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Temperature Robust Health Bench-Marking and Monitoring of an Heritage Suspension Bridge Using Coupled IWCM and TBSI Method
Abstract
Bridges play a crucial role in transportation across water bodies, junctions, etc., minimizing the distance and traffic in a route. With ageing and continuous use, the bridges deteriorate and may even collapse if not monitored. With the gradual deterioration of its health, the dynamic properties alter with time. This damage-sensitive feature is therefore exploited by several techniques for damage quantification, and localization, which is in general categorized as vibration-based structural health monitoring (VBSHM). However, these features also change in an environment of varying temperature quite substantially that may mask the damage effect. This eventually entails inclusion of temperature information within the SHM procedure. This study undertakes Temperature-Based Structural Identification (TBSI) to locate and quantify structural faults in terms of fixity damage, material degradation, etc. Further, to dilute the effect of ambient noises and bias errors in the estimation procedure, Iterative Windowed Curve Fitting Method (IWCM) has been opted that supplies cleaner modal domain information for the TBSI to work. The investigation is undertaken simultaneously through numerical as well as real experimentation.KeywordsIWCMTBSISHMFinite element modelling
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Temperature-Based Structural Identification (TBSI) is a quantitative structural evaluation approach that relies on responses resulting from temperature fluctuations. Through this approach the transfer function that defines how thermal induced strains give rise to global displacements and restrained member forces can be captured. This input-output relationship is highly sensitive to mechanisms that pose modeling challenges, such as boundary and continuity conditions, and thus is quite valuable within the model updating process. The method follows the traditional structural identification (St-Id) framework with a-priori modeling, experimentation, and model calibration steps appropriately modified to allow for the measurement and simulation of temperature-induced responses. TBSI was evaluated through the use of simulations and laboratory experiments, and then implemented to identify an arch bridge. In addition, a comparative study was performed with an independent St-Id of the same bridge using ambient vibration testing (AVSI). The results indicate that TBSI and AVSI are synergistic providing complementary information related to a diverse range of structural performances. In addition, the results illustrate several TBSI strong-points, including (1) the ability to identify both linear and nonlinear behaviors, (2) the ability to efficiently capture response patterns with long periods, and (3) a strong correlation between the captured transfer function and the behavior of boundary and continuity conditions.
The Land Transport Authority of Singapore has a continuing program of highway bridge upgrading, to refurbish and strengthen bridges to allow for increasing vehicle traffic and increasing axle loads. One subject of this program has been a short span bridge taking a busy main road across a coastal inlet near a major port facility. Experiment-based structural assessments of the bridge were conducted before and after upgrading works including strengthening. Each assessment exercise comprised three separate components; a strain and acceleration monitoring exercise lasting approximately one month, a full-scale dynamic test carried out in a single day without closing the bridge and a finite element model updating exercise to identify structural parameters and mechanisms. This paper presents the dynamic testing and the modal analysis used to identify the vibration properties and the quantification of the effectiveness of the upgrading through the subsequent model updating. Before and after upgrade, similar sets of vibration modes were identified, resembling those of an orthotropic plate having relatively weak transverse bending stiffness. Conversion of bearings from nominal simple supports to nominal full fixity was shown via model updating to be the principal cause of natural frequency increases of up to 50%. The utility of the combined experimental and analytical process in direct identification of structural properties has been proven and the procedure can be applied to other (e.g. larger) structures and their capacity assessments.
When using the analysis of vibration measurements as a tool for health monitoring of bridges, the problem arises of separating abnormal changes from normal changes in the dynamic behaviour. Normal changes are caused by varying environmental conditions such as humidity, wind and most important, temperature. The temperature may have an impact on the boundary conditions (frozen soil) and the Young's modulus of the material of which the structure consists. Abnormal changes on the other hand are caused by a loss of stiffness somewhere along the bridge. It is clear that the normal changes should not raise an alarm in the monitoring system (i.e. a false positive), whereas the abnormal changes may be critical for the structure's safety. This paper tries to give an answer to the question whether it is possible to separate the environmental influences from damage events. In the frame of the European SIMCES-project, the Z24-bridge in Switzerland was monitored during almost one year before it was ...
Extensive experimental investigations were conducted on the Golden Gate Bridge in San Francisco, California, to determine, using ambient vibration data, parameters of major interest in both wind and earthquake problems, such as effective damping, the three-dimensional mode shapes, and the associated frequencies of the bridge vibration. The paper deals with the tests that involved the simultaneous measurement of vertical, lateral, and longitudinal vibration of the suspended structure; a subsequent paper addresses the measurement of the tower vibration. Measurements were made at selected points on different cross sections of the stiffening structure: 12 were on the main span and 6 on the side span. Good modal identification was achieved by special deployment and orientation of the motion-sensing accelerometers and by summing and subtracting records to identify and enhance vertical, torsional, lateral, and longitudinal vibrational modes. In all, 91 modal frequencies and modal displacement shapes of the suspended span were recovered: 20 vertical, 18 torsional, 33 lateral, and 20 longitudinal, all in the frequency range 0.0-1.5 Hz. These numbers include symmetric and antisymmetric modes of vibration. Finally, comparison with previously computed two- and three-dimensional mode shapes and frequencies shows good agreement with the experimental results, thus confirming both the accuracy of the experimental determination and the reliability of the methods of computation.
