Modal identification of arch dams using balanced stochastic subspace identification

Abstract

Dynamic characteristics extracted from ambient and forced vibration tests are always associated with some level of uncertainties because of unknown nature of applied forces, existence of ambient noises as well as measurement errors. Stochastic Subspace Methods are among the most accurate and consistent methods within the domain of operational modal analysis. In this research, a new technique for Operational Modal Analysis is proposed using Stochastic Realization Theory and Canonical Correlation Analysis, which in comparison with previous methodologies, identification process are directly performed in the prediction space by extracting orthonormal vectors of data space. Considering its optimized nature, the proposed method is expected to have superior accuracy in terms of elimination of unstable poles as well as low time consumption analysis. To indicate the efficiency and accuracy of the proposed algorithm, it is applied to reanalyze the results of the forced vibration tests performed on the Shahid-Rajaee arch dam in northern Iran. These tests were conducted via steady-state sinusoidal stimulation method. More accurate natural frequencies are obtained compared to those of previously reported results, besides the fact that the first three modes of the structure were identified by the new approach, while they were not observed via the previous one. In order to examine the capabilities of the proposed method for processing of ambient vibration records, the dynamic characteristics of the Pacoima dam was identified using the recorded responses during 2001 earthquake in San Fernando, California. The results indicated good accuracy in the obtained frequencies and damping ratios compared to those obtained via data driven subspace method. Time consumption of identification process were reduced significantly (up to 50%) for both case studies indicating a faster convergence rate provided by the proposed method.

Damage detection based on system identification of concrete dams using an extended finite element–wavelet transform coupled procedure

“…Recently, structural health monitoring (SHM) became an attractive research area, because of its ability to prepare a global check of the structure for damage detection and monitoring (Tarinejad and Damadipour, 2016; Tarinejad and Pourgholi, 2016). Various techniques of SHM are ranging from application of electrical techniques to structural dynamics procedures.…”

Monitor wind characteristics and wind‐induced responses of a skyscraper during two typhoons

“…The stabilization diagram describes the poles (modes) of the system under different modal orders, which can separate the natural modes from the spurious ones. [ 39 ] Figures 16 and 17 show the stabilization diagram of the identified modes during the typhoons of Jongdari and Rumbia, respectively, in which the y ‐axis represents truncated model order. It can be observed that the stable modes, unstable modes, and noise modes are mixed because of ambient noise and systematic error.…”

Structural system identification based on combining weighted transmissibility and wavelet transform

“…Operational modal analysis (OMA), also called output-only modal analysis, is used to determine the modal parameters of structural systems based on ambient vibration data. The OMA techniques in different time, frequency, and timefrequency domains, such as Peak Picking (PP), 1 Frequency Domain Decomposition (FDD), [2][3][4][5] Stochastic Subspace Identification (SSI), 6,7 wavelet transform (WT), [8][9][10][11] and FDD-WT, [12][13][14] assume that the input forces to the system are white noise. This is a limiting assumption, because structural vibrations observed during operation cannot always be considered as pure white noise excitation.…”