A fine-segmentation algorithm for XCT images of multiphase composite building materials based on deep learning

In response to the challenges presented by the variable internal structures and complex components of multiphase composite building materials, this study introduces a novel image segmentation network named UT-FusionNet. Building on the U-Net model, UT-FusionNet integrates Transformer module and tensor concatenation and fusion mechanism to overcome the limitations of convolutional networks in relation to the receptive field. UT-FusionNet is employed to segment CT images of conventional concrete, grout consolidation bodies, and fiber-reinforced concrete. The results demonstrate that UT-FusionNet achieves superior segmentation accuracy and robustness, with Accuracy (ACC), Intersection over Union (IoU) and Dice scores exceeding 90 % across all subtasks. The mean accuracy metrics are 99.44 %, 96.70 %, and 98.31 %, respectively. This innovative end-to-end network offers robust support for detailed structural analysis, damage detection, and digital modeling of multiphase composite building materials through deep learning. •An end-to-end deep learning approach for construction materials image segmentation.•UT-FusionNet integrates Transformer, tensor concatenation & fusion mechanism.•UT-FusionNet overcomes convolutional network limitation related to receptive field.•UT-FusionNet achieves higher segmentation accuracy than U-Net on CT image dataset.•A robust tool for structural analysis, damage detection and digital modeling.