With the development of the Chinese transportation industry, the number of bridges has increased significantly, but this results in high pressure of structural maintenance and management. Bridge management system (BMS) is critical for efficient maintenance and ensured safety of bridge structures during long-term operation. Building information modeling (BIM) is an emerging technology with powerful visualization and informatization capability, making it an ideal tool for developing modern management systems. This paper introduces the development of a bridge management system based on the BIM technology. Industry Foundation Classes (IFC) and International Framework for Dictionaries (IFD) standards are studied and extended, and coding rules are proposed for the Chinese bridge industry. Also, a standard structural modeling method is proposed to fast build the bridge BIM model. Web-BIM oriented bridge management is proposed, and portable devices are introduced into the system. Collaborative management is realized for different users. The BIM-based maintenance management system is designed. Finally, a practical BIM-based BMS is established for a long-span cable-stayed bridge in China. This system integrates the BIM with the geographic information system (GIS) and contains information management, inspection management, technical condition evaluation, and enables users to cooperate with each other. Such a BMS could help to improve the management efficiency and ensure its normal operation, providing a useful platform for the maintenance of massive bridges in China.

Since physical parameters are much more sensitive than modal parameters, structural parameter identification with an extended Kalman filter (EKF) has received extensive attention in structural health monitoring for civil engineering structures. In this paper, EKF-based parameter identification technique is studied with numerical and experimental approaches. A four-degree-of-freedom (4-DOF) system is simulated and analyzed as an example. Different integration methods are examined and their influence to the final identification results of the structural stiffness and damping is also studied. Furthermore, the effect of different kinds of noise is studied as well. Identification results show that the convergence speed and estimation accuracy under Gaussian noises are better than those under non-Gaussian noises. Finally, experiments with a five-story steel frame are conducted to verify the damage identification capacity of the EKF. The results show that stiffness with different damage degrees can be identified effectively, which indicates that the EKF is capable of being applied for damage identification and health monitoring for civil engineering structures.

Non-destructive analysis of fiber reinforced polymer (FRP) composites is important for confirming the long-term safety and durability of concrete structures. In this study, a pulse-heating infrared thermography technique was used to detect and characterize bonding defects of externally bonded carbon fiber reinforced polymers (CFRP) on concrete surface structures. The CFRP composite contains various bonding defects of three different sizes located at five different depths. Sequential thermal images were obtained to describe the temperature contrast and shapes of the bonding defects. Through analysis of the maximum temperature response, we investigated the effects of defect size and depth on the defect temperature response. The relationship between the defect depth and maximum temperature response was used to quantitatively estimate the defect depth. In addition, finite element simulations were performed on the CFRP composites with bonding defects to investigate the temperature response of various defects, which showed good agreement with the experimental results. This confirms the effectiveness of the infrared thermography method to detect and characterize bonding defects of FRP composites bonded on concrete structures.

Distributed strain sensing techniques using long-gauge fiber Bragg grating sensors have been widely applied in health monitoring of civil engineering structures. Developing an effective damage identification method based on the acquired macro-strain is a crucial issue for real applications. In this paper, based on the previous studies on modal macro-strain vector (MMSV) for damage identification under single-point excitation, the damage detection approach under multi-point excitations is further studied. An effective damage index is further proposed as an indicator of the structural damage and the frequency domain decomposition (FDD) technique is introduced to accurately extract the MMSV. The sensitivity and effectiveness of the proposed method for damage detection of a cantilever beam are verified in detail with both numerical simulations and experiments. Results show that both the single and multi damages with different damage extents can be effectively detected under either single point excitations or the wind load excitations. It shows that the proposed approach can do the work well and could be applied for the structural health monitoring of real structures.