Real-time monitoring of extrudability and buildability in 3D concrete printing based on target detection method

To improve the quality of printed concrete structures, more refined and efficient detection methods are needed for construction monitoring. This paper proposes a target detection model for quantifying the extrudability and buildability of printed concrete. This model combines the squeeze-excitation attention mechanism with the YOLOv8 target detection model, thereby enhancing the target detection capability. The quantification of extrudability is achieved by detecting the number and size of two common defects in the concrete printing process: cracks and notches. The quantification of buildability is achieved by calculating the overall height deviation of concrete printing based on the height of the extrusion height detection box. Within the investigated case, detection results show that the proposed model improves the mean average precision (mAP) by about 0.15 compared to the original YOLOv8 model in the detection of cracks, notches, and extrusion height, reaching 0.94. Most inference times are under 39 milliseconds per image, demonstrating real-time detection capability. For extrudability, detection relative errors for notch widths within 1.5 mm are generally controlled within 10%. For buildability, underprinting and overprinting states can be determined based on the overall height deviation in concrete printing. The proposed method overcomes the problems of low real-time performance and difficulty in quantifying printing status in previous concrete 3D printing.