Signal processing and analysis of structural vibration measurements are key components of structural damage detection (SDD) in structural health monitoring (SHM). The goal of signal processing is to extract subtle changes in the measured signals, which can be used to infer changes in structural parameters and damage. Time-frequency analysis is one of the most popular characterization methods for studying non-stationary vibration signals. In this article, the local time-frequency transform (LTFT) is applied and evaluated to calculate the time-domain signals because of its excellent time-frequency energy distribution properties. The LTFT matches the input data by the Fourier basis in an inverse problem framework and uses the least squares method to solve the time-varying Fourier coefficients. Subsequently, it defines the time-frequency spectrum as the calculated time-varying Fourier coefficients. While the LTFT has been used in the field of geophysics for seismic data processing, its application to structural vibration signals is novel. Both synthetic signals as well as signals collected from a large-scale laboratory test of a reinforced concrete girder were processed with the LTFT and compared with Rényi entropy for quantifying the time-frequency spectrum, the time-frequency resolution abilities of short time Fourier transform (STFT), and S transform (ST). The results show that the LTFT is superior to the traditional time-frequency analysis schemes, in that it is more effective in identifying the energy changes in the time-frequency spectrum before and after structural damage in the form of cracking has occurred. At the same time, it provides high-precision time-frequency resolution and excellent noise suppression abilities. The effectiveness and feasibility of the LTFT applied to the synthetic and experimental signals are verified.

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.

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.

Concrete materials are an important part of global structure, and their fire resistance directly affects the safety of buildings and tunnels. In this study, basalt fiber was used to reinforce concrete with high content of stone powder in order to enhance its high‐temperature performance. The mechanical properties and ultrasonic characteristics at different temperatures were studied using the cube compressive strength test and nonlinear ultrasonic test. The results indicated that the addition of basalt fiber in specimens improved their compressive strength; however, this strength did not continuously increase with increases in the fiber length and fiber content, and the optimal values for fiber length and fiber content were determined to be 12 mm and 1 kg/m³ at 600°C, respectively. With increases in temperature, the unconfined compressive strength increased first and then decreased. When the temperature was 400°C, the unconfined compressive strength of the specimens reached their highest values and then decreased. When the temperature was 400°C and 600°C, the strength of the stone powder concrete with fiber was higher than that without fiber, which shows that fiber can improve the mechanical properties of concrete at high temperatures. Based on the Box‐Behnken design (BBD) method, the unconfined compressive strength response regression model of basalt fiber‐reinforced concrete with high content of stone powder, which follows parameters including fiber content, fiber length, and temperature at high‐temperature environments, was established, and it was found that the interaction of fiber content, fiber length, and the temperature was significant based on multifactor interaction analysis. The analysis of ultrasonic signals based on the S transform showed that, with increases in temperature, the amplitudes of the acoustic response signals, the corresponding frequency spectrum, and the time‐frequency spectrum were clearly reduced. At the same temperature, the amplitudes of the acoustic response signals of different concrete testing blocks did not change much and remained at the same level.

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.