Automated Evaluation of Layer Thickness Uniformity in 3D-Printed Cementitious Composites Using Deep Learning and Comparison with Manual Tracing Methods

Layer thickness uniformity critically influences the dimensional accuracy and mechanical performance of large-scale cementitious structures produced by material extrusion 3D printing. This study introduces a computer vision workflow that couples traditional preprocessing with a ResNet-50 convolutional neural network to automatically detect interlayer boundaries and quantify thickness variation. Hollow 50 × 50 × 50 mm specimens, printed from mixes optimized by void ratio (0.6–0.7) for fluidity and stackability, supplied 25 labeled RGB images for training and validation. The network achieved 96% training and 95% validation accuracy, generating boundary maps that required minimal linear interpolation. Pixel-based analysis yielded uniformity indices of 0.857–0.924, closely matching those from manual tracing (0.819–0.919) but with smaller standard deviations, indicating higher measurement stability and reduced sensitivity to lighting artifacts. The proposed method therefore provides an objective, reproducible alternative to labor-intensive manual evaluation and supports real-time prediction and control of dimensional errors during construction-scale 3D printing, advancing the precision and industrial applicability of additive manufacturing with cementitious composites. However, since this study was conducted under limited variable conditions, such as a simplified and repetitive experimental environment, a larger number of images will be required for model training to enable application under more general conditions.