Operational Early Warning Analysis of High- Speed Railway Bridge Structure Based on Sliding Window Technique and Data Driven Stochastic Subspace Identification Algorithm

Abstract

Taking the seismic shaking table model test of a typical railway cable-stayed bridge as the research background, the sliding window technique and data-driven stochastic subspace identification (Data-Driven SSI) algorithm were employed to study the early warning for operation state of high-speed railway bridge structure. To achieve the real-time tracking and recognition of the modal parameters of high-speed railway bridge in time domain, the sliding window technique was introduced to data-driven stochastic subspace identification algorithm, and a sliding-window stochastic subspace identification method was proposed. Subsequently, based on the results of sliding window stochastic subspace identification algorithm and combining the state evaluation indices of high-speed railway bridge in various countries, the operational early warning method of high-speed railway bridge based on frequency change rate was proposed, which realized the real-time state evaluation for railway bridge. By using the testing data of the model test in different testing conditions, the real-time evaluation and early warning analysis of the state of the model bridge corresponding to different testing conditions were carried out. And then the real-time early warning results were compared to the actual cracking and damage situation respectively, the results show the correctness of this method and that the method can be applied to the operational early warning analysis of high-speed railway bridge