With the steadily growing of global transportation market, the traffic load has increased dramatically over the past decades, which may develop into a risk source for existing bridges. The simultaneous presence of heavy trucks that are random in nature governs the serviceability limit for large bridges. This study investigated probabilistic traffic load effects on large bridges under actual heavy traffic load. Initially, critical stochastic traffic loading scenarios were simulated based on millions of traffic monitoring data in a highway bridge in China. A methodology of extrapolating maximum traffic load effects was presented based on the level-crossing theory. The effectiveness of the proposed method was demonstrated by probabilistic deflection investigation of a suspension bridge. Influence of traffic density variation and overloading control on the maximum deflection was investigated as recommendations for designers and managers. The numerical results show that the congested traffic mostly governs the critical traffic load effects on large bridges. Traffic growth results in higher maximum deformations and probabilities of failure of the bridge in its lifetime. Since the critical loading scenario contains multi-types of overloaded trucks, an effective overloading control measure has a remarkable influence on the lifetime maximum deflection. The stochastic traffic model and corresponding computational framework is expected to be developed to more types of bridges.

With the development of computer and image processing technologies, computer vision (CV) has been attracting increasing attention in the field of civil engineering measurement and monitoring. Cables in slender structures have unique challenges for CV‐based vibration measurement methods, such as low pixel proportion and sensitivity to environmental conditions. This study proposes a noncontact vibration measurement method based on a line tracking algorithm (LTA). The robustness and applicability of the proposed method under varying image resolutions, signal‐to‐noise ratios, and cable inclination angles were systematically evaluated through experimental test of a cable specimen. To validate the effectiveness of the proposed method for practical detection applications, a vibration test on a scaled cable‐stayed bridge model was carried out. The numerical result indicates that the LTA provides high reliability and accuracy values of the cable force. The maximum errors of the first‐order self‐vibration frequency and cable force of the scaled cable‐stayed bridge is 0.99% and 2%, respectively. The proposed method maintains strong stability across various conditions, which provides a reference for long‐term structural health monitoring of cable‐stayed bridges.

All Image restoration technology has undergone extensive research. For image degradation, inverse filtering was proposed in the last century. Then Helstrom proposed a Wiener filtering algorithm. In recent years, the convolutional neural network has promoted the further development of image restoration technology. The coarse-to-fine multi-scale residual network Multiscale Deblur Net (MDN) used in this paper is stable in operation, simple in structure, and easy to train. It has a good deblurring ability for motion-blurred images by testing in the GO_PRO dataset.