Load-Effect Separation of a Large-Span Prestressed Structure Based on an Enhanced EEMD-ICA Methodology

Prestressed structures have gained popularity in large-span buildings due to their great spanning capacity and light self-weights. This kind of structure is normally subjected to multiple types of loads, such as temperature load, wind load, and construction load. The determination of different load effects not only guides the design of similar structures but also helps reveal the damage-induced variation that would be concealed by the environmental loads. To determine the different load effects, separation of the load effects collected by structural health monitoring (SHM) is needed. This study presents an enhanced approach for load effect separation based on independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD), called EEMD-ICA*. The proposed method is to minimize manual tuning of user-defined parameters, which makes the EEMD-ICA suitable for separating load effects from the different measured data. Specifically, an optimization method is developed to determine the appropriate level of the added white noise in the EEMD using the relative root-mean-square error (RMSE) index. A logarithm form of Bayesian information criterion (BIC) is employed for the robust estimation of the number of load effects in the ICA. Simulated structural responses from a square orthogonal cable-net are used to validate the effectiveness of the EEMD-ICA*. Then, the proposed methodology is employed to extract various load effects from the SHM data of the National Speed Skating Oval (NSSO), which is the largest single-layer cable-net structure in the world. (C) 2021 American Society of Civil Engineers.

Automated summary

Prestressed structures are popular in large-span buildings due to their spanning capacity and light self-weights. This study proposes an enhanced approach for load effect separation using independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD), called EEMD-ICA*. The method can effectively separate load effects from different measured data.