Ke Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ke Wang has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
LianZhen Zhang
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China
1 shared publication- Lin Wang
School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
1 shared publication Mengyao Qiu
School of Civil Engineering, Harbin Institute of Technology, Harbin, China
1 shared publicationShanzhen Li
School of Civil Engineering and Transportation, Northeast Forestry University, Harbin, China
1 shared publication- Shuang Liu
College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China
1 shared publication Shuang Tian
School of Civil Engineering, Harbin Institute of Technology, Harbin, China
1 shared publication
Latest Publications
- Open Access
- 18 June 2026
- Published by MDPI AG in Infrastructures
- Vol. 11 (6) , 208
Analysis of the Ability of Well-Point Dewatering to Inhibit Silty Subgrade Frost Heave
Well-point dewatering can rapidly lower the level of groundwater, making the capillary zone fall below the depth at which the subgrade is frozen. This can have the effect of inhibiting frost heave in the subgrade. This paper draws upon a project focused on treatment of the frozen section of the Shenmu–Shuozhou railway subgrade to present a method for calculating the dynamic groundwater level when pumping water using group wells. A dynamic groundwater seepage model is established, and the influence of the type of pumping wells, their layout, and spacing on variations in the groundwater level and the inhibition of frost heave in the subgrade is examined. This forms the basis of an optimal treatment plan for the frozen section of the Shenmu–Shuozhou railway. Simulation results show that a double row of wells along the route that fully penetrate the phreatic aquifer led to a large drop in the groundwater level, thus significantly inhibiting frost heave. Reducing the spacing of the wells enhances the dewatering effect and frost heave inhibition, but also reduces the strength and stability of the subgrade, so the right balance needs to be struck between the stability requirements and the frost-heave inhibition requirements. This research can serve as a reference for the treatment of frost heave in silty subgrades.
- 18 June 2026
- Published by MDPI AG in Infrastructures
- Vol. 11 (6) , 208
Open AccessAnalysis of the Ability of Well-Point Dewatering to Inhibit Silty Subgrade Frost Heave
Well-point dewatering can rapidly lower the level of groundwater, making the capillary zone fall below the depth at which the subgrade is frozen. This can have the effect of inhibiting frost heave in the subgrade. This paper draws upon a project focused on treatment of the frozen section of the Shenmu–Shuozhou railway subgrade to present a method for calculating the dynamic groundwater level when pumping water using group wells. A dynamic groundwater seepage model is established, and the influence of the type of pumping wells, their layout, and spacing on variations in the groundwater level and the inhibition of frost heave in the subgrade is examined. This forms the basis of an optimal treatment plan for the frozen section of the Shenmu–Shuozhou railway. Simulation results show that a double row of wells along the route that fully penetrate the phreatic aquifer led to a large drop in the groundwater level, thus significantly inhibiting frost heave. Reducing the spacing of the wells enhances the dewatering effect and frost heave inhibition, but also reduces the strength and stability of the subgrade, so the right balance needs to be struck between the stability requirements and the frost-heave inhibition requirements. This research can serve as a reference for the treatment of frost heave in silty subgrades.
- Open Access
- 29 December 2025
- Published by Springer Nature in Scientific Reports
- Vol. 16 (1)
Prediction of thaw settling coefficient of frozen soil using machine learning techniques
The thaw settling coefficient (TSC) of frozen soil serves as a crucial metric for assessing frozen soil thawing deformation. This work investigates the relationship of four parameters - freezing temperature (T), dry density (\(\:{\rho\:}_{d}\)), water content (w), and overlying load (P) with the TSC of frozen soil. Utilizing a nonlinear prediction approach, multiple models are employed to predict the TSC values of frozen soil under varying influencing factors. Leveraging an experimental database comprising 841 data samples, the study conducts a series of sensitivity analyses to identify the most influential parameters in each nonlinear model and to determine the optimal performing model. Results from artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models exhibit superior accuracy in predicting the TSC of frozen soil. The ANFIS model undergoes validation twice using new databases, demonstrating its reliability in meeting engineering requirements. Employing the mutual information (MI) analysis method, the study quantifies the impact of different influencing factors on the TSC of frozen soil, offering valuable insights for predicting TSC of frozen soil.- 29 December 2025
- Published by Springer Nature in Scientific Reports
- Vol. 16 (1)
Open AccessPrediction of thaw settling coefficient of frozen soil using machine learning techniques
The thaw settling coefficient (TSC) of frozen soil serves as a crucial metric for assessing frozen soil thawing deformation. This work investigates the relationship of four parameters - freezing temperature (T), dry density (\(\:{\rho\:}_{d}\)), water content (w), and overlying load (P) with the TSC of frozen soil. Utilizing a nonlinear prediction approach, multiple models are employed to predict the TSC values of frozen soil under varying influencing factors. Leveraging an experimental database comprising 841 data samples, the study conducts a series of sensitivity analyses to identify the most influential parameters in each nonlinear model and to determine the optimal performing model. Results from artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models exhibit superior accuracy in predicting the TSC of frozen soil. The ANFIS model undergoes validation twice using new databases, demonstrating its reliability in meeting engineering requirements. Employing the mutual information (MI) analysis method, the study quantifies the impact of different influencing factors on the TSC of frozen soil, offering valuable insights for predicting TSC of frozen soil.