Ground penetrating radar (GPR) is a mature and important research method in the field of structural non-destructive testing. However, when the detection target scale is small and the amount of data collected is limited, it poses a serious challenge for this research method. In order to verify the applicability of typical one-dimensional radar signals combined with convolutional neural networks (CNN) in the non-destructive testing of concrete structures, this study created concrete specimens with embedded defects (voids, non-dense solids, and cracks) commonly found in concrete structures in a laboratory setting. High-frequency GPR equipment is used for data acquisition, A-scan data corresponding to different defects is extracted as a training set, and appropriate labeling is carried out. The extracted original radar signals were taken as the input of the CNN model. At the same time, in order to improve the sensitivity of the CNN models to specific damage types, the spectrums of A-scan are also used as part of the training datasets of the CNN models. In this paper, two CNN models with different dimensions are used to train the datasets and evaluate the classification results; one is the traditional one-dimensional CNN model, and the other is the classical two-dimensional CNN architecture AlexNet. In addition, the finite difference time domain (FDTD) model of three-dimensional complex media is established by gprMax, and the propagation characteristics of GPR in concrete media are simulated. The results of applying this method to both simulated and experimental data show that combining the A-scan data of ground penetrating radar and their spectrums as input with the CNN model can effectively identify different types of damage and defects inside the concrete structure. Compared with the one-dimensional CNN model, AlexNet has obvious advantages in extracting complex signal features and processing high-dimensional data. The feasibility of this method in the research field of damage detection of concrete structures has been verified.

Researchers have already used vibration data and deep learning methods, such as Convolutional Neural Networks (CNNs), to detect structural damage. Moreover, some researchers have employed image-based displacement sensors (such as the template matching and edge detection methods) to obtain structural vibration information. It is necessary to verify whether deep learning methods can detect minor damage inside beams, for example, small hollowing in concrete. In addition, there is an urgent need to develop an effective image-based displacement sensor that can simultaneously detect a large number of reliable vibration data from different measurement points. In this study, the vibration data of two beam-ABAQUS models were used as the input data for a newly designed deep learning-based structural health monitoring method. There were 500 vibration samples for each case, and the peak of vibrations was several millimeters. The proposed CNN model can locate damage positions in beams with high accuracy (close to 100%), and the damage sizes are 3 cm and 6 cm. Laboratory experiments were carried out on four beams with different damage. The optimized displacement sensor developed based on the edge detection method was used to detect the displacement of the beams. Each beam had 200 vibration data, and there were 800 vibration data in total. These vibration data were used as input data to train the proposed deep learning architecture, and satisfactory accuracy was achieved in detecting the damage of the beams with an accuracy of 97%. The training process is satisfactory in that the training loss and validation loss dropped very quickly.

Traditional structural damage detection relies on multi-sensor arrays (e.g., total stations, accelerometers, and GNSS). However, these sensors have some inherent limitations such as high cost, limited accuracy, and environmental sensitivity. Advances in computer vision technology have driven the research on vision-based structural vibration analysis and damage identification. In this study, an optimized Lucas–Kanade optical flow algorithm is proposed, and it integrates feature point trajectory analysis with an adaptive thresholding mechanism, and improves the accuracy of the measurements through an innovative error vector filtering strategy. Comprehensive experimental validation demonstrates the performance of the algorithm in a variety of test scenarios. The method tracked MTS vibrations with 97% accuracy in a laboratory environment, and the robustness of the environment was confirmed by successful noise reduction using a dedicated noise-suppression algorithm under camera-induced interference conditions. UAV field tests show that it effectively compensates for UAV-induced motion artifacts and maintains over 90% measurement accuracy in both indoor and outdoor environments. Comparative analyses show that the proposed UAV-based method has significantly improved accuracy compared to the traditional optical flow method, providing a highly robust visual monitoring solution for structural durability assessment in complex environments.

Reliable crack detection is essential for ensuring the safety, serviceability, and long-term performance of civil structures. Conventional manual inspections are labor-intensive and subjective, while existing computer vision models often exhibit reduced accuracy under variable field conditions. This study develops a computer vision-based automated crack detection framework utilizing a hybrid Transformer–CNN architecture to support infrastructure inspection and condition assessment. The proposed model leverages the global context modeling capability of Transformers and the local feature sensitivity of convolutional neural networks (CNNs) to enhance detection robustness. The optimized hybrid model achieved an Intersection over Union (IoU) of 91.8% and an accuracy of 98.7%, outperforming baseline CNN, Transformer-only, and LSTM architectures. Field validation on bridge inspection imagery demonstrated strong resilience to variations in illumination and texture. The developed approach contributes to digital inspection and intelligent lifecycle management of infrastructure assets by enabling reliable, automated, and non-intrusive structural condition evaluation under realistic field conditions.