During long-term service, concrete structures are exposed to various adverse factors, which often lead to the formation of numerous surface cracks. These cracks pose serious threats to structural safety and durability. Therefore, accurately identifying crack characteristics is essential for evaluating the service performance of concrete structures. A two-stage concrete crack segmentation method is presented in this study. The crack is initially located by the improved YOLOv11 that integrates three novel modules, namely Multi-scale Edge Information Enhancement, Efficient-Detection, and P2-Level Feature Integration, to form the MEP-YOLOv11 model. Then, the detected region is taken as input prompts for Segment Anything Model (SAM) to achieve precise crack segmentation. This approach eliminates the need for manual prompting in SAM, enabling automatic crack feature identification. The average Accuracy, precision, and Intersection over Union (IoU) for crack segmentation are 95.98%, 92.60%, and 0.77, respectively. To further enhance the robustness of the two-stage segmentation method under non-uniform illumination conditions, a mask re-input strategy is introduced. The crack mask generated by SAM using bounding-box prompts is fed back into SAM to guide a second round of segmentation. Experimental results demonstrate that the improved method maintains high segmentation performance, with an average Accuracy of 92.38%, precision of 85.70%, and IoU of 0.64. Overall, the proposed method meets engineering requirements for high-precision and efficient crack detection and segmentation, showing strong potential for practical inspection tasks.

Bolt connections are the primary component of beam–column joints, which frequently become loose during their service life due to environmental factors. Assessing the tightness of bolts is essential for maintaining structural integrity and safety. Although the guided wave method has been proven effective for detecting bolt looseness, the severe dispersion properties and complex structure of beam–column joints pose difficulties for the quantitative evaluation of bolt looseness. Therefore, a deep neural network model integrating a convolutional neural network (CNN), long short-term memory (LSTM), and multi-head self-attention mechanism (MHSA) is introduced to identify the degree of looseness in multiple bolts using SH-typed guided waves. The dispersion properties of the I-shaped steel beam were analyzed using the semi-analytical finite element method, and a mode weight coefficient was presented to clarify the mode distribution under different types of external loads. Two pairs of transducers arranged on the same side of the bolt-connected region were utilized to obtain the directly incoming and end-reflected wave packets from four wave propagation paths. The received signals were converted into time–frequency spectra, and the effective components were extracted to form the input pattern for the neural network. Numerical simulations were performed on a beam–column joint with eight bolts, and the number of training samples was increased using data augmentation techniques. The results indicate that the CNN-LSTM-MHSA model can accurately estimate the bolt looseness conditions better than other methods. Noise injection testing was also conducted to investigate the effect of measurement noise.