Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies

This paper presents a two-stage structural health monitoring methodology and applies it to the Phase I benchmark study sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. In the first stage, modal parameters are identified using measured structural response from the undamaged system and then from the (possibly) damaged system. In the second stage, these data are used to update a parametrized structural model of the system using Bayesian system identification. The approach allows one to obtain not only estimates of the stiffness parameters but also the probability that damage in any substructure exceeds any specified threshold expressed in terms of a fractional stiffness loss. It successfully identifies the location and severity of damage in all cases of the benchmark problem.     

Bayesian Analysis of the Phase II IASC–ASCE Structural Health Monitoring Experimental Benchmark Data

A two-step probabilistic structural health monitoring approach is used to analyze the Phase II experimental benchmark studies sponsored by the IASC–ASCE Task Group on Structural Health Monitoring. This study involves damage detection and assessment of the test structure using experimental data generated by hammer impact and ambient vibrations. The two-step approach involves modal identification followed by damage assessment using the pre- and postdamage modal parameters based on the Bayesian updating methodology. An Expectation–Maximization algorithm is proposed to find the most probable values of the parameters. It is shown that the brace damage can be successfully detected and assessed from either the hammer or ambient vibration data. The connection damage is much more difficult to reliably detect and assess because the identified modal parameters are less sensitive to connection damage, allowing the modeling errors to have more influence on the results.     

Flexibility Based Approach for Damage Characterization: Benchmark Application

A flexibility based damage characterization technique is described and its performance is examined in the context of Phase 1 of the benchmark study developed by the IASC-ASCE SHM Task Group. Noteworthy features of the analytical development are: (1) the methodology used to extract a matrix that is proportional to the flexibility when the excitation is stochastic; (2) the technique used to interrogate the changes in flexibility (or flexibility proportional matrices) with regards to the location of the damage; and (3) the method used to quantify the damage without the use of a model. The strategy proved successful in all the cases considered.     

Application of a Statistical Model Updating Approach on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark Study

This paper addresses the problem of structural health monitoring (SHM) and damage detection based on a statistical model updating methodology which utilizes the measured vibration responses of the structure without any knowledge of the input excitation. The emphasis in this paper is on the application of the proposed methodology in Phase I of the benchmark study set up by the IASC–ASCE Task Group on structural health monitoring. Details of this SHM benchmark study are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm〉. The benchmark study focuses on important issues, such as: (1) measurement noise; (2) modeling error; (3) lack of input measurements; and (4) limited number of sensors. A statistical methodology for model updating is adopted in this paper to establish stiffness reductions due to damage. This methodology allows for an explicit treatment of the measurement noise, modeling error, and possible nonuniqueness issues characterizing this inverse problem. The paper briefly describes the methodology and reports on the results obtained in detecting damage in all six cases of Phase I of the benchmark study assuming unknown (ambient) data. The performance, limitations, and difficulties encountered by the proposed statistical methodology are discussed.     

Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data

Structural health monitoring (SHM) is a promising field with widespread application in civil engineering. Structural health monitoring has the potential to make structures safer by observing both long-term structural changes and immediate postdisaster damage. However, the many SHM studies in the literature apply different monitoring methods to different structures, making side-by-side comparison of the methods difficult. This paper details the first phase in a benchmark SHM problem organized under the auspices of the IASC–ASCE Structural Health Monitoring Task Group. The scale-model structure adopted for use in this benchmark problem is described. Then, two analytical models based on the structure—one a 12 degree of freedom (DOF) shear-building model, the other a 120-DOF model, both finite element based—are given. The damage patterns to be identified are listed as well as the types and number of sensors, magnitude of sensor noise, and so forth. MATLAB computer codes to generate the response data for the various cases are explained. The codes, as well as details of the ongoing Task Group activities, are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm/〉.     

