Structural Health Monitoring Approach Research Articles

Structural health monitoring of bridges in the State of Connecticut

A joint effort between the Connecticut Department of Transportation and the University of Connecticut has been underway for more than 20 years to utilize various structural monitoring approaches to assess different bridges in Connecticut. This has been done to determine the performance of existing bridges, refine techniques needed to evaluate different bridge components, and develop approaches that can be used to provide a continuous status of a bridge’s structural integrity. This paper briefly introduces the background of these studies, with emphasis on recent research and the development of structural health monitoring concepts. This paper presents the results from three different bridge types: a post-tensioned curved concrete box girder bridge, a curved steel box-girder bridge, and a steel multi-girder bridge. The structural health monitoring approaches to be discussed have been successfully tested using field data collected during multi-year monitoring periods, and are based on vibrations, rotations and strains. The goal has been to develop cost-effective strategies to provide critical information needed to manage the State of Connecticut’s bridge infrastructure.

Towards a practical structural health monitoring technology for patched cracks in aircraft structure

Although bonded composite patches often offer a far more effective repair than conventional mechanically fastened patches, full credit can not be given for their effectiveness in reducing crack growth when used to repair flight safety structure. This is because of the lack of non-destructive techniques to detect bonds with poor long-term integrity and ability to predict the likelihood and rate of patch disbonding. Structural health monitoring (SHM) may overcome this limitation, since patch health can be monitored on a continuous basis. After discussing some of the requirements and options for structural health monitoring of bonded repair patches, a case history is presented on the application of a simple strain-based SHM approach for monitoring the boron/epoxy patch repair of a critical fatigue crack in an F-111C wing. The effectiveness of the strain-based SHM approach is demonstrated, and improvements which would reduce its limitations and raise its practicality to the level (TRL8) where it could be used for in- flight application are discussed. Conventional strain gauges were used in the SHM system which, although found effective, have several limitations. A study on the option of using Bragg grating optical fibres as strain sensors is briefly described. Finally brief details are provided on an alternative longer-term approach and study to use acousto-ultrasonics as the basis of the SHM system.

Autoregressive coefficients based Hotelling’s T2 control chart for structural health monitoring

This paper presents an innovative structural health monitoring (SHM) scheme based on time series analysis and multivariate statistical process control (MSPC) techniques. The scheme consists of two major procedures, viz vibration response data representation and characteristics monitoring. First, a series of autoregressive (AR) models are fitted to the response time histories of a structure to be monitored. Representing the health condition of the structure, the coefficients of these AR models are extracted to form a set of multivariate data known as vibration response data characteristics. Hotelling’s T 2 control chart is then applied to monitor these characteristics obtained. As an MSPC tool, Hotelling’s T 2 control chart has the capacity of simultaneously monitoring the multivariate characteristics data without having to neglect the inherent relation between the components of the data. The efficacy of the proposed SHM scheme is demonstrated by numerically simulated acceleration time histories based on a progressively damaged reinforced concrete (RC) frame, either with or without addressing the autocorrelation in the characteristics data. The results are compared with those obtained by using univariate Shewhart X ¯ control chart to show the advantages of the proposed scheme in terms of the sensitivity of the defined damage indicator with respect to damage severity. A parametric study is also included to investigate the effects of the number of data points used for AR model fitting, the order of AR models, the number and location of sensors on the proposed scheme and to further illustrate its potential as a promising SHM approach.

A Structural Neural System for Real-time Health Monitoring of Composite Materials

