Application of vibration-based damage detection algorithms to experimental data from multi-storey steel structures. (13th August 2020)

• Record Type: Journal Article
• Authors: Liao, Yizheng; Balafas, Konstantinos; Kiremidjian, Anne S.; Rajagopal, Ram; Loh, Chin-Hsiung
• Is Part Of: International journal of sustainable materials and structural systems. Volume 4:Number 2/4(2020)
• Page Start: 199
• Page End: 226
• Publication Date: 2020-08-13
• Subjects: structural health monitoring -- SHM -- wireless sensor network -- damage detection -- experimental study -- statistical pattern recognition -- SPR; Materials -- Environmental aspects -- Periodicals; Sustainable engineering -- Periodicals; Sustainable buildings -- Periodicals; 620.1
• Languages: English
• ISSNs: 2043-8621

Abstract

Recently, there has been remarkable progress in the development of data acquisition systems and statistical damage detection algorithms in the field of vibration-based structural health monitoring (SHM). However, many of them have not been validated by experimental data. This paper presents and analyzes a shake table experiment of two three-story steel structures. The structural damage was introduced in a systematic and controlled way and the structures were tested with different levels of earthquakes. Three types of analyses were conducted. First, a wireless sensor system designed for SHM, SnowFort, was demonstrated to have the same accuracy as the conventional wired sensors. Second, the rotation algorithm is applied to estimate residual displacements in the structures and achieves excellent agreements with the displacement sensor measurements. At last, a continuous wavelet transform-based damage detector is applied to the experimental data. The results highlight that the wavelet model parameters are sensitive to the damage state.