Sulfate attack is one of the crucial causes for the structural performance degradation of reinforced concrete infrastructures. Herein, a comprehensive multiphase mesoscopic numerical model is proposed to systematically study the chemical reaction-diffusion-mechanical mechanism of concrete under sulfate attack. Unlike existing models, the leaching of solid-phase calcium and the dissolution of solid-phase aluminate are modeled simultaneously in the developed model by introducing dissolution equilibrium equations. Additionally, a calibrated time-dependent model of sulfate concentration is suggested as the boundary condition. The reliability of the proposed model is verified by the third-party experiments from multiple perspectives. Further investigations reveal that the sulfate attack ability is underestimated if the solid-phase calcium leaching is ignored, and the concrete expansion rate is overestimated if the dissolution of solid-phase aluminate is not modeled in the simulation. More importantly, the sulfate attack ability and the concrete expansion rate is overestimated if the time-dependent boundary of sulfate concentration is not taken into consideration. Besides, the sulfate ion diffusion trajectories validate the promoting effect of interface transition zone on the sulfate ion diffusion. The research of this paper provides a theoretical support for the durability design of concrete under sulfate attack.

The microscopic pore structure of sandstone determines its macroscopic permeability. Based on computer tomography (CT) technology, CT scans were performed on three different types of sandstone pore structures, namely coarse sandstone, medium sandstone, and fine sandstone. And the three-dimensional microscopic structure of sandstone pores was reconstructed. Furthermore, based on the Navier–Stokes equations, the fluid flow process in the pore structure of sandstone was simulated, and the effective permeability of sandstone was obtained. By extracting the pore structure from sandstone CT images, the average porosity of coarse sandstone, medium sandstone, and fine sandstone was 16.43%, 12.03%, and 11.64%, respectively. And the porosity of unconnected pores is less than 0.5%. The porosity and permeability of coarse sandstone are higher than those of medium sandstone and fine sandstone with an average value of 1.7 D. The porosity of medium sandstone and fine sandstone is relatively similar. However, the average pore radius and pore throat radius of medium sandstone are larger than those of fine sandstone. More importantly, although the permeability and porosity of sandstone are generally linearly related, when the porosity is low, the data show a large dispersion, and auxiliary indicators such as pore structure characteristic parameters such as pore throat radius should be adopted to evaluate the permeability of sandstone. The flow trajectory of fluid in the pore structure of sandstone is revealed through the streamline of fluid in the pore structure, revealing the mechanism of fluid flow.

Concrete is a multiphase composite material composed of coarse aggregate, cement mortar, and interface transition zone (ITZ). It is of great significance to study the effect of ITZ and coarse aggregate on chloride microscopic diffusion behavior for predicting the service life of reinforced concrete (RC) structures. By introducing the random distribution function, a random coarse aggregate model considering the randomness of the thickness of the ITZ was established. Furthermore, a two-dimensional (2D) chloride ion diffusion mesoscopic model was developed by specifying different diffusion properties for different phase materials of concrete. Moreover, the effects of coarse aggregate rate, ITZ thickness, and ITZ diffusion property on chloride ion diffusion behavior were investigated in this paper. The research showed that the aggregate has hindrance and agglomeration action on chloride ion diffusion. Although the volume content of the ITZ was very small, less than 0.2% of the total volume of concrete, the effect of the ITZ on the chloride diffusion in concrete cannot be ignored. More importantly, the mechanism of promoting chloride diffusion in the ITZ was revealed through the chloride diffusion trajectory. The research revealed the transmission mechanism of chloride ions in the meso-structure of concrete and provides theoretical support for the design of RC structures in coastal areas.

Sleeve grouting compactness has a significant effect on the mechanical properties of rebar connections. However, a detection method for the grouting compactness inside the sleeve is still lacking. Therefore, the aim of this paper is to propose a new acoustic emission (AE) detection technology for horizontal defects and vertical defects in sleeves with different grout compactness. The basic waveform characteristic of the AE signal is analyzed. The results show that the count of acoustic emission signals decreases with the increase of grouting compactness, and the reduction rate of vertical defects is larger than that of horizontal defects. The acoustic emission waveform is further processed through wavelet packet decomposition. It is found that with the increase of grouting compactness, the composition of approximately 125–187.5 kHz in the signal is accelerated to approximately 62.5–125 kHz. The grouting compactness index is constructed by wavelet packet energy ratio. With the increase of grouting compactness, the compactness index decreases exponentially, indicating that the presence of defects can greatly reduce the attenuation of elastic wave energy. The compactness index is highly consistent with the size of defects and has little relationship with the distribution of grout materials. Experiments show that the proposed method is effective when grout defects reach a certain degree and provides a new method for sleeve grouting compactness detection.

The heterogeneity of concrete is a major challenge for acoustic emission monitoring. A method of active-passive joint acoustic emission monitoring considering the heterogeneity of concrete is presented herein, and the time-frequency-space multi-parameter response characteristics of active and passive acoustic emission signals were studied in relation to the damage evolution of concrete. This method provides an idea of evaluating the damage state of concrete more actively and quantitatively than traditional methods. The results show that the microscopic damage model of concrete based on the acoustic emission penetrating wave velocity and amplitude is in agreement with the damage process of concrete. The standard deviation of the wave velocity up to 1000 m/s and the change rate of the amplitude up to −0.66 can be adopted as two signs that the load of concrete reached 70% of the ultimate load. The time-of-arrival localization based on variable velocity was used to correct the acoustic emission localization results, and the localization accuracy was increased by 44.74%. The damage process of concrete undergoes diverse changes; that is, the distribution of damage changes from heterogeneous to homogeneous and then back to heterogeneous. Hence, it is necessary for researchers to consider the heterogeneity of concrete when using acoustic emission monitoring. The active-passive joint acoustic emission monitoring is an effective method.

The presence of free water in the concrete slurry significantly influences the crack patterns of concrete. In this study, uniaxial compression tests were conducted on concrete specimens with varying moisture contents under acoustic emission (AE) monitoring. Through parametric analysis and machine learning, the cracking process of water‐containing concrete was studied, signal patterns during the cracking process were identified, and the impact of moisture content on the damage evolution and fracture mechanism of concrete was understood. The results indicate that free water is capable of absorbing high‐frequency signals. With the increase of moisture content, the AE signals decrease. The failure of concrete is mainly of the tensile type, while the shear‐type accounts for a relatively small proportion. The presence of free water decreases the likelihood of diagonal shear failure in concrete structures. The unsupervised learning was used for various moisture content analyses. Three distinct AE signal patterns were identified during the concrete compression tests: frictional motion signals of the compression surface, fracture surface activity signals, and aggregate cracking signals. Based on the moisture content, this study analyzes the variations in signal responses across different modes. A predictive model was established utilizing the BP neural network to differentiate signals of various modes, achieving an accuracy rate of 99%.