Junyin Lian

This study employed response surface methodology (RSM) to optimize admixture proportions in 3D-printed cementitious materials, with the aim of enhancing printability. Based on preliminary tests, three additives, namely, an accelerator, hydroxypropyl methylcellulose (HPMC), and polycarboxylate superplasticizer (PCE), were incorporated to evaluate their effects on flowability and dynamic yield stress. A Box–Behnken central composite design was used to establish a mathematical model, followed by the RSM-driven optimization of mix proportions. The optimized formulation (0.32% accelerator, 0.24% HPMC, and 0.23% PCE) achieved a flowability of 147.5 mm and a dynamic yield stress of 711 Pa, which closely matched the predicted values and fulfilled the printability requirements, thus establishing RSM as an effective approach for designing printable cementitious composites. This approach established an RSM-based optimization framework for mix proportion design. These findings offer a mechanistic framework for rational 3DPC mixture design, combining theoretical insights and practical implementation in additive construction.

This study presents a comprehensive method for detecting debonding defects in concrete-filled steel tube (CFST) structures using wave propagation analysis with externally attached piezoelectric ceramic sensors. Experimental tests and numerical simulations were conducted to evaluate the sensitivity and accuracy of two measurement techniques—the flat and oblique measurement methods—in detecting debonding defects of varying lengths and heights. The results demonstrate that the flat measurement method excels in detecting debonding height, while the oblique method is more effective for detecting debonding length. A normalized judgment index (DI) was introduced to quantify the correlation between debonding characteristics and the detected signal amplitude, revealing the significant influence of sensor spacing on detection accuracy. Furthermore, a mathematical model based on wavelet packet energy analysis was developed to establish a linear relationship between wavelet packet energy and debonding size. This model offers a scientific foundation for the quantitative detection of debonding defects and provides a new approach to the health monitoring of CFST structures. The integrated use of both measurement techniques enhances detection precision, enabling both qualitative and quantitative defect analysis, which can significantly guide the maintenance and repair of CFST structures.