Concrete is a highly heterogeneous composite material, and accurately predicting its elastic modulus remains a major challenge in mechanical analysis. To address this, this study systematically investigates the predictive performance of several classical homogenization methods for estimating the effective elastic modulus of concrete, including the dilute approximation, self-consistent method, generalized self-consistent method, Mori–Tanaka model, differential method, as well as the Voigt and Reuss models. To enhance prediction accuracy, an improved computational framework is proposed based on an iterative strategy that enables dynamic updating of model parameters. This approach combines principles of mesomechanics with numerical simulation techniques and is implemented using Mathematica for both symbolic and numerical computations. The performance of the models is evaluated under varying aggregate volume fractions and aggregate-matrix stiffness combinations, and validated using multiple experimental datasets from the literature. The results show that the iterative strategy significantly improves the predictive accuracy of several models, reducing the maximum error by up to 30%. Further analysis indicates that the dilute method performs best at low aggregate volume fractions, the Mori–Tanaka model yields the most accurate results when the aggregates are stiff and moderately concentrated, and the generalized self-consistent method outperforms the standard version when the elastic moduli of the aggregate and matrix are similar.

Rock bridges are important structures for maintaining rock mass stability, but their shapes are not well known. The researchers propose a method for determining the shape of rock bridges based on experiments, discrete element methods and machine learning, which is applicable to complex joints with arbitrary spatial distribution. Numerical models are constructed using the discrete element method, and parameter matching is performed based on experimental results. The particles were clustered using the k-means algorithm with the maximum principal stress (σ₁) as an indicator and the selection of initial values was optimized. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to delete the noise from the particles. Finally, the boundary lines of the particles were extracted by self-programming, and the shape of the rock bridges was determined. Twenty-four sets of simulations were used to analyze the effect of rock bridges on the specimens. The results show that the failure mode of the specimen changes from shear to tensile damage as the cohesive force of the rock bridges increases. The peak strength and peak strain of the specimens increased with the increase of cohesion in the rock bridge. Rock bridges are the fastest growing areas of stress in the specimen.

Previous joint test studies have mainly been conducted under the condition of uniformly distributed loads, but in engineering, overlying loads are often non-uniformly distributed. It is necessary to investigate the shear mechanical properties of joints under non-uniform loads. This paper establishes three typical mechanical models: far-field concentration (FFC), near-field concentration (NFC), and focus on center (FOC). Direct shear test simulations using the DEM software PFC reveal that the location of load concentration affects main shear parameters. The closer the load concentration is to the far end along the shear direction, the greater the deformation of rock mass during failure is, the higher the proportion of shear cracks is, and the greater the strength and energy required for failure are. Compared to a uniformly distributed load (UD) condition, FOC shows similar mechanical properties; NFC yields inferior outcomes, while FFC results in superior mechanical performance compared to UD. By utilizing machine learning, five prediction models for non-uniform load shear strength are developed, with the Genetic-XGBoost algorithm demonstrating the highest accuracy. Weight calculation results indicate that load distribution form is the most critical factor influencing shear strength.

Coiled carbon nanotubes (CCNTs) and hydrogenated coiled carbon nanotubes (HCCNTs) are advanced nanomaterials recognized as having exceptional mechanical properties and the potential to enhance composite materials. This study examines the pullout performance of CCNTs and HCCNTs in reinforcing asphalt nanocomposites through molecular dynamics simulations. Results indicate that CCNTs generally exhibit superior maximum pullout forces compared to HCCNTs, particularly at lower temperatures, suggesting stronger adhesion within the asphalt matrix. Conversely, HCCNTs demonstrate enhanced stability and flexibility under varying temperature conditions, allowing them to maintain more pullout energy over longer distances. Molecular movement analyses reveal that CCNTs facilitate greater displacement of asphalt molecules, indicating stronger interactions, while spatial configuration studies show that CCNTs attract more asphalt molecules in close proximity, enhancing their effectiveness. Additionally, HCCNTs possess a larger free volume, which may increase the risk of nano-damage but also allows for greater flexibility during the pullout process. This research highlights the distinct roles of CCNTs and HCCNTs in improving asphalt’s mechanical properties, ultimately contributing to the development of more resilient and sustainable asphalt pavements.