2021/04/12 by Xianjie Gao, Gao, Xianjie, Xueguan Song +7 · 3 citations
Computer Science · Engineering · #Advanced machining processes and optimization #Artificial intelligence #Artificial neural network #Computer science #Data mining #Drilling and Well Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Feature selection #Generalization #Lasso (programming language) #Machine Learning (cs.LG) #Machine learning #Predictive modelling #Prognostics #Random forest #Signal Processing (eess.SP) #Support vector machine #Tunneling and Rock Mechanics #cs.LG #eess.SP #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2104.06353
published in arXiv (Cornell University) (Cornell University)
arxiv created 2021/04/12 · openalex publication_date 2021/04/12 · arxiv updated 2021/04/14 · openalex created_date 2021/04/26 · openalex updated_date 2026/08/05
Because of the fast advance rate and the improved personnel safety, tunnel boring machines (TBMs) have been widely used in a variety of tunnel construction projects. The dynamic modeling of TBM load parameters (including torque, advance rate and thrust) plays an essential part in the design, safe operation and fault prognostics of this complex engineering system. In this paper, based on in-situ TBM operational data, we use the machine-learning (ML) methods to build the real-time forecast models for TBM load parameters, which can instantaneously provide the future values of the TBM load parameters as long as the current data are collected. To decrease the model complexity and improve the generalization, we also apply the least absolute shrinkage and selection (Lasso) method to extract the essential features of the forecast task. The experimental results show that the forecast models based on deep-learning methods, \it e.g., recurrent neural network and its variants, outperform the ones based on the shallow-learning methods, \it e.g., support vector regression and random forest. Moreover, the Lasso-based feature extraction significantly improves the performance of the resultant models.