Numerical simulation of concrete strength based on microstructure and mineral composition analysis using micro-CT and XRD technology

Abstract

The investigation of mechanical properties in porous materials, such as concrete, has been a significant area of interest in the fields of civil engineering and architecture. Traditional macroscopic mechanical testing methods are limited due to destructive experimental conditions, time-consuming procedures, and the inability to characterize internal structures. To overcome these limitations, researchers have utilized microscopic factors with micro-CT and simulation models. Different simulation methods demand mechanical parameters to effectively model the constituent materials; however, there exists variability in the amount and standard values of these input parameters, which leads to a decrease in the reliability of the obtained results. In order to tackle this issue, a novel approach is proposed, which integrates mineral component analysis using X-ray diffraction (XRD) and establishes coefficients to minimize the number of necessary input parameters. Simulation results demonstrate excellent agreement with experimental measurements, accurately reproducing the stages of elastic deformation, failure, and fracture in the stress-strain curve of concrete. Our findings reveal that changes in the concrete skeletal structure, microstructure, and mineral composition significantly influence compressive strength. Coarse aggregates and high-mechanical-performance minerals, in particular, have a substantial impact. Additionally, the mechanical properties show a significant linear relationship with the strength reduction coefficient of aggregates and concrete cores. In conclusion, the proposed method, combining CT scanning and XRD with FDEM, proves to be a reliable and practical approach for accurately assessing the mechanical properties of concrete. This methodology simplifies the intricate processes associated with the time-intensive parameter calibration, leading to a more efficient parameter allocation procedure. As a result, this method enhances concrete design, promotes a better understanding of parameter impacts, and enables accurate prediction of concrete behavior across diverse scenarios.