Static Analysis Method and Structural Optimization of Box-Type Subgrade for High-Speed Railways
Year: 2025
Type: article
Abstract: A method based on a statically indeterminate planar frame model was developed for the analysis and evaluation of box-type subgrade structures in high-speed railways. The objective of this study is to establish a concise and mechanically rigorous framework capable of quantifying the effects of key geometric parameters on bending moments, shear forces, and slab deflection, thereby providing guidance for structural refinement. Symbolic derivation and structural mechanics theory are combined to formulate the analytical model, and finite element simulations in Abaqus are used to verify the theoretical predictions under the design loads of the Quzhou–Lishui railway section located between Quzhou City and Lishui City in Zhejiang Province, China. Key findings show the maximum bending moments at the slab center and web-slab junction, reaching 14,818 kN·m, and the maximum shear forces of 16,934 kN at the web-slab junction. The top slab center showed the maximum deflection, approximately 7.5 × 10−2 mm. Simulation errors were below 5%. The optimization results recommend a web spacing of 4.5–5 m and a web height of 5–8 m. In an engineering case, reducing the web spacing from 6 m to 5 m and adjusting the web height from 7 m to 6.5 m dropped the top-slab mid-span bending moment from 10,628 kN·m to 5603 kN·m (an 89.7% reduction). Concrete use fell by 2.61% (from 24,900 to 24,250 m3/km), and overall costs dropped by about 5%. These findings demonstrate that the proposed analytical method provides an effective basis for rational parameter selection and preliminary structural design of box-type railway subgrades.
Source: Applied Mechanics
Authors: Jianfeng Fang, Gentian Hong, Xin Wen, Runmin Wang, Qiang Shentu, Tao Chen, Weichao Li
Institution: Tongji University
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,448
OpenAlex ID: W7110437428