Semi-supervised method for tunnel blasting quality prediction using measurement while drilling data
Hengxiang Jin, Qian Fang, Jun Wang, Jiayao Chen, Gan Wang, Guoli Zheng
Abstract
Predicting blasting quality during tunnel construction holds practical significance. In this study, a new semi-supervised learning method using convolutional variational autoencoder (CVAE) and deep neural network (DNN) is proposed for the prediction of blasting quality grades. Tunnel blasting quality can be measured by over/under excavation. The occurrence of over/under excavation is influenced by three factors: geological conditions, blasting parameters, and tunnel geometric dimensions. The proposed method reflects the geological conditions through measurements while drilling and utilizes blasting parameters, tunnel geometric dimensions, and tunnel depth as input variables to achieve tunnel blasting quality grades prediction. Furthermore, the model is optimized by considering the influence of surrounding rock mass features on the predicted positions. The results demonstrate that the proposed method outperforms other commonly used machine learning and deep learning algorithms in extracting over/under excavation feature information and achieving blasting quality prediction.