A prototype structural neural system (SNS) is tested for the first time and damage detection results are presented in this study. The SNS is a passive online structural health monitoring (SHM) system that mimics the synaptic parallel computation networks present in the human biological neural system. Piezoelectric ceramic sensors and analog electronics are used to form neurons that measure strain waves generated by damage. The sensing of strain waves is similar to the proven nondestructive evaluation (NDE) technique of acoustic emission (AE) monitoring. Fatigue testing of a composite specimen on a four-point bending fiXture is performed, and the SNS is used to monitor the specimen for damage in real time. The prototype SNS used four sensors as inputs, but the number of inputs can be in the tens or hundreds depending on the type of SNS processor used. This is an area of continuing development. The SNS has two channels of signal output that are digitized and processed in a computer. The first output channel tracks the propagation of waves due to damage, and the second output channel provides the combined AE responses of the sensors. The data from these two channels are used to predict the location of damage and to qualitatively indicate the severity of the damage. Overall, this study shows that the SNS can detect damage growth in composites during operation of the structure, and the SNS architecture has the potential to tremendously simplify the AE technique for use in on-board SHM. Ten or more input neurons can be used, and still only two output channels are needed. Two levels of monitoring are possible using the SNS; a coarser SHM approach, or an on-board NDE approach. The SHM approach uses the SNS with a coarse grid of neurons to monitor and detect damage occurring in a general area during operation of the structure. The SNS will indicate where and when a more sensitive inspection is needed which can be done using ground-based NDE techniques. The on-board NDE approach uses the SNS with a fine coverage of neurons for highly sensitive NDE which continuously listens for damage and provides real-time processing and information about any damage in the structure and the performance limits and safety of the vehicle.

A novel approach for the structural identification and monitoring of a full-scale 17-storybuilding based on ambient vibration measurements

For reliable and practical application of structural health monitoring approaches inconjunction with dense sensor arrays deployed on ‘smart’ systems, there is a need todevelop and evaluate alternate strategies for efficient problem decomposition to rapidlyand accurately determine the occurrence, location and level of small changesin the underlying structural characteristics of a monitored system based on itsvibrational signature. Furthermore, there is also a need to quantify the level ofuncertainties in the identified system characteristics so as to have a measurablelevel of confidence in the parameters to be relied on for the detection of genuinechanges (damage) in the monitored system. This study presents the results of twotime-domain identification techniques applied to a full-scale 17-story building, based onambient vibration measurements. The Factor building is a steel frame structurelocated on the UCLA campus. This building was instrumented permanently with adense array of 72-channel accelerometers, and the acceleration data are beingcontinuously recorded. The first identification method used in this study is theNExT/ERA, which is regarded as a global (or centralized) approach, since it dealswith the global dynamic properties of the structure. The second method is atime-domain identification technique for chain-like MDOF systems. Since in thismethod the identification of each link of the chain is performed independently,it is regarded as a local (or decentralized) identification methodology. For thesame reason, this method can be easily adopted for large-scale sensor networkarchitectures in which the centralized approaches are not feasible due to massivestorage, power, bandwidth and computational requirements. To have a statisticallymeaningful results, 50 days of recorded data are considered in this study. Themodal parameter and chain identification procedures are performed over timewindows of 2 h each and with 50% overlap. Using the NExT/ERA method, 12dominant modes of the building were identified. It was observed that variations in thefrequency estimation are relatively small; the coefficient of variation is about 1–2%for most of the estimated modal frequencies. Chain system identification wassuccessfully implemented using the output-only data acquired from the Factorbuilding. Probability distributions of the estimated coefficients of displacement andvelocity terms in the interstory restoring functions (which are the mass-normalizedlocal stiffness and damping values) that were found based on the chain systemidentification are presented. The variability of the estimated parameters due totemperature fluctuations is investigated. It is shown that there is a strong correlationbetween the modal frequency variations and the temperature variations in a 24 hperiod.

An inductive fuzzy damage classification approach for structural health monitoring

Structural health monitoring (SHM) research gained momentum in the last two decades. Early damage detection in infrastructure is the main goal of SHM to enhance structural reliability and safety. SHM has a broader impact by extending service life of structures. A critical part of damage detection is to quantify damage severity in structures. In this article, fuzzy set theory is used in the damage classification process. Using principles of inductive reasoning, fuzzy sets are established to describe damage states in the structure. The proposed method does not rely on a specific damage feature and is applicable to different SHM systems. To demonstrate the ability of the proposed method in damage classification, two case studies are demonstrated. The two cases examine damage detection in a multi-story structure and in a pipeline structure. We establish fuzzy sets based on SHM inducted knowledge and then we implement fuzzy pattern recognition means to identify the unknown states of damage. The efficiency of the proposed method in damage detection is presented.

