Experimental Verification of Modal Identification of a High-rise Building Using Independent Component Analysis

Abstract Independent component analysis is one of the linear transformation methods based the techniques for separating blind sources from the output signals of the system. Recently, the method has been analytically applied to the identification of mode shapes and modal responses from the output signal of structures. This study aims to experimentally validate the blind source separation using ICA method and propose a novel method for identification of the modal parameters from the decomposed modal responses. The result of the experimental testing on the three-story steel scale model shows that the mode shapes obtained by ICA method are in good agreement with those by the analytical and peak-picking method in the frequency domain. Based on the robust mathematical model, ICA can calculate the natural frequency and damping ratio effectively using the probability distribution function of the instantaneous natural frequency determined by Hilbert transform of the decomposed modal responses and the change in the output covariance. Finally, the validity of the proposed method paves the way for more effective output-only modal identification for assessment of existing steel-concrete buildings

Ambient vibration re-testing and operational modal analysis of the Humber Bridge

An ambient vibration survey of the Humber Bridge was carried out in July 2008 by a combined team from the UK, Portugal and Hong Kong. The exercise had several purposes that included the evaluation of the current technology for instrumentation and system identification and the generation of an experimental dataset of modal properties to be used for validation and updating of finite element models for scenario simulation and structural health monitoring. The exercise was conducted as part of a project aimed at developing online diagnosis capabilities for three landmark European suspension bridges. Ten stand-alone tri-axial acceleration recorders were deployed at locations along all three spans and in all four pylons during five days of consecutive one-hour recordings. Time series segments from the recorders were merged, and several operational modal analysis techniques were used to analyse these data and assemble modal models representing the global behaviour of the bridge in all three dimensions for all components of the structure. The paper describes the equipment and procedures used for the exercise, compares the operational modal analysis (OMA) technology used for system identification and presents modal parameters for key vibration modes of the complete structure. The results obtained using three techniques, natural excitation technique/eigensystem realisation algorithm, stochastic subspace identification and poly-Least Squares Frequency Domain method, are compared among themselves and with those obtained from a 1985 test of the bridge, showing few significant modal parameter changes over 23 years in cases where direct comparison is possible. The measurement system and the much more sophisticated OMA technology used in the present test show clear advantages necessary due to the compressed timescales compared to the earlier exercise. Even so, the parameter estimates exhibit significant variability between different methods and variations of the same method, while also varying in time and having inherent variability. (C) 2010 Elsevier Ltd. All rights reserved

Mastering Complex Modes: A New Method for Real-Time Modal Identification of Vibrating Systems

A novel algorithm for real-time modal identification in linear vibrating systems with complex modes is introduced, utilizing a combination of first order eigen-perturbation and second order separation techniques. In practical settings, structures with complex modes are frequently encountered and their presence often poses a challenge in accurately estimating the source signal in real-time. The proposed methodology addresses this issue by incorporating the right angle phase shift of the response in the sensor output and updating the second order statistics of the complex response through first order eigen-perturbation. Empirical evidence of the efficacy of the technique is demonstrated through numerical case studies and validation using various numerically modeled systems, as well as a standard ASCE-SHM benchmark problem with complex modes, highlighting the capability of the proposed method to achieve precise real-time modal property identification and online source separation with a minimal number of initially required batch data.Comment: 18 pages, journal articl

Perspectives of Second-Order Blind Identification for Operational Modal Analysis of Civil Structures

Innovative methods for output-only estimation of the modal properties of civil structures are based on blind source separation techniques. In the present paper attention is focused on the second-order blind identification (SOBI) algorithm and the influence of its analysis parameters on computational time and accuracy of modal parameter estimates. These represent key issues in view of the automation of the algorithm and its integration within vibration-based monitoring systems. The herein reported analyses and results provide useful hints for reduction of computational time and control of accuracy of estimates. The latter topic is of interest in the case of single modal identification tests, too. A criterion for extraction of accurate modal parameter estimates is identified and applied to selected experimental case studies. They are representative of the different levels of complexity that can be encountered during real modal tests. The obtained results point out that SOBI can provide accurate estimates and it can also be automated, confirming that it represents a profitable alternative for output-only modal analysis and vibration-based monitoring of civil structures

Characterization of Dynamic Structures Using Parametric and Non-parametric System Identification Methods

