Optimization Design and Regression Model Analysis of Mechanical Properties of 3D Printed Concrete (2025-05)¶

To investigate the effects of the basalt fiber volume ratio, fiber length, fly ash content, and water reducer dosage on the mechanical properties of 3D printed concrete (3DPC), a response surface regression model was constructed and optimized using a design of experiments with a desirability function. The measured values of compressive strength, flexural strength, and interlayer shear strength of 3DPC were compared with the predicted values from the model, validating the effectiveness and reliability of the response surface regression model. The research results indicate that with a volume fraction of basalt fibers at 1%, a fiber length of 6 mm, a fly ash content of 29%, and a water reducer content of 0.29%, the mechanical properties of the 3DPC are closest to the expected values. Scanning electron microscopy (SEM) analysis reveals that the optimized concrete possesses a more dense internal microstructure, significantly enhancing its mechanical performance. Thus, applying the response surface regression model and the desirability function to the optimization of the mechanical properties of 3DPC offers a novel research approach and methodology for this field, possessing significant application reference value. Future research should investigate the comprehensive effects of environmental conditions and new materials on the mechanical properties of 3DPC, and validate the feasibility of optimization strategies in real-world engineering to foster further development and application of 3DPC technology.