with tunnel seismic prediction (TSP) data, ultimately enhancing the RMR prediction accuracy. Our methodology is applied to the Laoying rock tunneling project in Yunnan Province, China. Our findings demonstrate that the fusion of soft data and geological interpretation significantly improves the accuracy of RMR predictions. At selected prediction points, the relative error of our method is less than 15% when compared to the traditional Kriging method. This approach holds substantial potential for advancing RMR estimation ahead of tunnel excavation, particularly when advanced geological forecast data are available.

show abstract

Generative adversarial network for geological prediction based on TBM operational data

Zhang

Liang

Song

et al. 2022

Mechanical Systems and Signal Processing

View full text Add to dashboard Cite

Evaluation of tunnel seismic prediction (TSP) result using the Japanese highway rock mass classification system for Pahang-Selangor Raw Water Transfer Tunnel

Cited by 5 publications

References 5 publications

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

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

Predicting rock mass rating ahead of the tunnel face with Bayesian estimation

Generative adversarial network for geological prediction based on TBM operational data

Contact Info

Product

Resources

About