Investigation on the moving load identification for bridges based on long-gauge strain sensing and skew-Laplace fitting

Abstract

Abstract Vehicle loads have long-term and repeated characteristics, affecting the service and safety performance of bridges. Therefore, the identification method of moving load is a meaningful research field. This paper proposes a novel method of moving load identification based on long-gauge strain sensing to solve the shortcomings of weigh-in-motion techniques and traditional monitoring technology. The theoretical derivation shows that the envelope area of the long-gauge strain influence line is directly proportional to the vehicle weight. The load identification is conducted based on this relation. Then, the extremum of the influence line is extracted by Laplace function fitting, which is used to identify the speed and wheelbase. A series of numerical simulations and experiments are carried out to verify the effectiveness of the proposed method. The numerical simulation results show that the identification errors of vehicle speed and gross vehicle weight (GVW) are less than 3%, and the overall error of the wheelbase is less than 5%. In addition, the experiment researchers present the identification error of GVW as less than 10%, which indicates that the proposed identification method has excellent practicability and robustness.