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%.

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.

This study aims to elucidate the deterministic correlation between the microscopic fracture mechanisms and the multi-domain characteristics of acoustic emission in reinforced concrete beams under cyclic loading. Cyclic incremental tests were designed and conducted, with synchronized application of digital image correlation and AE techniques to capture the entire damage evolution process and corresponding signal responses throughout. The findings reveal that the damage stage division based on mechanical responses is consistent with that based on AE responses. Damage accumulation and irreversible processes can be clearly characterized by AE activity, and the systematic decrease in the Felicity ratio quantitatively verifies the irreversible accumulation of damage. Under cyclic loading, different microscopic fracture mechanisms generate AE frequency-domain signatures with statistically significant differences. A damage identification model integrating the Felicity ratio and multi-band energy features was developed, achieving an accuracy of 88.89% in identifying macroscopic damage stages. This research quantitatively confirms the effectiveness of AE characteristics as reliable identifiers of microscopic fracture mechanisms, providing a new basis for advancing structural health monitoring technologies grounded in fracture mechanism recognition.

As the main load-bearing components of engineering structures, regular health assessment of reinforced concrete (RC) columns is crucial for improving the service life and overall performance of the structures. This study focuses on the health detection problem of in-service RC columns. By combining deep learning algorithms and acoustic emission (AE) technology, the AE sources of in-service RC columns are located, and the optimal sensor layout form for the health monitoring of in-service RC columns is determined. The results show that the data cleaning method based on the k-means clustering algorithm and the voting selection concept can significantly improve the data quality. By comparing the localization performance of the Back Propagation (BP), Radial Basis Function (RBF) and Support Vector Regression (SVR) models, it is found that compared with the RBF and SVR models, the MAE of the BP model is reduced by 7.513 mm and 6.326 mm, the RMSE is reduced by 9.225 mm and 8.781 mm, and the R² is increased by 0.059 and 0.056, respectively. The BP model has achieved good results in AE source localization of in-service RC columns. By comparing different sensor layout schemes, it is found that the linear arrangement scheme is more effective for the damage location of shallow concrete matrix, while the hybrid linear-volumetric arrangement scheme is better for the damage location of deep concrete matrix. The hybrid linear-volumetric arrangement scheme can simultaneously detect damage signals from both shallow and deep concrete matrix, which has certain application value for the health monitoring of in-service RC columns.

Partially replacing cement with mine tailings offers a sustainable strategy for solid waste resource utilization. As a cement admixture, the compressive strength of tailings-based cement materials serves as a critical performance indicator. Machine learning (ML) techniques offer high efficiency, cost-effectiveness, and superior predictive accuracy. However, variations in the chemical composition of tailings often introduce uncertainties into model predictions. Consequently, this study developed an integrated approach incorporating chemical composition and activation methods as input parameters. Four optimized ML models were deployed to predict the compressive strength of tailings-based cementitious materials. Multiple metrics were employed to evaluate model performance, which identified the PSO-XGBoost model as the superior predictive architecture. SHAP analysis revealed that mechanical grinding, NaOH concentration, and the proportions of gypsum and tailings were the primary features influencing compressive strength. Experimental validation yielded a low prediction error of 8.7%, confirming the model’s high predictive accuracy. This research establishes a robust framework for predicting the strength of tailings-based cementitious materials, providing a theoretical foundation for solid waste upcycling.