Understanding the role of aggregates in the compressive strength of brittle composites is crucial for optimizing construction material usage. In this study, we employed the rigid-body-spring-network model validated with experimental data. Through uniaxial compression loading tests on concrete, considering Young’s modulus and compressive strength of the coarse aggregate as variables, we elucidated how the coarse aggregate’s physical properties influence concrete's compressive strength, illustrated using a straightforward diagram. When the coarse aggregate’s Young's modulus is lower than the mortar's, the stress transfer path within the mortar bends and cracks more rapidly, significantly lowering strength. Conversely, if the coarse aggregate’s Young’s modulus exceeds that of the mortar, stress becomes concentrated in the aggregate, and the crack of the coarse aggregate governs the failure of concrete.

To study the variation in the strength of carbon fiber-reinforced concrete (CFRC) under impact loading after freeze-thaw cycles, we conducted a split Hopkinson pressure bar impact compression test on CFRC subjected to freeze-thaw cycles. Dynamic stress-strain curves of CFRC subjected to different numbers of freeze-thaw cycles (0, 50, 100, and 150 cycles) under impact loading (at strain rates of 55 s^-1, 110 s^-1 and 165 s^-1). The results show that even after freeze-thaw cycles, CFRC has a significant strain rate effect, with the peak stress increasing with the strain rate. Meanwhile, the peak stress of CFRC gradually decreased as the number of freeze-thaw cycles increased. The mechanisms of the effects of freeze-thaw cycles and strain rate on the peak stress of CFRC were analyzed from an energy transformation perspective. A damage factor was defined through the dynamic elastic modulus, and the freeze-thaw cycle damage evolution of the CFRC was analyzed based on the Weibull probability distribution. A predictive model for the impact compressive strength of CFRC was developed considering freeze-thaw cycle damage and strain rate. This model accurately predicts the impact compressive strength of CFRC after freeze-thaw cycles, providing a reference for predicting CFRC strength under impact loading after freeze-thaw cycles.

Ensuring the durability of reinforced concrete structures through numerical analysis requires addressing complex multiphysics phenomena that govern the evolution of volumetric deformations, strength and stiffness. The global structural response is significantly influenced by spatially varying properties, highlighting the need for advanced numerical tools to accurately account for these localized effects. This paper presents a pointwise thermo-hygro-mechanical (THM) model for 3D finite element simulations designed to perform numerical analysis of concrete behaviour throughout its service life. The mechanical properties of concrete are modelled based on the local hygrothermal properties. Concrete maturity is simulated using the equivalent age concept. The viscoelastic behaviour of concrete is described through the microprestress solidification theory. Key innovation is a new algorithm for integrating time-dependent mechanical properties into total strain crack models, assuming adaptive stress-strain relationships incorporating rotating and fixed crack concepts. The pointwise THM model is validated at the structural level through numerical simulations of experimental tests in reinforced concrete (RC) elements, assessing its ability to predict early-age stresses, crack patterns in thick members, and long-term shrinkage effects on the crack opening. Conclusions are based on the model’s efficacy in simulating the structural response of RC structures from early ages through their service life.

Precise prediction of self-compacting concrete (SCC) workability is vital for efficient production and robust structural performance. This study integrates paste rheological threshold theory with machine learning (ML) to enhance the accuracy of SCC slump flow (SF) and V-Funnel (VF) predictions. A multiscale dataset comprising 313 experimental sets was constructed, featuring both SCC mix proportion data and paste experimental parameters as inputs, which capture critical factors not represented by mix proportions alone. Eight distinct ML models were trained and evaluated, and the results confirmed that models using the multiscale dataset achieved higher predictive accuracy than those trained on mix proportion data alone. The best-performing model was then refined via the Optuna framework for hyperparameter optimization, achieving R² values of 0.96 for SF and 0.91 for VF. Shapley Additive Explanations were employed to elucidate the contributions of each input variable, demonstrating that paste workability significantly enhances both predictive capability and model interpretability. These findings establish a convenient and intelligent approach for predicting SCC workability, thereby promoting its practical engineering application.