Use of vibration monitoring data to track structural changes in a retrofitted building

Structural health monitoring (SHM) approaches for condition assessment and damage detection based on vibration signature of target infrastructure systems have received much attention for a long time. However, there is a paucity of studies that evaluate methodologies based on actual vibration measurements, obtained under realistic field conditions. Particularly lacking is the availability of experimental data from physical structures, where quantifiable changes are made in the structure while structural health monitoring studies are being performed. That is precisely the focus of this paper. As a result of the 1994 Northridge earthquake, a critical facility in the metropolitan Los Angeles region was found to need significant seismic mitigation measures. The facility consisted of a six-storey building that housed essential emergency services. 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 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 presents an overview of the ambient vibration data collected before, during, and after the structural retrofit. A finite-element model of the building was developed and used to estimate the dominant frequencies and mode shapes before and after the retrofit. The experimental data are used to determine the spectral characteristics of the ambient response and to compare the experimental measurements with the computational model. 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 scan be extremely useful in calibrating the accuracy and sensitivity of various SHM schemes. Copyright © 2005 John Wiley & Sons, Ltd.

Damage Pattern Recognition for Structural Health Monitoring Using Fuzzy Similarity Prescription

Abstract: Structural health monitoring (SHM) is a systematic method for non-destructive evaluation of a structure's performance by sensing, extracting, patterning, and recognizing features of the structural response. Most SHM approaches focus on statistical analysis for damage identification considering only random uncertainties. This article introduces a method that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness which are statistically non-describable. The proposed method deals primarily with epistemic uncertainty. The method improves damage identification by performing damage pattern recognition using fuzzy sets. In this approach, healthy observations are used to construct a fuzzy set representing healthy performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed. Thus, an optimal group of fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. Piecewise linear functions are used as fuzzy membership functions representing the states of healthy and damaged. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fuzzy pattern recognition based on maximum approaching degree. A case study for damage pattern recognition of a model steel bridge is presented and discussed. The approach is capable of identifying damage patterns accurately.

Support vector regression for on-line health monitoring of large-scale structures

Large-scale, structural health monitoring remains a challenge especially when I/O measurement data are contaminated by high-level noise. A novel approach that uses incremental support vector regression (SVR), a promising statistics technology, is proposed for large-scale, structural health monitoring. Due to the potential properties of this novel SVR, the SVR-based approach makes structural health monitoring accurately and robustly. A sub-structure strategy is utilized to reduce the number of unknown parameters in the health monitoring formula, thereby making large-scale structural health monitoring possible. Lastly, an incremental SVR training algorithm adopted for the SVR-based approach not only markedly reduces computation time, but identifies structural parameters on-line. Numerical examples show that results of this SVR-based approach for large-scale structural health monitoring are accurate and robust, even when observed data are contaminated with different kinds and intensity levels of noise.

Structural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural Networks

Abstract: A pattern recognition approach for structural health monitoring (SHM) is presented that uses damage-induced changes in Ritz vectors as the features to characterize the damage patterns defined by the corresponding locations and severity of damage. Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology.

Damage detection in beams and plates using wavelet transforms

A wavelet based approach is proposed for structural damage detection in beams, plate and delamination of composite plates. Wavelet theory is applied here for crack identification of a beam element with a transverse on edge non-propagating open crack. Finite difference method was used for generating a general displacement equation for the cracked beam in the first example. In the second and third example, damage is detected from the deformed shape of a loaded simply supported plate applying the wavelet theory. Delamination in composite plate is identified using wavelet theory in the fourth example. The main concept used is the breaking down of the dynamic signal of a structural response into a series of local basis function called wavelets, so as to detect the special characteristics of the structure by scaling and transformation property of wavelets. In the light of the results obtained, limitations of the proposed method as well as suggestions for future work are presented. Results show great promise of wavelet approach for damage detection and structural health monitoring.

