R
Renpeng Chen
Researcher at Hunan University
Publications - 172
Citations - 4698
Renpeng Chen is an academic researcher from Hunan University. The author has contributed to research in topics: Pile & Settlement (structural). The author has an hindex of 30, co-authored 160 publications receiving 2648 citations. Previous affiliations of Renpeng Chen include Zhejiang University & Chinese Ministry of Education.
Papers
More filters
Journal ArticleDOI
Experimental study on face instability of shield tunnel in sand
TL;DR: In this paper, a series of 3D large-scale model tests with a tunnel of 1m diameter were conducted in dry sand for various cover-to-diameter ratios C / D ǫ = 0, 1, and 2 (ie, relative depth; C is the cover depth and D is the diameter of tunnel) each test provided a measurement of the support pressure and the ground settlement with the advance of face displacement.
Journal ArticleDOI
Face stability analysis of shallow shield tunnels in dry sandy ground using the discrete element method
TL;DR: In this paper, the failure mechanism and limit support pressure of a tunnel face in dry sandy ground were investigated by using discrete element method (DEM), which has particular advantages for revealing mechanical properties of granular materials.
Journal ArticleDOI
Investigation of response of metro tunnels due to adjacent large excavation and protective measures in soft soils
TL;DR: In this article, the influence of a nearby large excavation on existing metro tunnels of the Ningbo Metro Line 1 in sensitive soft soils is investigated and presented, and several protective measures are studied, including divided excavation, soil improvement and a cut-off wall.
Journal ArticleDOI
Full-scale model testing on a ballastless high-speed railway under simulated train moving loads
TL;DR: In this paper, the authors present comprehensive experimental results on track vibration and soil response of a ballastless high-speed railway from a full-scale model testing with simulated train moving loads at various speeds.
Journal ArticleDOI
Prediction of maximum surface settlement caused by earth pressure balance (EPB) shield tunneling with ANN methods
TL;DR: A modified index that defines the physical significance of the input parameters was proposed to quantify the geological parameters, which improves the prediction accuracy of ANN models and found the GRNN model to outperform the BP and RBF neural networks in terms of accuracy and computational time.