The subject of vibration-based structural health monitoring (SHM) has attracted increasing attention, especially in the field of civil engineering. However, the development of these monitoring processes is not a simple task, with user interaction playing a significant role in the extraction of modal characteristics. In this paper, an automated operational modal analysis methodology based on an eigensystem realization algorithm (ERA) and a two-stage clustering strategy is proposed. Three crucial steps are addressed in this study. In the first phase, ERA is adopted to calculate modes from state-space models of different orders. Subsequently, the dissimilarity of modal parameters is employed as the features of fuzzy C-means (FCM) clustering to separate stable modes from unstable ones. The final step consists of grouping stable modes with similar structural properties to select physical modes. No user-specified parameter is required in the clustering procedure to single out physical modes. A practical bridge example is used to verify that the proposed method can estimate modal parameters effectively in real time.

Ground penetrating radar (GPR) is one of the most recommended tools for routine inspection of tunnel linings. However, the rebars in the reinforced concrete produce a strong shielding effect on the electromagnetic waves, which may hinder the interpretation of GPR data. In this work, we proposed a method to improve the identification of tunnel lining voids by designing a generative adversarial network-based rebar clutter elimination network (RCE-GAN). The designed network has two sets of generators and discriminators, and by introducing the cycle-consistency loss, the network is capable of learning high-level features between unpaired GPR images. In addition, an attention module and a dilation center part were designed in the network to improve the network performance. Validation of the proposed method was conducted on both synthetic and real-world GPR images, collected from the implementation of finite-difference time-domain (FDTD) simulations and a controlled physical model experiment, respectively. The results demonstrate that the proposed method is promising for its lower demand on the training dataset and the improvement in the identification of tunnel lining voids.

Hinge joint damage is the main defect affecting the safety of assembled hollow slab bridges. The timely discovery of hinge joint damage is significant for guaranteeing the structural safety of assembled hollow slab bridges. This paper presents a damage detection approach for hinge joints of hollow slab bridges using mode shapes extracted from moving vehicle responses. A stationary excitation vehicle equipped with a shaker excites a bridge, another vehicle moves along the driving path, and its acceleration is collected to extract the mode shape, which is called the stationary excitation extraction mode (SEEM) method. First, the theoretical analysis model of the proposed method is established to analyze the response of the vehicle–bridge system, and the mode shape is extracted from vehicle acceleration by the Hilbert transform. An algorithm that requires no baseline is adopted to detect damage from the extracted mode shape. Then a procedure for damage detection in field testing is proposed in which the determination of important parameters is explained, including excitation frequency, moving vehicle departure time, and narrowband filtering. Finally, factors that affect the accuracy of the approach are studied by numerical analysis, including road roughness, vehicle speed, damage degree, and damage location. The results demonstrate that the damage detection approach for hinge joints is feasible at low vehicle speeds.

Hinge joint damage is the main defect that affects the safety of assembled hollow slab bridges. There are some studies on vibration-based damage detection for hinge joints. However, the effect of hinge damage on the modal parameters of bridges lacks in-depth analysis. The sensitivities of different order modes to hinge damage are dissimilar. This study determines the orders of modes sensitive to hinge damage in assembled hollow slab bridges, which is instructive for vibration-based hinge damage detection. First, the effect of the degree and location of the damage on the frequencies and curvature mode shapes is theoretically analyzed using a novel equivalent model. The orders of the modes sensitive to hinge damage are determined. Then, the numerical analysis of a bridge is performed. The equivalent model is proven to accurately reflect the dynamic characteristics by comparing it to the entity reference model. Various scenarios are established to quantitatively analyze the influence of joint damage on the partial order frequencies and mode curvatures. The accuracy of the sensitivity analysis is verified. Damage detection based on frequencies and curvature mode shapes sensitive to hinge damage is discussed briefly.