Time synchronization for acceleration measurement data of Jiangyin Bridge subjected to a ship collision. Issue 1 (26th May 2017)

• Record Type: Journal Article
• Authors: Wang, W.D.; Jiang, S.F.; Zhou, H.F.; Yang, M.; Ni, Y.Q.; Ko, J.M.
• Is Part Of: Structural control and health monitoring. Volume 25:Issue 1(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-05-26
• Subjects: acceleration -- ship collision -- state‐space model -- structural health monitoring -- time synchronization; Structural engineering -- Periodicals; Structural control (Engineering) -- Periodicals; Automatic data collection systems -- Periodicals; Detectors -- Periodicals; 624.17
• DOI: 10.1002/stc.2039
• Languages: English
• ISSNs: 1545-2255

Abstract

Summary: State‐space (SS) model is proposed to identify the time lag between the asynchronous accelerations at different locations of the Jiangyin Bridge measured during a ship–bridge collision. One of the accelerations is chosen as the reference signal, and the time axis of the rest of them are shifted relative to that of the reference signal with a series of shifting times. For each pair of reference and time shifted signals, SS models in correspondence to the shifting time series are formulated. Their system matrices are identified with data‐driven stochastic subspace identification algorithm, and their model order is determined by Akaike's information theoretic criterion and final prediction error. If the 2 accelerations for model fitting are asynchronous, errors may be introduced into the SS model and its prediction error is expected to be greater than the counterpart obtained with synchronous accelerations. Therefore, the actual time lag between them is identified from the shifting time that corresponds to the minimum of loss function, which is used to map the prediction error vector sequence to a real number. In addition, asynchronous acceleration data measured 2 hr ahead of the ship–bridge collision and synchronous acceleration data measured long after the ship–bridge collision are also analyzed. The former dataset is exploited to evaluate the reproducibility of the SS model for time synchronization, and the latter dataset is utilized to examine its anti‐false‐identification capability. The results show that the SS model achieves a satisfactory performance in the identification of time lag for both asynchronous and synchronous measurement data.