Enhanced sparse component analysis for operational modal identification of real-life bridge structures

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.Blind source separation receives increasing attention as an alternative tool for operational modal analysis in civil applications. However, the implementations on real-life structures in literature are rare, especially in the case of using limited sensors. In this study, an enhanced version of sparse component analysis is proposed for output-only modal identification with less user involvement compared with the existing work. The method is validated on ambient and non-stationary vibration signals collected from two bridge structures with the working performance evaluated by the classic operational modal analysis methods, stochastic subspace identification and natural excitation technique combined with the eigensystem realisation algorithm (NExT/ERA). Analysis results indicate that the method is capable of providing comparative results about modal parameters as the NExT/ERA for ambient vibration data. The method is also effective in analysing non-stationary signals due to heavy truck loads or human excitations and capturing small changes in mode shapes and modal frequencies of bridges. Additionally, closely-spaced and low-energy modes can be easily identified. The proposed method indicates the potential for automatic modal identification on field test data.The third author gratefully thanks the funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 330195

Output-Only Vibration-Based Monitoring of Civil Infrastructure via Sub-Nyquist/Compressive Measurements Supporting Reduced Wireless Data Transmission

The consideration of wireless acceleration sensors is highly promising for cost-effective output-only system identification in the context of operational modal analysis (OMA) of large-scale civil structures as they alleviate the need for wiring. However, practical monitoring implementations for OMA using wireless units suffer a number of drawbacks related to wireless transmission of densely sampled acceleration time-series including the energy self-sustainability of the sensing nodes. In this work, two recently proposed approaches for output-only modal identification addressing the above issues through balancing monitoring accuracy with data transmission costs are comparatively studied and numerically assessed using field recorded acceleration datasets from two different structures: (i) an operating on-shore wind turbine, (ii) an open to traffic highway bridge. One approach utilizes non-uniform-in-time deterministic multi-coset sampling at sub-Nyquist rates to capture structural response acceleration time-series under ambient excitation assuming stationary signal conditions. In this approach, a power spectrum blind sampling technique is used to estimate the response acceleration power spectral density matrix from the low-rate sampled measurements and is coupled with the Frequency Domain Decomposition method of OMA. The other is a spectro-temporal compressive sensing approach which recovers response acceleration signals through time-series reconstruction in the time domain from sub-Nyquist non-uniform-in-time randomly sampled measurements. Prior knowledge of signal structure in the spectral domain is exploited through smart on-sensor operations and sensor/server communication. The benefits and limitations of the considered approaches are discussed and demonstrated by processing the field recorded datasets for different levels of signal compression and by estimating battery lifetime gains at a single sensor achieved by reduced data transmission. It is concluded that the two approaches are readily applicable in OMA of large-scale structures and can be used complementarily depending on the requirements of any particular acceleration monitoring campaign: time-series extraction for further interrogation vs. solely modal properties estimation

Decentralized Ambient System Identification of Structures

Many of the existing ambient modal identification methods based on vibration data process information centrally to calculate the modal properties. Such methods demand relatively large memory and processing capabilities to interrogate the data. With the recent advances in wireless sensor technology, it is now possible to process information on the sensor itself. The decentralized information so obtained from individual sensors can be combined to estimate the global modal information of the structure. The main objective of this thesis is to present a new class of decentralized algorithms that can address the limitations stated above. The completed work in this regard involves casting the identification problem within the framework of underdetermined blind source separation (BSS). Time-frequency transformations of measurements are carried out, resulting in a sparse representation of the signals. Stationary wavelet packet transform (SWPT) is used as the primary means to obtain a sparse representation in the time-frequency domain. Several partial setups are used to obtain the partial modal information, which are then combined to obtain the global structural mode information. Most BSS methods in the context of modal identification assume that the excitation is white and do not contain narrow band excitation frequencies. However, this assumption is not satisfied in many situations (e.g., pedestrian bridges) when the excitation is a superposition of narrow-band harmonic(s) and broad-band disturbance. Under such conditions, traditional BSS methods yield sources (modes) without any indication as to whether the identified source(s) is a system or an excitation harmonic. In this research, a novel under-determined BSS algorithm is developed involving statistical characterization of the sources which are used to delineate the sources corresponding to external disturbances versus intrinsic modes of the system. Moreover, the issue of computational burden involving an over-complete dictionary of sparse bases is alleviated through a new underdetermined BSS method based on a tensor algebra tool called PARAllel FACtor (PARAFAC) decomposition. At the core of this method, the wavelet packet decomposition coefficients are used to form a covariance tensor, followed by PARAFAC tensor decomposition to separate the modal responses. Finally, the proposed methods are validated using measurements obtained from both wired and wireless sensors on laboratory scale and full scale buildings and bridges

