Xianqiang Wang

To identify the development stages of load-induced cracks in reinforced concrete beams under freeze–thaw and corrosion degradation, this paper proposes a mechanism-guided multi-branch neural network approach. Based on the physical meanings of acoustic emission parameters, the input features are divided into the following four groups: crack evolution process, damage intensity characterization, crack pattern characterization, and structural degradation state. Independent branches are employed for feature extraction, and hierarchical fusion is used to achieve collaborative representation of multidimensional damage information. Additionally, the Coyote Optimization Algorithm (COA) is introduced to optimize the initial parameters of key network layers. A total of 5789 valid acoustic emission event samples were obtained from loading tests, covering four degradation levels and four damage stages. Repeated experiments (20 trials) demonstrated that the proposed COA-MB-MLP model achieved an accuracy of 91.42% and a Macro-F1 score of 0.9118 on the test set, outperforming traditional benchmark models such as KNN, LDA, SVM, and RF, with statistically significant performance improvements. Ablation experiments revealed that the mechanism-guided multi-branch structure is the primary source of performance enhancement; COA initialization provides modest improvements in average test performance and reduces variability across repeated experiments. Feature response and confusion sample analysis indicated that amplitude is more sensitive to damage stage evolution, while features like counts, energy, and duration exhibit significant increases in the failure stage. Confusion between adjacent stages primarily stems from crack evolution continuity and ambiguous phase boundaries. The findings demonstrate that the proposed method effectively integrates multidimensional physical information from acoustic emission signals, providing a valuable reference for identifying crack development stages in freeze–thaw and corrosion-damaged reinforced concrete beams.

Raw mix-design variables used to predict ultra-high-performance concrete (UHPC) properties cannot fully represent internal proportions and structural compatibility. This study develops mechanism-informed proxy-core features based on particle packing, water film thickness, and rheology, and evaluates four feature systems using literature-derived datasets for compressive strength (924 samples), flexural strength (406 samples), and slump flow (192 samples). Eight regression models were compared using five-fold cross-validation, Bayesian optimization, SHAP, and ablation analysis. Proxy-W achieved an R² of 0.918 for compressive strength, only 0.005 higher than baseline-W on the original split. For flexural strength, baseline-WB achieved the highest R² (0.906), whereas proxy-W yielded the lowest MAE (2.140 MPa). The largest single-split difference occurred for slump flow (R²: 0.740 to 0.838 for W-based systems), but 30 repeated splits yielded mean R² values of 0.667 ± 0.169 and 0.738 ± 0.101 for baseline-W and proxy-W, respectively, while proxy-WB showed no average improvement over baseline-WB. Repeated and source-group validation further indicated that the predictive effects of proxy-core features were property-, representation-, partition-, and source-dependent. Overall, proxy-core features are best interpreted as physically informed relational representations rather than universally accuracy-enhancing features.

Acoustic emission (AE) signals associated with different damage processes in reinforced concrete (RC) beams often show overlapping feature distributions, making unsupervised identification difficult. In this study, three RC beams were tested under loading-induced damage, freeze–thaw damage and reinforcement corrosion conditions, and 490 valid AE samples were obtained. Seven AE parameters were selected to construct the clustering feature set. To enhance the separation of different damage-related AE signals, a kernel K-means clustering framework was adopted, and the coyote optimization algorithm was used to optimize the kernel function type and key parameters based on the Gap Statistic. Comparative analysis with K-means, FCM and GMM was also conducted. The results show that the COA-optimized kernel K-means method achieved the best overall clustering performance, increasing the mean ACC by 10.35 percentage points compared with K-means and by 2.49 percentage points compared with GMM. Its mean ARI was also higher than that of GMM by 0.0330, while the standard deviation of ACC decreased from 7.73% to 4.19%. Class-level results indicated that loading-induced AE signals were more readily identified, whereas freeze–thaw and corrosion signals were more affected by feature overlap. Feature interpretation further showed that the main misclassified samples were located in transitional feature regions. The results suggest that COA-based kernel optimization can improve the clustering separation and stability of AE signal identification for different damage types in RC beams.

Acoustic emission (AE) technology is widely used in structural health monitoring. Glass sand (GS) made from waste glass is a promising replacement aggregate for quartz sand (QS) in ultra-high performance concrete (UHPC). This paper addresses the effects of different factors including water-binder ratio, length of basalt fiber (BF) and ratio of GS replacing QS on the fluidity and flexural strength of UHPC notched beam under four-point flexural loads. Meanwhile, the fracture characteristics of UHPC notched beam were characterized through acoustic emission (AE) technique. The results show that water-binder ratio and replacement ratio of GS present a positive correlation with work performance of UHPC, while length of BF exhibits a negative one. The flexural strength of UHPC notched beams can be improved by the decrease of the water-binder ratio and fiber length. The effect of water-binder ratio on flexural strength is the most significant, while the addition of GS presents the minimum one. The fracture characteristics of UHPC notched beams could be favorably characterized by AE parameters. Through the analysis and comparison of the evolution of AE parameters, the differences in fracture properties of UHPC notched beams with different flexural strengths can be realized. Through this study, the fluidity and flexural performance of UHPC produced by replacing QS with GS were demonstrated, which is beneficial to the cleaner production of UHPC. Meanwhile, the AE technique presented great potential for fracture characterization of UHPC notched beam, which also provided a promising method for real-time monitoring of cracking in the diagnosis of UHPC structures.