Investigation of a System Identification Methodology in the Context of the ASCE Benchmark Problem

This article briefly presents the theory for a system identification and damage detection algorithm for linear systems, and discusses the effectiveness of such a methodology in the context of a benchmark problem that was proposed by the ASCE Task Group in Health Monitoring. The proposed approach has two well-defined phases: (1) identification of a state space model using the Observer/Kalman filter identification algorithm, the eigensystem realization algorithm, and a nonlinear optimization approach based on sequential quadratic programming techniques, and (2) identification of the second-order dynamic model parameters from the realized state space model. Structural changes (damage) are characterized by investigating the changes in the second-order parameters of the “reference” and “damaged” models. An extensive numerical analysis, along with the underlying theory, is presented in order to assess the advantages and disadvantages of the proposed identification methodology.     

Structural Health Monitoring by Recursive Bayesian Filtering

A new vision of structural health monitoring (SHM) is presented, in which the ultimate goal of SHM is not limited to damage identification, but to describe the structure by a probabilistic model, whose parameters and uncertainty are periodically updated using measured data in a recursive Bayesian filtering (RBF) approach. Such a model of a structure is essential in evaluating its current condition and predicting its future performance in a probabilistic context. RBF is conventionally implemented by the extended Kalman filter, which suffers from its intrinsic drawbacks. Recent progress on high-fidelity propagation of a probability distribution through nonlinear functions has revived RBF as a promising tool for SHM. The central difference filter, as an example of the new versions of RBF, is implemented in this study, with the adaptation of a convergence and consistency improvement technique. Two numerical examples are presented to demonstrate the superior capacity of RBF for a SHM purpose. The proposed method is also validated by large-scale shake table tests on a reinforced concrete two-span three-bent bridge specimen.     

Structural Health Monitoring and Damage Assessment Using Frequency Response Correlation Criteria

Two frequency response correlation criteria, namely the global shape correlation (GSC) function and the global amplitude correlation (GAC) function, are established tools to quantify the correlation between predictions from a finite-element (FE) model and measured data for the purposes of FE model validation and updating. This paper extends the application of these two correlation criteria to structural health monitoring and damage detection. In addition, window-averaged versions of the GSC and GAC, namely WAIGSC and WAIGAC, are defined as effective damage indicators to quantify the change in structural response. An integrated method of structural health monitoring and damage assessment, based on the correlation functions and radial basis function neural networks, is proposed and the technique is applied to a bookshelf structure with 24 measured responses. The undamaged and damaged states, single and multiple damage locations, as well as damage levels, were successfully identified in all cases studied. The ability of the proposed method to cope with incomplete measurements is also discussed.     

Electromechanical Impedance-Based Structural Health Monitoring with Evolutionary Programming

An impedance-based structural health monitoring technique is presented. By analyzing the in-plane vibration of a thin lead–zirconate–titanate (PZT) patch, the electromechanical impedance of the PZT patch is predicted. The force impedances of a beam and a plate with damage are calculated by Ritz method using polynomial as shape functions. The damage is then identified from the changes of the impedance spectra caused by the appearance of damage. A hybrid evolutionary programming is employed as a global search technique to back-calculate the damage. A specially designed fitness function is proposed, which is able to effectively reduce the inaccuracy in representing the real structure using analytical or numerical models. Experiments are carried out on a beam and a plate to verify the numerical predictions. The results demonstrate that the proposed method is able to effectively and reliably locate and quantify the damage in the beam and the plate.     

Structural Health Monitoring by Piezo-Impedance Transducers. I: Modeling

The electromechanical impedance (EMI) technique, which employs piezoelectric–ceramic lead–zirconate–titarate (PZT) patches as impedance transducers, has emerged as a powerful nondestructive evaluation technique during the last few years. This series of two papers present a new simplified methodology to diagnose structural damages by means of surface bonded piezo-impedance transducers. The first part introduces a new PZT–structure electroelastic interaction model based on the concept of “effective impedance.” The proposed formulations can be conveniently employed to extract the mechanical impedance of any “unknown” structural system from the admittance signatures of a surface bonded PZT patch. This is an improvement over the existing models, whose complexity prohibits direct application in similar practical scenarios. This also eliminates the requirement of any a priori information concerning the phenomenological nature of the structure. The proposed model is experimentally verified by means of test on a smart system comprising an aluminum block with a PZT patch instrumented on it. Part II of this paper outlines a new methodology to evaluate structural damages using the extracted impedance spectra. The proposed approach is found to be suitable for diagnosing damages in structures ranging from miniature precision machine and aerospace components to large civil structures.     

