Optimizing grout injection: A computational and experimental study

Abstract

Grouting is a critical engineering technique used to repair and reinforce infrastructure, with its effectiveness largely dependent on the grout’s fluidity and injectability. This study investigates grout injection for railroad ballast reinforcement through a comprehensive approach combining computational modeling and laboratory experiments to optimize grout mixture rheology and injection strategies. The computational model simulates grout flow through aggregates using particle-resolving method for small-scale cases and a porosity-based model for large-scale applications. Parameters for the porosity model were calibrated by upscaling the small-scale results via numerical Darcy experiments. The non-Newtonian behavior of grout is represented by the Herschel–Bulkley model. Rheological parameters for the optimal grout mixture were identified through model simulations, experimental comparisons, and fluidity requirements. Laboratory experiments helped narrow down candidate rheological parameters which were further evaluated using the computational model. Additionally, the model guided the design of the injection pipe layout, spacing, and perforation hole locations. This integrated study provides an optimized grout mixture and injection configuration, offering practical recommendations for railroad ballast reinforcement applications.