journal article·2026-07Open access

Performance and optimization of rapid-setting and high-early-strength cement-based grout incorporating chemical additives used in capsule expansion technology

Gang Zheng, Wei Gan, Jianyou Huang, Yu Diao, Yutao Yan, Xubin Zhang, Fei Liu, Haizuo Zhou

Abstract

As an efficient method for controlling subgrade settlement, capsule expansion technology (CET) is increasingly applied in the rapid lifting of high-speed railway subgrades. However, the effectiveness of CET is limited by the long curing time and low early strength of conventional grouts. To address this issue, this study developed a rapid-setting, high-early-strength cement-based grout (RHCG) with chemical additives for CET-based settlement control. An orthogonal experimental design was used to systematically investigate the influence of four factors: water-cement ratio, bentonite, chemical accelerators, and water-reducing agents. The results show that chemical accelerators play a significant role in controlling the initial setting time and flowability, while the water-cement ratio significantly affects the segregation behavior of the grout. The evolution of compressive strength occurs in three stages: the rapid hydration stage (Stage I: 0–1 d), the slow hydration stage (Stage II: 1–14 d), and the stable improvement stage (Stage III: 14–28 d). The optimized mix, consisting of a water-cement ratio of 0.9, 5% bentonite, 30% chemical accelerator, and 1% water-reducing agent, improves both the setting time and workability of the RHCG. Microstructure analysis reveals that the additives accelerate the formation of key hydration products, such as C–S–H gel and calcite, by promoting aluminite generation. This study offers valuable insights into material optimization and process design for CET in high-speed rail infrastructure.
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Research context

OpenAlex topics: Grouting, Rheology, and Soil Mechanics · Concrete and Cement Materials Research · Geotechnical Engineering and Analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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