Optimization of Slab Bottom Grouting: A Case Study Utilizing an Underplate Grouting Model with Brinkman Equation and Level Set Method
DOI:
https://doi.org/10.21776/ub.civense.2024.007.02.7Keywords:
Underplate grouting model, Brinkman equation, Level set method, Slab bottom grouting projectAbstract
This paper presents a case study on a slab bottom grouting project, focusing on the development and optimization of an underplate grouting model. The primary research objective is to determine the optimal grouting implementation strategy for the project by analyzing the impact of various structural parameters, grout properties, and grouting process properties on the grouting radius. The model incorporates the Brinkman equation and the level set method to explore the impact of various structural parameters, grout properties, and grouting process properties on the grouting radius. Through extensive analysis, the paper identifies the optimal implementation strategy for the project. The study reveals that parameters such as subbase permeability (K2), grouting pressure (P), and grouting time (t) positively affect the grouting radius, while the grout viscosity (μg) has a negative impact. The influence of grout density (ρg) and displaced fluid density (ρa, ρw) on the grouting radius is found to be negligible. Based on the underplate grouting model, the paper determines the optimal grouting implementation scheme for the slab bottom grouting project, specifying a grouting pressure of 1.2 MPa and a grouting time of 60 seconds. Furthermore, the effectiveness of the proposed implementation strategy is indirectly validated through the Falling Weight Deflectometer (FWD) analysis. The results demonstrate that the underplate grouting model successfully eliminates voids at the bottom of the slab, providing conclusive evidence for the reliability of the grouting strategy proposed by the model. Overall, this study contributes to the understanding and optimization of slab bottom grouting projects, offering valuable insights for practitioners in the field.
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Copyright (c) 2024 Deqiang Chen, Zhu Pan, Zhenchao Chang, Peng Mo, Tianzhi Hao
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