Lap joints are commonly used in civil infrastructure and mechanical assembly structures. Detecting the connection status of bolts, particularly in the early stages of use, is critical for ensuring structural integrity. A shear horizontal (SH) guided wave-based bolt looseness detection method, excited by a magnetostrictive patch transducer (MPT), is presented in this paper. A normalised bolt-looseness index I BL nor was proposed based on the wave energy transmission theory by analysing the signals received at the transducer for the transmitted wave passing through the actual contact area and the transducer for the direct incoming wave. Numerical and experimental studies were conducted on a lap joint with four bolts. The relationship between the I BL nor and bolt loads was obtained, verifying the effectiveness of this method for detecting the extent of the bolt looseness under various bolt tightness states. The results also show that SH0 waves, excited by an MPT, have better detection capability than Lamb waves, when the surface of the lap joint is subjected to liquid medium such as water and professional paint for bridges.

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.