Image Recognition-Based Analysis and Simulation Optimization of Mechanical Performance of Steel Fiber-Reinforced Concrete

The traditional analysis of the mechanical performance of steel fiber-reinforced concrete (SFRC) predominantly relies on the assumption of an ideally random fiber distribution. This approach fails to account for the significant distribution inhomogeneity caused by practical construction processes like vibration, creating a discrepancy between simulation and reality. To address this, the main aim of this study was to demonstrate the critical impact of realistic fiber distribution on mechanical behavior by integrating image recognition with meso-mechanical simulation. Multi-factor controlled experiments were conducted to investigate the influence of vibration energy, fiber content, and aggregate volume fraction. An image recognition method was developed to accurately characterize the real spatial distribution of fibers, and these data were used to construct a three-dimensional meso-scale finite element model. Compared with the traditional model assuming random distribution, the proposed model based on the actual distribution showed significantly improved agreement with experimental results in terms of crack propagation paths and reduced the prediction error of the initial cracking load by more than 16.3%. For practitioners, the key takeaway is that modeling based on the actual fiber distribution is crucial for achieving realistic simulations. Our work provides a validated methodology to incorporate real distribution data, thereby improving the reliability of numerical assessments for SFRC structures, rather than relying on idealized random distribution assumptions.