Review on Quality Monitoring Methods for 3D Printed Concrete

3D printed concrete technology has demonstrated great potential in transforming construction methods, improving efficiency, and reducing environmental impacts. However, the current quality control and identification of 3D printed concrete mainly rely on manual experience and traditional non-real-time measurements, enabling the printed quality to face major challenges. Although an increasing number of studies have investigated automated quality monitoring and defect detection in 3D printed concrete, a dedicated review that systematically synthesizes these methods is still lacking. This paper provides a comprehensive review of automated quality monitoring methods for 3D printed concrete, focusing on current techniques, challenges, and future applications. Optical image processing and machine learning have been successfully used to detect defects in 3D printed concrete, although these methods have limitations in real-time performance, automation, and data quality. Further, deep learning-based methods have shown great potential in improving the accuracy and automation of defect detection, although data annotation, model generalization, large-scale construction projects, and real-time integration still face challenges. Finally, the integration of quality monitoring with building information modeling and further developments in multi-source data fusion, data augmentation, real-time adaptive control, and active quality control are recommended to address current challenges.