Application of Structural Health Monitoring Techniques to Track Structural Changes in a Retrofitted Building Based on Ambient Vibration

One of the issues complicating the reliability assessment of structural health monitoring (SHM) methodologies slated for implementation under field conditions for damage detection in conjunction with typical infrastructure systems, is the paucity of experimental measurements from such structures. Particularly lacking is the availability of experimental data from physical structures, where quantifiable changes are made in the structure while SHM studies are being performed. That is precisely the focus of this paper. As a result of the 1994 Northridge Earthquake, a critical six-story building in the metropolitan Los Angeles region was found to need significant seismic mitigation measures. The building was instrumented with 14 state-of-the-art strong-motion accelerometers that were placed at various locations and in different orientations throughout the building. The instrumentation network was used to acquire extensive ambient vibration data sets at regular intervals that covered the whole construction phase, during which the building evolved from its original condition to the retrofitted status. This paper evaluates the usefulness of the natural excitation technique (NExT) in conjunction with the eigensystem realization algorithm (ERA) to determine the evolution of the modal properties of the subject building during the various phases of its retrofit process. Further, an assessment is made of the influence on the system identification results of significant user-selectable parameters such as: data window size and overlap; reference degree-of-freedom; and the dimensions of the associated Hankel matrix. In spite of the very low levels of ambient excitation, and the low spatial resolution of the sensors, use of the NExT/ERA algorithm yielded excellent identification results of the dominant modes of the building. Changes in the identified structural frequencies are correlated with the time that specific structural changes were made. It is shown that this unique collection of data can be extremely useful in calibrating the accuracy and sensitivity of various SHM schemes, as well as in providing useful identification parameter guidelines that can assist in the planning and deployment of sensor networks and associated data collection schemes for SHM applications.     

Linear Predictor-Based Lossless Compression of Vibration Sensor Data: Systems Approach

This paper presents a novel systems approach to compressing sensor network data. Unlike previous data compression methods, the proposed lossless linear predictor-based sensor data compression method utilizes structural system information to minimize the signal correlation in sensor network data. In the proposed method, linear predictor is derived in a system identification framework in which auto-regressive (AR) model is used as its model structure and the instrumental variables (IV) method is used to calculate the predictor parameters. A parametric study was carried out to study the effects of changes in system property, number of sensors, and sensor noise level on the compression performance of the proposed method. Both numerical simulation and experimental results show that the proposed sensor data compression method has a better compression performance than conventional linear predictor-based data compression method for single sensor.     

Hilbert-Huang Based Approach for Structural Damage Detection

When measured data contain damage events of the structure, it is important to extract the information of damage as much as possible from the data. In this paper, two methods are proposed for such a purpose. The first method, based on the empirical mode decomposition (EMD), is intended to extract damage spikes due to a sudden change of structural stiffness from the measured data thereby detecting the damage time instants and damage locations. The second method, based on EMD and Hilbert transform is capable of (1) detecting the damage time instants, and (2) determining the natural frequencies and damping ratios of the structure before and after damage. The two proposed methods are applied to a benchmark problem established by the ASCE Task Group on Structural Health Monitoring. Simulation results demonstrate that the proposed methods provide new and useful tools for the damage detection and evaluation of structures.     

Baseline Models for Bridge Performance Monitoring

A baseline model is essential for long-term structural performance monitoring and evaluation. This study represents the first effort in applying a neural network-based system identification technique to establish and update a baseline finite element model of an instrumented highway bridge based on the measurement of its traffic-induced vibrations. The neural network approach is particularly effective in dealing with measurement of a large-scale structure by a limited number of sensors. In this study, sensor systems were installed on two highway bridges and extensive vibration data were collected, based on which modal parameters including natural frequencies and mode shapes of the bridges were extracted using the frequency domain decomposition method as well as the conventional peak picking method. Then an innovative neural network is designed with the input being the modal parameters and the output being the structural parameters of a three-dimensional finite element model of the bridge such as the mass and stiffness elements. After extensively training and testing through finite element analysis, the neural network became capable to identify, with a high level of accuracy, the structural parameter values based on the measured modal parameters, and thus the finite element model of the bridge was successfully updated to a baseline. The neural network developed in this study can be used for future baseline updates as the bridge being monitored periodically over its lifetime.     

