The displacement response of structures is an important parameter in structural health monitoring (SHM). Displacement responses can be applied in both structural performance monitoring and structural dynamic characteristics monitoring. To overcome the shortcomings of traditional contact sensors, a vision-based multi-point structural displacement measurement system equipped with an inexpensive surveillance camera and a consumer camera was developed herein. In addition, to reduce the computing time of target tracking, an improved region-matching algorithm based on the prior knowledge of structural deformation was proposed. Numerical results revealed that the improved region-matching algorithm could save computing time without reducing location accuracy. Moreover, static and dynamic loading tests were conducted on a scale model of a suspension bridge to validate the effectiveness of the proposed vision-based measurement system. Displacement responses and modal parameters obtained from the vision-based measurement system were compared with those of traditional contact sensors, and a satisfactory consistency was obtained. Hence, the proposed vision-based measurement system could be a cost-effective alternative to conventional displacement sensors and accelerometers for SHM.

Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

Application of ultra-high-performance concrete (UHPC) in joints can improve the impact resistance, crack resistance, and durability of structures. In this paper, the direct shear performance of ultra-high-performance concrete (UHPC) adhesive joints was experimentally studied. Twenty-four direct shear loading tests of UHPC adhesive joints were carried out considering different interface types and constraint states. The failure modes and load-slip curves of different interfaces were studied. Results indicated that passive confinement could enhance the strength and ductility of the interface; the average ultimate bearing capacity of the smooth, rough, grooved, and keyway specimens with passive restraint were, respectively, increased by 11.92%, 8.91%, 11.93%, and 17.766% compared with the unrestrained ones. The passive constraint force changes with the loading and finally tends to be stable. The epoxy adhesive has high reliability as a coating for the UHPC interface. The adhesive layer is not cracked before the failure of the specimen, which is also different from the common failure mode of adhesive joints. Failure of all specimens occurred in the UHPC layer, and the convex part of the groove interface shows the UHPC matrix peeling failure; the keyway interface is the shear damage of the key-tooth root, and the rest of the keyway showed UHPC surface peeling failure. According to the failure mode, the shear capacity of UHPC keyway adhesive joints under passive restraint is mainly provided by the shear resistance of key teeth, the friction force of the joint surface, and the bonding force of the UHPC surface. The friction coefficient was determined based on the test results, and the high-precision fitting formula between the shear strength of the UHPC surface and the passive constraint force was established. According to the Mohr stress circle theory, the proposed formula for direct shear strength of UHPC bonded joints under passive constraint was established. The average ratio of the proposed UHPC adhesive joint calculation formula to the test results was 0.99, and the standard deviation was 0.027.

In order to efficiently and automatically detect and classify bridge deck diseases, a method using Gaussian curvature to detect bridge deck surface diseases is proposed. When there is a defect in the bridge paving surface, there is a high-order discontinuity in the common boundary between the defect area and the non-defect area, that is, the surface Gaussian curvature value at the common boundary is a discontinuous change. According to the location and extent of Gaussian curvature mutation, the disease was located and quantified, and the disease was classified and screened by the Gaussian curvature value distribution feature and Hu rectangular morphological feature. For a 3D point cloud model of a concrete continuous girder bridge, the distribution of Gaussian curvature values in the paved area of the bridge deck was analyzed. The results show that the distribution of Gaussian curvature can characterize surface diseases such as pavement pits and loose spalling of the bridge, and this method is consistent with the assessment results of disease degree by traditional detection methods, and has high implementability, which provides a new technical approach for bridge deck disease assessment.