Estimation approach for structural health monitoring using piezoelectric impedance based measuring method

A truss structure consisting of aluminium beams and L-shape joints was used for investigation of the evaluation of the joint connection. Since the fastening state of bolts affects the mechanical impedance in the joint, near which a pair of PZT patches was utilised and embedded on the structure. Therefore, a high accuracy result on the mechanical impedance variation can be obtained by measurement of the electric impedance change in PZT patches. In the experiment, the joint fastening condition is given by tightening and loosening a bolt with a torque wrench. In order to improve the estimation accuracy whether the joint is in safe or not, two new evaluation indices e, area difference, and ∆S, template shift distance, are proposed by impedance spectrum curve matching method. The experimental results show that damage overlooking can be improved by the indices e and ∆S with aid of the impedance peak amplitude ratio δ and the peak frequency shift ∆F , which were proposed in our previous work.

Identification of stiffness degradation in structures using wavelet analysis

An approach for structural health monitoring (SHM) and damage detection using a wavelet based time-frequency technique is presented here. The characteristics of dynamic responses of structures are studied by wavelet analysis. Inferences are drawn from the wavelet coefficients obtained. Numerical simulation data are used for the purpose of illustration. Two examples (i) a bi-linear structure with breakable springs causing stiffness degradation and (ii) a hysteretic system with continuous variation in stiffness with non-linearity involving displacement and velocity response are considered. Information regarding the stiffness degradation and temporal location of damage are successfully obtained from the wavelet coefficients of the response. The effect of system degradation is reflected in the time-frequency characteristics of the response and inferences on stiffness degradation can be obtained even without any apriori knowledge of the original structural system.

New Bayesian Model Updating Algorithm Applied to a Structural Health Monitoring Benchmark

A new two-step approach for probabilistic structural health monitoring is presented, which involves modal identification followed by damage assessment using the pre and post-damage modal parameters based on a new Bayesian model updating algorithm. The new approach aims to attack the structural health monitoring problems with incomplete modeshape information by including the underlying full modeshapes of the system as extra random variables, and by employing the Expectation-Maximisation algorithm to determine the most probable parameter values. The non-concave non-linear optimisation problem associated with incomplete modeshape cases is converted into two coupled quadratic optimisation problems, so that the computation becomes simpler and more robust. We illustrate the new approach by analysing the Phase II Simulated Benchmark problems sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. The results of the analysis show that the probabilistic approach is able to successfully detect and locate the simulated damage involving stiffness loss in the braces of the analytical benchmark model based on simulated ambient-vibration data.

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.

Preface to the Special Issue on Phase I of the IASC-ASCE Structural Health Monitoring Benchmark

The importance of developing robust monitoring systems that can detect and locate progressive deterioration in structures or abrupt damage induced by extreme loading events is well recognized in the aerospace, mechanical, and civil engineering communities. In the case of civil structures such as buildings, bridges, off-shore platforms, or dams, the most commonly used approach for structural health monitoring ~SHM! relies on visual inspections that are labor intensive, often not timely, and may miss hidden damage. A good example of these limitations is found in the fractures induced by the 1994 Northridge earthquake in welded momentresisting beam-column connections in many steel buildings that were not discovered until several months after the earthquake because there was no distress apparent from visual inspections. An alternative to visual inspections and localized testing that is being aggressively investigated in various engineering disciplines is that of structural health monitoring based on vibration signals. A typical SHM system consists of a distributed set of motion sensors ~e.g., accelerometers, velocimeters, or fiber-optic strain gauges! connected through a data acquisition system to a central processing unit that may be in an operator room. For remote real-time monitoring of structures, the processed data must be on-line, possibly through a dedicated line or via an Internet link. The basic idea is well established, namely, the dynamic characteristics of a system ~usually natural frequencies and mode shapes! that can be identified from recorded motions are a function of the physical parameters ~mass and stiffness distributions!. Therefore, changes in physical properties resulting from damage may be inferred from changes in the identified dynamic characteristics using suitable algorithms. Then, in theory, damage may be detected, localized, and assessed through vibration monitoring. In practice, however, realization of the concept has proved to be a very challenging proposition because the inverse problem that must be solved, for typical operating conditions, is poorly conditioned, that is, results are sensitive to measurement noise and modeling errors. An important part of the current effort to affect positive progress in SHM technology in civil engineering structures is the development of well-defined benchmark problems that allow comparison of the performance of various techniques for realistic conditions. This special issue of the Journal of Engineering Mechanics contains eight papers that define and study a benchmark developed by the Task Group on Structural Health Monitoring that was formed in 1999 under the sponsorship of the Dynamics Committee of ASCE and the International Association of Structural Control ~IASC!. These papers expand on some partial and

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.

