Synchronous Identification of Damage and Vehicle Load on Simply Supported Bridges Based on Long-Gauge Fiber Bragg Grating Sensors

Bridge vehicle-load and damage-condition information plays a vital role in the structural analysis and assessment of bridges. This information can be derived from bridge responses (such as displacement, acceleration, and strains). Most existing bridge damage and load identification methods are based on different measurements from both the bridge damage and load subsystems (different sensors and methods) in the literature. Accordingly, more sensors and measuring activities are required; therefore, the costs associated with structural health monitoring tend to rise. This paper proposes a new method for the synchronous identification of damage and vehicle loads on simply supported bridges, which uses just one set of long-gauge fiber Bragg grating (FBG) sensors and reduces the use of sensing devices, sensors, and costs. In the proposed method, the correlations among the peak values of static macrostrain curves, bending stiffness of bridge cross sections, and vehicle loads are established based on the macrostrain influence line theory. A numerical case study of a 30-m simply supported bridge, subject to moving vehicles, is performed to preliminarily validate the effectiveness of the proposed method. In this case, the damage identification error is below 6%, and the load identification error is less than 4%. In addition, a laboratory experiment including a model bridge and car is conducted to further examine the feasibility of this method in engineering practice, and the results show that the model bridge damage and load can be identified synchronously with good accuracies. (C) 2019 American Society of Civil Engineers.