Load Estimation, Structural Identification and Human Comfort Assessment of Flexible Structures

Stadiums, pedestrian bridges, dance floors, and concert halls are distinct from other civil engineering structures due to several challenges in their design and dynamic behavior. These challenges originate from the flexible inherent nature of these structures coupled with human interactions in the form of loading. The investigations in past literature on this topic clearly state that the design of flexible structures can be improved with better load modeling strategies acquired with reliable load quantification, a deeper understanding of structural response, generation of simple and efficient human-structure interaction models and new measurement and assessment criteria for acceptable vibration levels. In contribution to these possible improvements, this dissertation taps into three specific areas: the load quantification of lively individuals or crowds, the structural identification under non-stationary and narrowband disturbances and the measurement of excessive vibration levels for human comfort. For load quantification, a computer vision based approach capable of tracking both individual and crowd motion is used. For structural identification, a noise-assisted Multivariate Empirical Mode Decomposition (MEMD) algorithm is incorporated into the operational modal analysis. The measurement of excessive vibration levels and the assessment of human comfort are accomplished through computer vision based human and object tracking, which provides a more convenient means for measurement and computation. All the proposed methods are tested in the laboratory environment utilizing a grandstand simulator and in the field on a pedestrian bridge and on a football stadium. Findings and interpretations from the experimental results are presented. The dissertation is concluded by highlighting the critical findings and the possible future work that may be conducted

A probabilistic approach to analyse Blade Tip Timing data of non-synchronous vibrations under constant rotor speed

Blades are among the most critical components of turbomachines, their monitoring and characterization undergoing working conditions are fundamental for the insiders, both for preventing eventual breakage and for optimising future development. Two approaches are possible for monitoring rotor blade vibrations: a traditional one based on the use of strain gauges and another one called Blade Tip Timing (BTT). BTT is an indirect, non-intrusive simple and robust measurement method, but the processing of such data is not easy because they are often subsampled with respect to the Nyquist limit and the ordering of the samples is not unique. In this work the focus is on multi component non-synchronous vibrations, typical for example of flutter, measured at constant rotor speed by a BTT system. These data are organized into batches of fixed length called snapshots and they are interpreted as members of a random vector. When the signal contains only one harmonic component the frequency can be determined using a method here described and called Harmonic Matching (HM). While for the analyses of multi harmonic component vibrations a probabilistic approach capable of separating and identify the components using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) is proposed. For the development of data processing methods, the possibility of having controllable and repeatable data is fundamental, for this reason two test rigs of increasing complexity have been developed and are here described

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

Hybrid Time and Time-Frequency Blind Source Separation Towards Ambient System Identi cation of Structures

Blind source separation methods such as independent component analysis (ICA) and second order blind identification (SOBI) have shown considerable potential in the area of ambient vibration system identification. The objective of these methods is to separate the modal responses, or sources, from the measured output responses, without the knowledge of excitation. Several frequency domain and time domain methods have been proposed and successfully implemented in the literature. Whereas frequency-domain methods pose several challenges typical of dealing with signals in the frequency-domain, popular time-domain methods such as NExT/ERA and SSI pose limitations in dealing with noise, low sensor density, modes having low energy content, or in dealing with systems having closely-spaced modes, such as those found in structures with passive energy dissipation devices, for example, tuned mass dampers.Motivated by these challenges, the current research focuses on developing methods to address the problem of separability of sources with low energy content, closely-spaced modes, and under-determined blind identification, that is, when the number of response measurements is less than the number of sources. These methods, requiring the time and frequency diversities of the measured outputs, are referred to as hybrid time and time-frequency source separation methods. The hybrid methods are classified into two categories. In the first one, the basic principles of modified SOBI are extended using the stationary wavelet transform (SWT) in order to improve the separability of sources, thereby improving the quality of identification. In the second category, empirical mode decomposition is employed to extract the intrinsic mode functions from measurements, followed by an estimation of the mode shape matrix using iterative and/or non iterative procedures within the framework of modified-SOBI. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of these hybrid approaches to structural system identification problems