Some structural health monitoring approaches for nonlinear hydraulic dampers

AbstractViscous dampers are integral components in the implementation of structural control retrofit strategies on several new or retrofitted bridges throughout the world. Design engineers have realized the economic and engineering value of dissipating the large anticipated forces from wind and seismic loads. Owing to the significance of the role played by the dampers in the retrofit schemes, it is imperative that health monitoring strategies be investigated. Health monitoring procedures provide potential capabilities to continuously evaluate the integrity of components. The goals of the study reported herein were to conduct some analytical and experimental studies to evaluate a promising strategy for structural health monitoring of nonlinear viscous dampers. A nonparametric identification technique previously developed to handle a very broad class of nonlinear problems usually encountered in the structural dynamics field is used in conjunction with noise‐polluted synthetic data to assess the sensitivity of the proposed approach to detect slight changes in the characteristics of a nonlinear viscous damper under broadband excitation. By inspecting changes in the probability density function of the identified dominant system parameters, it is shown that the proposed structural health monitoring approach is capable of detecting small changes, even in the presence of a modest amount of noise pollution. Preliminary experimental results are also presented from a realistic laboratory model to illustrate the application of the monitoring scheme. Copyright © 2002 John Wiley & Sons, Ltd.

Wavelet-Based Approach for Structural Damage Detection

A wavelet-based approach is proposed for structural damage detection and health monitoring. Characteristics of representative vibration signals under the wavelet transformation are examined. The methodology is then applied to simulation data generated from a simple structural model subjected to a harmonic excitation. The model consists of multiple breakable springs, some of which may suffer irreversible damage when the response exceeds a threshold value or the number of cycles of motion is accumulated beyond their fatigue life. In cases of either abrupt or accumulative damages, occurrence of damage and the moment when it occurs can be clearly determined in the details of the wavelet decomposition of these data. Similar results are observed for the real acceleration data of the seismic response recorded on the roof of a building during the 1971 San Fernando earthquake. Effects of noise intensity and damage severity are investigated and presented by a detectability map. Results show the great promise of the wavelet approach for damage detection and structural health monitoring.

A Digital Mems-based Strain Gage for Structural Health Monitoring

ABSTRACTOne approach for structural health monitoring of aging aircraft is to take discrete airframe strain measurements and record the flight loads history. A complementary method consists of measuring changes in dynamic response due to fatigue crack growth. The challenge in implementing such methods is the need for inexpensive networks of distributed strain sensors which possess high resolution with no drift over time. The Uni-Axial Strain Transducer (UAST) has been developed as a digital, absolute encoding device to address these very issues. The UAST is a micro-electromechanical system (MEMS) which exploits the capacitive coupling between an array of electrostatic field emitters and an array of 64 field detectors on a CMOS IC chip. The slightly different array element spacings form a vernier scale and digital signal processing of the detector outputs is used to calculate the absolute translational displacement of the emitter array relative to the CMOS detector chip. The UAST provides a dynamic range of 11,500 μ-strain and displacements of 2.5 nm are easily resolved. The sensor sampling rate is dynamically configurable for 150, 290, 540, 1000, 1600 or 2500 Hz, providing 15, 14, 13, 12, 11, or 10 bits of resolution (equal to 0.35, 0.7, 1.4, 2.8, 5.6, or 11.4 μ-strain), respectively. The sensor network can communicate with up to 128 UASTs on a common 5-wire digital bus, eliminating the need for shielding and considerably reducing the number of wires which will have to be routed through the airframe. A network technology demonstration is being conducted on a 1/2 scale F-I 8 vertical tail where dynamic loads are applied to evaluate network performance related to monitoring of fatigue crack growth or rivet-line failures. Application of the UAST in a helicopter rotor health usage and monitoring system, and the design of a bi-axial transducer under development, are also mentioned.