SSI-LSTM network for adaptive operational modal analysis of building structures. (15th July 2023)

• Record Type: Journal Article
• Authors: Yun, Da Yo; Shim, Hak Bo; Park, Hyo Seon
• Is Part Of: Mechanical systems and signal processing. Volume 195(2023)
• Publication Date: 2023-07-15
• Subjects: Structural health monitoring -- Long-short term memory (LSTM) algorithm -- Deep learning -- High-rise building -- Operational modal analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2023.110306
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: An adaptive SSI-LSTM method for estimation of changes in natural frequency is presented. The problem of computational time in conventional OMA can be solved by the adaptive SSI-LSTM. Both past and present responses of buildings can be considered in the adaptive SSI-LSTM method. The practicality of the proposed method is validated using the responses from a 55-story high-rise building. Abstract: Various operational modal analysis (OMA) methods have been developed to identify the modal parameters of buildings in use. Recently, efforts have been expended to solve the existing problems associated with accuracy and computational time of OMA methods using deep learning algorithms of convolution neural network (CNN) and deep neural network (DNN). Considering that the result of modal parameters involves a process associated with the extraction of the dynamic characteristics by separating the periodic modes based on a complex ambient vibration response, the neural network technique, whereby the outputs of the past and present data are independent and static, may not be suitable for application in OMA. In this study, an adaptive, stochastic subspace identification long-short term memory (SSI-LSTM) method is proposed to evaluate the variations of the modal parameters as an indicator of the dynamic characteristics of a structure in the time history data of the structural response measured in real-time. Both past and present responses can be considered in the adaptive SSI-LSTM method and can estimate the natural frequency that changes with aging and damage to buildings. Simultaneously, as the deep learning method is basically used, the problems of accuracy and computational time continuously raised in the OMA method can be solved. The proposed method can configure the OMA method using the modal parameter results from the ambient vibration response and SSI for direct network learning. The covariance-driven SSI (SSI-COV) method is used to construct the training data for the proposed adaptive SSI-LSTM method. The proposed SSI-LSTM method is verified by applying it to a simulation model with four degrees-of-freedom and a three-story experimental model. Lastly, the practicality of the proposed method is validated using the response obtained from a 55-story high-rise building.