This study presents experimental and numerical methods to reveal concrete structures’ crack propagation and intersection laws under static and dynamic loads. Firstly, a numerical simulation method was established using the user-defined material subroutines to solve the free crack surface contact problem of concrete structures. Secondly, three-point bending tests of concrete beams containing double cracks of different approaching angles were carried out based on the digital image correlation (DIC) technology to study the intersection and propagation of cracks. Finally, the fracture processes of double-crack concrete beams and concrete gravity dams with and without a longitudinal crack were simulated and analyzed under static and dynamic loads. The numerical results were compared with the test results to verify the effectiveness and accuracy of the proposed method in simulating crack intersection and propagation in concrete structures. Results indicate that the crack intersection affects the fracture path of concrete structures, weakens their bearing capacity, and accelerates the failure of the structures. The proposed simulation method provides an effective technical approach for crack propagation prediction and safety evaluation of engineering structures.

Recycled aggregate concrete (RAC) exhibits inferior mechanical and durability properties owing to the deterioration of the recycled coarse aggregate (RCA) surface quality. To improve the surface properties of RCA, the reinforcement efficiency of RAC, and the maneuverability of the surface treatment method, this study used magnesium phosphate cement (MPC), a clinker-free low-carbon cement with excellent bonding properties, to precoat RCA under three-day pre-conditioning. Moreover, variable amounts of fly ash (FA) or granulated blast furnace slag (GBFS) were utilized to partly substitute MPC to enhance the compressive strength and chloride ion penetration resistance. Subsequently, FA–MPC and GBFS–MPC hybrid slurries with the best comprehensive performance were selected to coat the RCA for optimal reinforcement. The crushing value and water absorption of RCA, as well as the mechanical strengths and durability of RAC, were investigated, and microstructures around interfaces were studied via BSE-EDS and microhardness analysis to reveal the strengthening mechanism. The results indicated that the comprehensive property of strengthening paste was enhanced significantly through substituting MPC with 10% FA or GBFS. Surface coating resulted in a maximum reduction of 8.15% in the crushing value, while the water absorption barely changed. In addition, modified RAC outperformed untreated RAC regarding compressive strength, splitting tensile strength, and chloride ion penetration resistance with maximum optimization efficiencies of 31.58%, 49.75%, and 43.11%, respectively. It was also evidenced that the improved MPC paste properties enhanced the performance of modified RAC. Microanalysis revealed that MPC pastes exhibited an excellent bond with RCA or new mortar, and the newly formed interfacial transition zone between MPC and the fresh mortar exhibited a dense microstructure and outstanding micro-mechanical properties supported with an increase in the average microhardness value of 30.2–33.4%. Therefore, MPC pastes incorporating an appropriate mineral admixture have enormous potential to be utilized as effective RCA surface treatment materials and improve the operability of RCA application in practice.

The pore-throat characteristics significantly affect the consolidated properties, such as the mechanical and permeability-related performance of the cementitious composites. By virtue of the nucleation and pore-infilling effects, graphene oxide (GO) has been proven as a great additive in reinforcing cement-based materials. However, the quantitative characterization reports of GO on the pore-throat connection are limited. This study applied advanced metal intrusion and backscattered electron (BSE) microscopy scanning technology to investigate the pore-throat connection characteristics of the cement waste rock backfill (CWRB) specimens before and after GO modification. The results show that the microscopic pore structure of CWRB is significantly improved by the GO nanosheets, manifested by a decrease in the total porosity up to 31.2%. With the assistance of the GO, the transfer among internal pores is from large equivalent pore size distribution to small equivalent pore size distribution. The fitting relationship between strength enhancement and pore reinforcement efficiency under different pore-throat characteristics reveals that the 1.70 μm pore-throat owns the highest correlation in the CWRB specimens, implying apply GO nanosheets to optimizing the pore-throat under this interval is most efficient. Overall, this research broadens our understanding of the pore-throat connection characteristics of CWRB and stimulates the potential application of GO in enhancing the mechanical properties and microstructure of CWRB.

The existence of cracks is a key factor affecting the strength of concrete. However, traditional numerical methods still have some limitations in the simulation of crack growth in fissured concrete structures. Based on this background, the numerical treatment method of particle failure in smoothed particle hydrodynamics (SPH) is proposed, and the generation method for concrete meso-structures under the smoothed particle hydrodynamics (SPH) framework is developed. The concrete meso-models under different pre-existing micro-fissure inclinations and bridge angles (the inner tip line of the double pre-existing micro-fissure is defined as a bridge, and the angle between the bridge and the horizontal direction is defined as the bridge angle) were established, and numerical simulations of the crack propagation processes of concrete structures under tensile stress were carried out. The main findings were as follows: The concrete meso-structures and the pre-existing micro-fissures all have great impacts on the final failure modes of concrete. The stress–strain curve of the concrete model presents four typical stages. Finally, the crack initiation and propagation mechanisms of fissured concrete are discussed, and the application of smoothed particle hydrodynamics (SPH) in crack simulations of fissured concrete is prospected.

A thorough understanding of the dispersion characteristics of graphene oxide (GO), its micro-pore enhancement mechanisms, and correlations with mechanical properties are crucial for advancing high-strength, durable green concrete. Introducing recycled concrete powder (RCP) can weaken the interfacial transition zone (ITZ) and inhibit hydration reactions, degrading the pore structure and affecting mechanical strength and durability. However, traditional methods struggle to accurately characterize and quantitatively analyze GO-modified pore structures due to their nanoscale size, microstructural diversity, and characterization technique limitations. To address these challenges, this study integrates deep learning-based backscattered electron image analysis with deep Taylor decomposition feature extraction. This innovative method systematically analyzes pore characteristic evolution and the correlation between porosity and mechanical strength. The results indicate that GO promotes Calcium Silicate Hydrate gel growth, refines pores, and reduces pore connectivity, decreasing the maximum pore size by 33.4–45.2%. Using a Convolutional Neural Network architecture, BSE images are efficiently processed and analyzed, achieving an average recognition accuracy of 94.3–96.9%. The optimized degree of GO coating on enhanced regions reaches 30.2%. Fitting porosity with mechanical strength and chloride ion permeability coefficients reveals that enhanced regions exhibit the highest correlation with mechanical strength and durability in regenerated cementitious materials, with R² values ranging from 0.79 to 0.99. The deep learning-assisted pore structure characterization method demonstrates high accuracy and efficiency, providing a critical theoretical basis and data support for performance optimization and engineering applications of recycled cementitious materials. This research expands the application of deep learning in building materials and offers new insights into the relationship between the microstructural and macroscopic properties of recycled cementitious materials.