The effects of soil-foundation-structure (SFS) interaction and extreme loading on structural behaviors are important issues in structural dynamics. System identification is an important technique to characterize linear and nonlinear dynamic structures. The identification methods are usually classified into the parametric and non-parametric approaches based on how to model dynamic systems. The objective of this study is to characterize the dynamic behaviors of two realistic civil engineering structures in SFS configuration and subjected to impact loading by comparing different parametric and non-parametric identification results. First, SFS building models were studied to investigate the effects of the foundation types on the structural behaviors under seismic excitation. Three foundation types were tested including the fixed, pile and box foundations on a hydraulic shake table, and the dynamic responses of the SFS systems were measured with the instrumented sensing devices. Parametric modal analysis methods, including NExT-ERA, DSSI, and SSI, were studied as linear identification methods whose governing equations were modeled based on linear equations of motion. NExT-ERA, DSSI, and SSI were used to analyze earthquake-induced damage effects on the global behavior of the superstructures for different foundation types. MRFM was also studied to characterize the nonlinear behavior of the superstructure during the seismic events. MRFM is a nonlinear non-parametric identification method which has advantages to characterized local nonlinear behaviors using the interstory stiffness and damping phase diagrams. The major findings from the SFS study are: *The investigated modal analysis methods identified the linearized version of the model behavior. The change of global structural behavior induced by the seismic damage could be quantified through the modal parameter identification. The foundation types also affected the identification results due to different SFS interactions. The identification accuracy was reduced as the nonlinear effects due to damage increased. *MRFM could characterize the nonlinear behavior of the interstory restoring forces. The localized damage could be quantified by measuring dissipated energy of each floor. The most severe damage in the superstructure was observed with the fixed foundation. Second, the responses of a full-scale suspension bridge in a ship-bridge collision accident were analyzed to characterize the dynamic properties of the bridge. Three parametric and non-parametric identification methods, NExT-ERA, PCA and ICA were used to process the bridge response data to evaluate the performance of mode decomposition of these methods for traffic, no-traffic, and collision loading conditions. The PCA and ICA identification results were compared with those of NExT-ERA method for different excitation, response types, system damping and sensor spatial resolution. The major findings from the ship-bridge collision study include: *PCA was able to characterize the mode shapes and modal coordinates for velocity and displacement responses. The results using the acceleration were less accurate. The inter-channel correlation and sensor spatial resolution had significant effects on the mode decomposition accuracy. *ICA showed the lowest performance in this mode decomposition study. It was observed that the excitation type and system characteristics significantly affected the ICA accuracy

Improved frequency domain decomposition and stochastic subspace identification algorithms for operational modal analysis

The accuracy of the estimated modal damping ratios in operational modal analysis (OMA) remains an open issue and is often characterized by a large error. The modal damping ratio is considered to be a good practical parameter for structural damage detection due to its sensitivity and sufficient responsiveness to damage compared to natural frequency and mode shape. Therefore, an accurate estimate of the modal damping ratio will assist in developing an effective modal-based structural damage detection approach. The objective of this research focuses on improvements of frequency domain decomposition (FDD) and stochastic subspace identification (SSI) algorithms, particularly in estimating modal damping ratio. These methods have gained a lot of attention and interest compared to other OMA methods due to their ability in estimating modal parameters. However, FDD has a problem dealing with high damping levels, while SSI has difficulty in handling harmonic components. This will cause a large error in estimating the modal damping ratio. Difficulties also arise for automation of SSI as several predefined set parameters are compulsory at start-up for each analysis. This study introduces an iterative loop of advanced optimization to enhance the capabilities of classical FDD algorithm by optimizing the value of the modal assurance criterion (MAC) index and the selection of the correct time window on the auto-correlation function that represents the most challenging part of the algorithms. This study also presents the development of the SSI framework in automated OMA and harmonic removal method using image-based feature extraction along with the application of empirical mode decomposition. The implementation of image-based feature extraction can be used for clustering and classification of harmonic components from structural poles as well as to identify modal parameters by neglecting any calibration or user-defined parameter at start-up. The proposed approach is assessed through experimental and numerical simulation analysis. Based on the numerical simulation results, the proposed optimized FDD can estimate modal damping ratio with high accuracy and consistency by showing average percentage deviation (error) below 5.50% compared to classical FDD and benchmark approach, which is a refined FDD. Errors in classical FDD can reach an average of up to 15%, whereas for refined FDD the average is around 10%. Meanwhile, the results of the proposed approach in experimental verification show a reasonable average percentage deviation of about 5.75%, while the classical FDD algorithm is overestimated which averages about 29% in all cases. For the proposed automation of SSI, the estimated results of modal damping ratio in the numerical simulation are below 2.5% of the average error compared to other SSI methods which on average exceed 3.2%. For experimental verification, the results of the proposed approach indicate very satisfactory agreement by showing average deviation percentage below 4.20% compared to other SSI methods which on average exceeds 14%. Furthermore, the results of the proposed automated harmonic removal in SSI framework for estimating modal damping ratio using existing online experimental data sets demonstrate very high accuracy and consistent results after removing harmonic components, showing an average deviation percentage of below 7.22% compared to orthogonal projection and smoothing technique based on linear interpolation approaches where the average deviation percentage exceeds 9%

An improved multi-variate empirical mode decomposition method towards system identification of structures

Structural health monitoring (SHM) plays a key role towards condition assessment of large-scale civil structures using modern sensing technology. Once the rich vibration data is collected, important system information is extracted from the data and sub- sequently such information is used for necessary decision making including adopting maintenance, retro tting or control strategies. System identi cation is one of the key steps in SHM where unknown system information of the structures is estimated based on the response measurements. However, depending on excitation characteristics or system behavior, vibration measurements become complicated where traditional methods are unable to accurately analyze the data. In this thesis, Multivariate Empirical Mode Decomposition (MEMD) method is ex- plored to undertake ambient system identi cation of structures using the multi-sensor vibration data. Due to inherent sifting operation of EMD, the traditional MEMD re- sults into mode-mixing that causes signi cant inaccuracy in structural modal identi - cation. In this research, Independent Component Analysis (ICA) method is integrated with the MEMD to alleviate mode mixing in the resulting modal responses. The pro- posed hybrid MEMD method is veri ed using a suite of numerical, experimental and full-scale studies (e.g., a high-rise tower in China and a long-span bridge in Canada) considering several practical applications including low energy modes, closely spaced frequencies and measurement noise in real-life buildings and bridges. The results show signi cantly improved performance of the proposed method compared to the standard EMD method and therefore, the proposed method can be considered as a robust ambient modal identi cation method for exible structures