Monotonic push-out tests were carried out on 11 specimens having high-strength bolt, T-type perforated plate, or slot-type perforated plate connections to investigate the influence of different connection types on the interface performance of glass fiber reinforced polymer (GFRP)–concrete composite beams. The effects of the number of rows and spacing of high-strength bolts on the failure mode, load–slip relationship, and shear capacity were analyzed. The effects of the number and spacing of holes in the perforated plates, and the inclusion of transverse rebar were analyzed. The results show that the failure mode of the bolt specimens is brittle failure and the perforated plate is ductile failure. The single bolt connection has the lowest ultimate bearing capacity, while the single T-shaped and slotted perforated plates are 170% and 270% times greater. The rows and spacing have no difference in bolts. The perforated plate gradually decreased with an increase in rows and gradually increased with an increase in spacing. The transverse rebar can increase the ultimate bearing capacity and ductility in the plastic stage for perforated plate. Accordingly, there are good choices for interface connectors for GFRP–concrete composite beams, while bolt connectors need to be carefully chosen.

Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor‐intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal‐to‐noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system.

Vehicle detection and classification (VDC) is a crucial aspect of bridge engineering, as vehicles exert significant dynamic loads on bridges. Various contact and noncontact methods have been proposed for VDC, but finding a durable and cost-effective sensing approach remains challenging for practical bridge applications. In this study, we propose a voiceprint recognition (VPR) method for VDC using a single microphone, offering flexibility and affordability. The distinctive acoustic signatures generated by vehicles impacting bridge expansion joints (BEJs) are captured and utilized for VDC. We optimize a threshold-based algorithm for vehicle detection and a deep learning-based VPR model for vehicle classification. In addition, cascading VPR models enable fine-grained vehicle classification, including lanes and axle types. We validate the proposed method on an actual bridge. The short-term energy (STE) thresholding algorithm achieves a detection accuracy and recall of 90.0% and 91.6%, respectively. The ConFormer model achieves an area under the curve (AuC) of 0.925 for vehicle classification. These results highlight the method as an easy-to-implement and cost-effective solution for efficient VDC surveys. Future work can focus on expanding datasets and incorporating multiple microphones to further enhance the system’s capabilities.

The complex and diverse structural forms make it impossible to define universal shape coefficients for large-span roof structures, which usually need to be obtained by wind tunnel tests. However, the number of test measurement points is limited, which leads to obvious limitations in the study of wind loads on large-span roof structures. Taking a large-span folded-plate roof as an example, based on the wind tunnel pressure test results of the rigid model, the proper orthogonal decomposition (POD) method is used to reconstruct the wind pressure field of the roof using the first several eigenmodes. The wind pressure of several typical characteristic points is predicted based on four different interpolations methods, and the accuracy and feasibility of POD method in reconstruction and prediction of wind pressure field of large-span roof are analyzed and studied from multiple perspectives. The results show that the order of the selected structural eigenmodes has an impact on the reconstruction accuracy of the wind pressure field. The more orders are selected, the closer the wind pressure field reconstruction is to the true value. The reconstruction effect of the wind pressure field based on the POD method is related to the spatial position of the predicted point, and the reconstruction effect of the wind field based on the fluctuating wind pressure is obviously better than the that based on the mean wind pressure. When the POD method is used to predict the wind pressure of an unknown point, different interpolation methods can achieve ideal results. Among them, the bilinear interpolation method has the highest prediction accuracy, and the adjacent point interpolation method and Griddata V4 interpolation method only have certain errors in the low frequency region.

A double-column self-centering pier fused with shear links is a novel structure developed to reduce residual deformation and facilitate post-earthquake repair. With this novel structure, the seismic resilience of bridges can be improved, and the reliability of lifeline infrastructure can be ensured. This paper presents the proposed pier configuration and investigates the mechanical behavior of the pier. A simplified finite element model is established to develop the lateral force-displacement relationship under cyclic loading. Additionally, a theoretical model based on the matrix displacement method and the virtual work principle is proposed to calculate the lateral force-displacement skeleton curve. The rationality and reliability of the theoretical model are validated by the satisfactory agreement observed between the numerical and theoretical results. Furthermore, a series of parametric analyses are conducted to discuss the effects of key parameters. The outcomes of this work can serve as a reference for further development of the design method for the innovated pier.