A review and case study of Artificial intelligence and Machine learning methods used for ground condition prediction ahead of tunnel boring Machines

• Published in: Tunnelling and underground space technology Vol. 125; p. 104497
• Main Authors: Ayawah, Prosper E.A., Sebbeh-Newton, Sylvanus, Azure, Jessica W.A., Kaba, Azupuri G.A., Anani, Angelina, Bansah, Samuel, Zabidi, Hareyani
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.07.2022; Elsevier BV
• Subjects: Artificial intelligence; Boring machines; Classification; Datasets; Drilling & boring machinery; Ground condition prediction; Hazard detection; Literature reviews; Machine learning; Mathematical models; Parameters; Performance measurement; Rock mass classification; Rock mass rating; Rocks; Transfer tunnels; Tunnel boring machines; Tunnel construction; Tunnels; Underground construction; Water transfer; Tunnel boring machines; Ground condition prediction; Artificial intelligence; Rock mass classification; Machine learning; Hazard detection
• ISSN: 0886-7798; 1878-4364
• DOI: 10.1016/j.tust.2022.104497

•Several machine learning methods can be used to predict ground conditions ahead of TBMs with high accuracy.•Ensemble methods have better ground condition prediction accuracy than other machine learning models evaluated.•The classification system used in characterizing the ground condition affects the performance of the machine models.•The prediction performance of the machine models is different in soils and rocks of different lithologies. This paper reviews literature on data-driven approaches for characterizing rock mass and ground conditions in tunnels. There have been significant advances in the use of both unsupervised and supervised machine learning (ML) methods to predict the ground condition or rock mass class ahead of tunnel boring machines (TBMs). This study evaluates the likelihood of a single ML model being able to predict ground conditions or rock mass ahead of TBMs regardless of the TBM type, rock mass condition, or the rock mass classification system used in classifying the rock mass conditions. To do this, extensive literature review was conducted to develop a list of ML models for the evaluation. Ground conditions/rock mass data and TBM operational data collected from the Pahang-Selangor Raw Water Transfer Tunnel (PSRWT) project were used to evaluate the selected models. The selected models were trained and evaluated on the PSRWT dataset. The performance metrics obtained from these models using the PSRWT data were then compared to the performance metrics reported by the respective authors. The second part of this paper focused on determining the best model among all the models reviewed using nine input variables from the from PSRWT dataset. Variable importance evaluation was conducted to determine the relevant input variables for this analysis. The results revealed that the ML models performed well in correctly predicting the rock mass conditions on the PSRWT dataset, but the performances were relatively lower compared to the performances reported by the various authors. However, when all the nine selected variables were used to train and test the models, better performances were achieved. This indicates that it is highly unlikely that a single ML model can predict every rock mass behavior with the same degree of accuracy using the same input variables. The model type, number and input parameters required for a given model will depend on among other factors, the soil and rock types and their conditions. It is worth noting that where rock mass classes were similar to the PSWRT data, the models’ performances were similar. It is therefore highly recommended to conduct site-specific modeling to understand which parameters are relevant and determine the kind of model that works well for the different cases. If a model is being adopted due to similarities in rock mass, it is recommended to proceed with caution and ascertain that model works in a similar manner.