Grouped machine learning methods for predicting rock mass parameters in a tunnel boring machine-driven tunnel based on fuzzy C-means clustering

To guarantee safe and efficient tunneling of a tunnel boring machine (TBM), rapid and accurate judgment of the rock mass condition is essential. Based on fuzzy C-means clustering, this paper proposes a grouped machine learning method for predicting rock mass parameters. An elaborate data set on field rock mass is collected, which also matches field TBM tunneling. Meanwhile, target stratum samples are divided into several clusters by fuzzy C-means clustering, and multiple submodels are trained by samples in different clusters with the input of pretreated TBM tunneling data and the output of rock mass parameter data. Each testing sample or newly encountered tunneling condition can be predicted by multiple submodels with the weight of the membership degree of the sample to each cluster. The proposed method has been realized by 100 training samples and verified by 30 testing samples collected from the C1 part of the Pearl Delta water resources allocation project. The average percentage error of uniaxial compressive strength and joint frequency (Jf) of the 30 testing samples predicted by the pure back propagation (BP) neural network is 13.62% and 12.38%, while that predicted by the BP neural network combined with fuzzy C-means is 7.66% and 6.40%, respectively. In addition, by combining fuzzy C-means clustering, the prediction accuracies of support vector regression and random forest are also improved to different degrees, which demonstrates that fuzzy C-means clustering is helpful for improving the prediction accuracy of machine learning and thus has good applicability. Accordingly, the proposed method is valuable for predicting rock mass parameters during TBM tunneling. This paper used fuzzy C-means clustering to improve the accuracy of the traditional machine learning method, and provides a method to predict rock mass parameters in tunnels constructed by tunnel boring machine with acceptable accuracy. image The traditional rock mass parameter predicting method based on machine learning and tunnel boring machine driving data is improved by fuzzy C-means clustering. Using fuzzy C-means clustering, samples are divided into multiple clusters and multiple targeted submodels are trained. The prediction results weighted by the multiple submodels are more accurate than those obtained by the traditional method. The proposed method is verified by multiple machine learning algorithms, including back propagation neural network, support vector regression, random forest, and 130 series of field data. The results show that the proposed method has advantages in terms of accuracy compared with the traditional method.