TECHNICAL | MECHANISED TUNNELLING


ML METHODS FOR TBM PERFORMANCE MODELS ML techniques also have been used to examine relationships between FPI and geological parameters for each rock type. The background and the pro/cons of using ML for TBM performance prediction have been discussed by Zhang et al. (2020), Salimi et al. (2019a), and Armaghani et al. (2017). In this context, decision trees (DT) are methods that are relatively easily applied, being transparent and interpretable; they allow patterns to be obtained to better explain given phenomena, showing the most important variables and their threshold values. This contribution reports the application of regression trees to assess the performance of TBM, and offers graphs for others to reproduce the results and to predict TBM performance for future projects.


TBM performance prediction models using regression tree One of the most popular techniques in data mining (analysis) is DT, in which a simple and comprehensible structure is used for classification, recognition, decision making, and prediction of certain target parameters. There are several kinds of DT analytical methods


Top, figure 4:


Regression tree developed for estimation of TBM FPI prediction ‘G & GN’


Bottom, figure 5: Optimum tree (CART) size generated by R statistical program for each rock type categorisation


and CART has been widely used with a high level of accuracy and performance for predicting problems in different engineering fields (Salimi 2021; Breiman et al. 1984; Tiryaki 2008). CART is a rule-based analysis method and is based on whether the dependent variable is qualitative or quantitative; as such it can be categorised as a classification tree (CT) or regression tree (RT), respectively. The technique is recommended where the relationships between dependent variable


22 | July 2024


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