97 citations · 125 across the 6 of their papers we have counts for
6 papers
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data
Hao Wang, Lixue Liu, Xueguan Song +2
In tunnel boring machine (TBM) underground projects, an accurate description of the rock-soil types distributed in the tunnel can decrease the construction risk ({\it e.g.} surface…
The direct force correction based framework for general co-rotational analysis
Ziyun Kan, Kaijun Dong, Biaosong Chen +2
The use of nonlinear projection matrix in co-rotational (CR) analysis was pioneered by Rankin and Nour-Omid in 1990s (Computers & Structures, 30 (1988) 257-267; Comput. Methods App…
Real-time Forecast Models for TBM Load Parameters Based on Machine Learning Methods
Xianjie Gao, Xueguan Song, Maolin Shi +2
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 mo…
A support vector regression-based multi-fidelity surrogate model
Maolin Shi, Shuo Wang, Wei Sun +2
Computational simulations with different fidelity have been widely used in engineering design. A high-fidelity (HF) model is generally more accurate but also more time-consuming th…
High-low level support vector regression prediction approach (HL-SVR) for data modeling with input parameters of unequal sample sizes
Maolin Shi, Wei Sun, Xueguan Song +1
Support vector regression (SVR) has been widely used to reduce the high computational cost of computer simulation. SVR assumes the input parameters have equal sample sizes, but une…
Geology prediction based on operation data of TBM: comparison between deep neural network and statistical learning methods
Maolin Shi, Xueguan Song, Wei Sun
Tunnel boring machine (TBM) is a complex engineering system widely used for tunnel construction. In view of the complicated construction environments, it is necessary to predict ge…