Overview of Vibrational Structural Health Monitoring with Representative Case Studies

In the field of nondestructive evaluation and damage detection, there is continued interest in the utilization of vibrational techniques. Structural damage will result in permanent changes in the distribution of structural stiffness. These changes may be detected through structural monitoring. Because of the direct relationship of stiffness, mass, and damping of a multi-degree-of-freedom system to the natural frequencies, mode shapes, and modal damping values, many studies have been directed at using these dynamic properties for the purpose of structural health monitoring. The use of vibrational monitoring is a developing field of structural analysis and is capable of assisting in both detecting and locating structural damage. Vibrational data have been shown to be most useful when used in conjunction with other monitoring systems if a remote and robust damage detection scheme is desired. This paper includes a literature review that summarizes the basic approaches to vibrational monitoring, suggested guidelines for sensor selection and monitoring, and concludes with three example case studies.     

Modified Cross-Correlation Method for the Blind Identification of Structures

Recently, blind source separation (BSS) methods have gained significant attention in the area of signal processing. Independent component analysis (ICA) and second-order blind identification (SOBI) are two popular BSS methods that have been applied to modal identification of mechanical and structural systems. Published results by several researchers have shown that ICA performs satisfactorily for systems with very low levels of structural damping, for example, for damping ratios of the order of 1% critical. For practical structural applications with higher levels of damping, methods based on SOBI have shown significant improvement over ICA methods. However, traditional SOBI methods suffer when nonstationary sources are present, such as those that occur during earthquakes and other transient excitations. In this paper, a new technique based on SOBI, called the modified cross-correlation method, is proposed to address these shortcomings. The conditions in which the problem of structural system identification can be posed as a BSS problem is also discussed. The results of simulation described in terms of identified natural frequencies, mode shapes, and damping ratios are presented for the cases of synthetic wind and recorded earthquake excitations. The results of identification show that the proposed method achieves better performance over traditional ICA and SOBI methods. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of the newly proposed method to structural identification problems.     

Multivariate Statistical Approach to Structural Damage Detection

The issue of structural damage detection is addressed through an innovative multivariate statistical approach in this paper. By invoking principal component analysis, the vibration responses acquired from the structure being monitored are represented by the multivariate data of the sample principal component coefficients (PCCs). A damage indicator is then defined based on a multivariate exponentially weighted moving average control chart analysis formulation, involving special procedures to allow for the effects of the estimated parameters and to determine the upper control limits in the control chart analysis for structural damage detection applications. Also, a data shuffling procedure is proposed to remove the autocorrelation probably present in the obtained sample PCCs. This multivariate statistical structural damage detection scheme can be applied to either the time domain responses or the frequency domain responses. The efficacy and advantages of the scheme are demonstrated by the numerical examples of a five-story shear frame and a shear wall as well as the experimental example of the I-40 Bridge benchmark.     

Ambient Vibration Data Analysis for Structural Identification and Global Condition Assessment

System identification is an area which deals with developing mathematical models to characterize the input-output behavior of an unknown system by means of experimental data. Structural health monitoring (SHM) provides the tools and technologies to collect and analyze input and output data to track the structural behavior. One of the most commonly used SHM technologies is dynamic testing. Ambient vibration testing is a practical dynamic testing method especially for large civil structures where input excitation cannot be directly measured. This paper presents a conceptual and reliable methodology for system identification and structural condition assessment using ambient vibration data where input data are not available. The system identification methodology presented in this study is based on the use of complex mode indicator functions (CMIFs) coupled with the random decrement (RD) method to identify the modal parameters from the output only data sets. CMIF is employed for parameter identification from the unscaled multiple-input multiple-output data sets generated using the RD method. For condition assessment, unscaled flexibility and the deflection profiles obtained from the dynamic tests are presented as a conceptual indicator. Laboratory tests on a steel grid and field tests on a long-span bridge were conducted and the dynamic properties identified from these tests are presented. For demonstrating condition assessment, deflected shapes obtained from unscaled flexibility are compared for undamaged and damaged laboratory grid structures. It is shown that structural changes on the steel grid structure are identified by using the unscaled deflected shapes.     

