Bridge vehicle-induced effect influence line characteristic function based on monitoring big data: definition and identification

Abstract

In this paper, an extended influence line parameter under the action of statistical steady-state traffic flow loads is defined, which is called the vehicle-induced effect influence line characteristic function (ILF). It is theoretically proved that this function can characterize the static characteristics of bridge structures, and can also be used to track and monitor the evolution of static characteristics of bridge structures throughout their service periods. On this basis, this paper presents a set of methods to identify this function from monitoring big data, mainly based on extracting the static effect of a single vehicle on the structure, including methods such as empirical mode decomposition (EMD), spectrum analysis, and mode reorganization. The set of methods only requires monitoring structural effects, without the need for traffic load information, which greatly reduces the requirements for the monitoring system, with simple implementation and high operability. Through the application of the monitoring data of the actual bridge, the results show that the recognition effect of the vehicle-induced strain influence line characteristic function ( $\mathrm{IL}{\mathrm{F}}_{\epsilon }$ ) by the proposed method is in line with the theoretical expectation, and it is suitable for the actual complex and changeable monitoring environment with good robustness. The identification technology of ILF proposed in this paper can be further used to study a new index system to characterize the static characteristics of the structure, thus opening up a new methodological field for the intelligent perception and diagnosis of the structure under the condition of monitoring big data.