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Kalman filter–random forest-based method of dynamic load identification for structures with interval uncertainties

Liu, Yaru, Wang, Lei and Li, Min 2022. Kalman filter–random forest-based method of dynamic load identification for structures with interval uncertainties. Structural Control and Health Monitoring 29 (5) , e2935. 10.1002/stc.2935

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It is widely acknowledged that dynamic load identification is a powerful technology for structural health monitoring and reliability analysis. This paper investigates an uncertain dynamic load identification strategy with the combination of the Kalman filter (KF) algorithm and the random forest (RF) model. Enlightened by the concept of the KF, a one-step delayed identification of dynamic loads is completed in the state space domain. The previous loads can be calculated recursively utilizing the previous state estimation and the current displacement observation based on the least square method. In addition, the optimal state estimation will be achieved synchronously with the classical KF algorithm. In terms of inevitable uncertainties, the interval model is introduced to quantify uncertain system parameters and uncertain input loads. To determine the interval load process, the RF surrogate model is constructed to characterize their functional relationship, which is integrated by massive decision trees (DTs). Particularly, Latin hypercube sampling (LHS) and bootstrap sampling (BS) methods are employed to generate training samples for the RF model. The validity and feasibility of the developed methodology are eventually demonstrated by three numerical examples. The results indicate that the proposed approach has an excellent performance in both accuracy and efficiency.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: John Wiley and Sons
ISSN: 1545-2255
Date of Acceptance: 15 January 2022
Last Modified: 11 Apr 2022 14:11

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