Vibration Testing at Meazza Stadium: Reliability of Operational Modal Analysis to Health Monitoring Purposes

In the last years an increasing interest has been devoted to all the topics related to the security and safety of people. Particular attention has been paid to health monitoring of large civil structures hosting many people, such as high-rise buildings and stadiums. Some extraordinary events, such as the Millennium Bridge oscillations in London, excited by pedestrians, or the Bruce Springsteen concert at the Ullevi Stadium in which coordinated jumps from the crowd caused serious damage to the structure, and drew attention toward a deeper and more careful study of all those problems related to the dynamic behavior of civil structures and their interaction with crowds. Research on these topics is also aimed, among others, at developing techniques allowing for a continuous monitoring of the structure, starting from a set of measurements that can be performed continuously, 24?h a day, without the need to stop the structure's functionality. The vast scientific literature confirms the possibility of relating structural health to the evolution of modal parameters, often reaching the aim of localizing any eventual damage, a task otherwise impossible with different techniques. This paper shows part of a long lasting project involving Politecnico di Milano in the setting up of a permanent health monitoring system at the G. Meazza Stadium in Milan. The aim of this project was the evaluation of the actual health state of the structures constituting the stands of the stadium and the deployment of a permanent monitoring system to record the vibration levels reached in all substructures during each event. Evaluation of the actual structure condition was performed by the use of ambient vibration, which was also checked against traditional experimental modal analysis, performed by using an inertial force given by a hydraulic actuator and a detailed measurement mesh. This offered the chance to exploit all possible information concerning natural frequencies, modal shapes, and damping factors. This task is extremely time consuming and expensive, therefore, it cannot be repeated very often. The possibility of using the data coming from the permanent monitoring system, which is about to be installed, is then an attractive perspective to improve structural diagnosis. It is expected that using operational modal analysis techniques will mean knowledge of the excitation applied to the structure will not be required. The parameter estimation obtained by this technique is usually affected by a spread, given both by the uncertainty of the adopted identification techniques and the influence of external parameters, such as crowd loading or temperature. As damage identification is related to changes of the modal parameters, the evaluation of their normal spread is fundamental to fix a threshold in order to identify possible worrysome situations. This paper deals with the identification of the spread in the modal parameter estimation of one of the grandstands of the so-called 3° ring of the G. Meazza Stadium in Milan, performed analyzing data collected over more than one year. Vibration data have been recorded during different events, such as soccer matches and concerts. The considered data came from a set of sensors similar to that which is to be installed for the permanent monitoring system, to check about the possibility to use the monitoring system as a diagnostic tool for the structure. A study was also carried out to identify critical aspects in the sensors’ choice and their placement, in order to provide useful information about the design of the permanent monitoring system. The presented results can be used to determine confidence intervals out of which changes in the modal properties can be considered anomalous, and so, worthy of being deeply investigated to assess structural integrity.     

Damage Identification on the Tilff Bridge by Vibration Monitoring Using Optical Fiber Strain Sensors

Vibration testing is a well-known practice for damage identification of civil engineering structures. The real modal parameters of a structure can be determined from the data obtained by tests using system identification methods. By comparing these measured modal parameters with the modal parameters of a numerical model of the same structure in undamaged condition, damage detection, localization, and quantification is possible. This paper presents a real-life application of this technique to assess the structural health of the 50-year old bridge of Tilff, a prestressed three-cell box-girder concrete bridge with variable height. A complete ambient vibration survey comprising both vertical accelerations and axial strains has been carried out. The in situ use of optical fiber strain sensors for the direct measurement of modal strains is an original contribution of this work. It is a big step forward in the exploration of modal curvatures for damage identification because the accuracy in calculating the modal curvatures is substantially improved by directly measuring modal strains rather than deriving the modal curvatures from acceleration measurements. From the ambient vibrations, natural frequencies, damping factors, modal displacements and modal curvatures are extracted by the stochastic subspace identification method. These modal parameters are used for damage identification which is performed by the updating of a finite element model of the intact structure. The obtained results are then compared to the inspections performed on the bridge.