Xuelian Deng

Recycled brick aggregate (RBA) and old mortar (OM) account for a large proportion of construction and demolition waste. To date, few systematic studies have addressed how their coupling interaction governs the performance of recycled concrete (RC). In this study, a two-factor experimental design was carried out, where RBA content (0–15%) and OM content (0–15%) were set as independent variables. The physical properties of recycled aggregates (RAs) and mechanical behaviors of RC were measured, and the underlying mechanisms were elaborated from both macroscopic and microscopic perspectives. Experimental results reveal that increasing RBA and OM contents raise the water absorption and crushing value of RA while reducing their apparent density. OM exerts a more substantial impact on water absorption and apparent density, whereas RBA predominantly controls the fluctuation of crushing value. When RBA dosage rises from 0% to 15%, the maximum reductions in compressive strength and flexural strength reach 20.7% and 23.4%, respectively. When OM dosage increases from 5% to 15%, the corresponding strength reductions are 24.8% and 20.3%. Scanning electron microscopy (SEM) characterizations prove that internal pores and microcracks within RBA, along with loose zones at the interface between OM and fresh mortar, act as vulnerable paths for crack initiation and propagation, which deteriorates the bulk macroscopic mechanical properties. Range analysis and analysis of variance confirm that RBA serves as the dominant influential factor. The contribution proportions of RBA to compressive strength and flexural strength are 68.3% and 72.5%, which exceed those of OM (31.7% and 27.5%). The combined action of RBA and OM produces an evident negative superposition effect, with no detectable performance compensation effect observed. This research lays a solid foundation for optimizing the mixing proportion of RA derived from construction waste and promoting the engineering application of RC.

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.