Increased dynamic impact on bridge piers caused by seismic events, blasts, and vehicular impact have become increasingly common. Recent research eforts indicate that code provisions for designing reinforced concrete members to withstand such dynamic loads are inadequate and need additional insights for this purpose. Numerous works have been undertaken to investigate reinforced concrete (RC) traditional bridge pier performance on high strain rate loading. However, little attention has been given to evaluate the performance of connections used in present day bridges including accelerated bridge constructions (ABC) to withstand vehicle impacts, and hence, is relatively unknown. In this study, the use of grouted couplers to contain the unbalanced moments resulting from vehicular impact forces exceeding the moment capacity of the reinforced concrete piers and avoiding extensive damage to the piers is investigated. A representative column, typical of those specifed by state departments of transportation, is studied to determine the performance. The performance of the coupler is investigated for both dynamic and static combined stresses. Quasi-static to dynamic strain rates of steel reinforcement connected to the couplers is also evaluated. Quantifying the stresses and strains developed at coupler region from dynamic impact can help coupler manufacturers to optimize the strength properties, thus improving serviceability. This study investigated utilizing splice sleeves in mitigating the formation of plastic hinges, as well as addressing the essential properties of coupler sections required to adequately carry out this function, and will provide a useful design tool for the manufacturers, forensic structural engineers, and practitioners.
This paper presents the behavior of a 102-year-old truss bridge under wind loading. To examine the wind-related responses of the historical bridge, state-of-the-art and traditional modeling methodologies are employed: a machine learning approach called random forest and three-dimensional finite element analysis. Upon training and validating these modeling methods using experimental data collected from the field, member-level forces and stresses are predicted in tandem with wind speeds inferred by Weibull distributions. The intensities of the in-situ wind are dominated by the location of sampling, and the degree of partial fixities at the supports of the truss system is found to be insignificant. Compared with quadrantal pressure distributions, uniform pressure distributions better represent the characteristics of wind-induced loadings. The magnitude of stress in the truss members is enveloped by the stress range in line with the occurrence probabilities of the characterized wind speed between 40% and 60%. The uneven wind distributions cause asymmetric displacements at the supports.
Along with the advancement in sensing and communication technologies, the explosion in the measurement data collected by structural health monitoring (SHM) systems installed in bridges brings both opportunities and challenges to the engineering community for the SHM of bridges. Deep learning (DL), based on deep neural networks and equipped with high-end computer resources, provides a promising way of using big measurement data to address the problem and has made remarkable successes in recent years. This paper focuses on the review of the recent application of DL in SHM, particularly damage detection, and provides readers with an overall understanding of the missions faced by the SHM of the bridges. The general studies of DL in vibration-based SHM and vision-based SHM are respectively reviewed first. The applications of DL to some real bridges are then commented. A summary of limitations and prospects in the DL application for bridge health monitoring is finally given.
Shunquan Qin serves as Academician of the Chinese Academy of Engineering for his lifelong contributions to bridge Engineering. He is the chairman of China Railway Major Bridge Reconnaissance & Design Institute Co. Ltd., deputy director of Bridge and Structural Engineering Branch of China Civil Engineering Society. He is also the distinguished professor in Southwest Jiaotong University. He established the theory of unstressed state control method for the bridge construction in stages and applied the method to more than 30 long-span bridges. He promoted the innovation of the industrialized and standardized construction method for the long-span bridges, and the development of integral prefabrication and erection technology for the bridges across the sea or on the railway passenger special lines, such as Dashengguan Yangtze River Bridge, Zhengzhou Yellow River Railway Bridge, Donghai Bridge, Hangzhou Bay Bridge, Macao Xiwan Bridge, Lhasa River Bridge on Qinghai-Tibet Railway, Wuhan 27 Yangtze River Bridge, Bangladesh Paksey Bridge, Huanggang Railway Bridge, Tongling Yangtze River Bridge, Anqing Yangtze River Bridge, Wuhan Parrot Island Yangtze River Bridge and Hong Kong-Zhuhai-Macao Bridge. He invented 50 Patents, published 3 academic monographs.
Professor Yongle Li, head of the Department of Bridge Engineering in Southwest Jiaotong University. He was selected successively as the winner of the National Outstanding Youth Fund, Cheung Kong scholar, a leader in scientific and technological innovation of the National Ten Thousand Plan. As the head, he organized and leads the "bridge wind resistance and driving safety" innovation team in Sichuan Province. He is the core member of the innovation team of the Ministry of Science and Technology, the main member of the national innovation and intelligent introduction base. He is also the member or director in 8 academic societies or sub committees. For a long time, he conducts the research on dynamic performance and driving safety of long-span bridges. He published more than 200 journal articles, including more than 80 SCI indexed papers and over 100 EI indexed papers.