Structural identification is a powerful concept that permits rational characterizations of constructed facilities and their loading environments. Potential uses of structural identification to solve numerous problems related to condition assessment of civil infrastructure are proposed. Definitions and technology related to the concept are formulated, The experimental, analytical, and information tools that are needed for a successful structural identification of a constructed facility are discussed. Experimental techniques that permit effective structural identification applications to highway bridges have been validated and demonstrated on an operating steel-stringer highway bridge serving as a lest bed. Experiences gained during the applications of different forms of modal analysis and instrumented monitoring are presented with example experimental data and results. Hardware, software, and design requirements for field experiments are discussed.
The parametric approach to eliminating the temperature-caused modal variability in vibration-based structural damage detection requires a correlation model between the modal properties and environmental temperatures. This paper examines the generalization capability of neural network models, established using long-term monitoring data, for correlation between the modal frequencies and environmental temperatures. A total of 770 h modal frequency and temperature data obtained from an instrumented bridge are available for this study, which are further divided into three sets: training data, validation data, and testing data. A two-layer back-propagation neural network (BPNN) is first trained using the training data by the conventional training algorithm, in which the number of hidden nodes is optimally determined using the validation data. Then two new BPNNs are configured with the same data by applying the early stopping technique and the Bayesian regularization technique, respectively. The reproduction and prediction capabilities of the two new BPNNs are examined in respect of the training data and the unseen testing data, and compared with the performance of the baseline BPNN model. This study indicates that both the early stopping and Bayesian regularization techniques can significantly ameliorate the generalization capability of BPNN-based correlation models, and the BPNN model formulated using the early stopping technique outperforms that using the Bayesian regularization technique in both reproduction and prediction capabilities.
A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.
In this study, the construction of appropriate input to neural networks for modeling the temperature-caused modal variability is addressed with intent to enhance the reproduction and prediction capabilities of the formulated correlation models. Available for this study are 770 h modal frequency and temperature data that were obtained from the instrumented cable-stayed Ting Kau Bridge in Hong Kong. With the temperature data measured at different portions of the bridge, three kinds of input, i.e., mean temperatures, effective temperatures, and principal components (PCs) of temperatures, are constructed as input to neural networks for modeling the correlation between the modal frequencies and environmental temperatures. By dividing the 770 h modal frequency and temperature data into training data set, validation data set and testing data set, an optimally configured back-propagation neural network (BPNN) is formulated for each kind of input, in which the validation data are utilized to determine the optimal number of hidden nodes while the early stopping technique is applied to optimize the BPNN parameters. Then the reproduction and prediction performance of the BPNNs configured with the three kinds of input is examined and compared in respect of the seen training data set and the unseen testing data set, respectively. It is revealed that the temperature profile characterized by the effective temperatures is insufficient for formulating a good correlation model between the modal frequencies and temperatures. When a sufficient number of PCs are used, the BPNN with input of the PCs of temperatures performs better than the BPNN with input of the mean temperatures in both reproduction and prediction capabilities.
The problem of determining linear models of structures from seismic response data is investigated using ideas from the theory of system identification. The approach is to determine the optimal estimates of the model parameters by minimizing a selected measure-of-fit between the responses of the structure and the model. Because earthquake records are normally available from only a small number of locations in a structure, and because of noise in the records, it is necessary in practice to estimate parameters of the dominant modes in the records, rather than the stiffness and damping matrices of the linear model. A new algorithm is developed to determine the optimal estimates of the modal parameters. After tests with simulated data, the method is applied to a multi-storey building using records from the 1971 San Fernando earthquake in California. New information is obtained concerning the properties of the lower modes of the building and the time-varying character of the equivalent linear parameters.
Damage detection techniques have been proposed to exploit changes in modal parameters and to identify the extent and location of damage in large structures. Most of such techniques, however, generally neglect the environmental effects on modal parameters. Such environmental effects include changes in loads, boundary conditions, temperature, and humidity. In fact, the changes due to environmental effects can often mask more subtle structural changes caused by damage. This paper examines a linear adaptive model to discriminate the changes of modal parameters due to temperature changes from those caused by structural damage or other environmental effects. Data from the Alamosa Canyon Bridge in the state of New Mexico were used to demonstrate the effectiveness of the adaptive filter for this problem. Results indicate that a linear four-input (two time and two spatial dimensions) filter to temperature can reproduce the natural variability of the frequencies with respect to time of day. Using this simple model, we attempt to establish a confidence interval of the frequencies for a new temperature profile in order to discriminate the natural variation due to temperature. Copyright © 1999 John Wiley & Sons, Ltd.
Interest in the ability to monitor a structure and detect damage at the earliest possible stage is pervasive throughout the civil, mechanical and aerospace engineering communities. Significant work has been done in the formulation of vibration-bas ed damage detection algorithms, but unfortunately, investigations studying the variability of dynamic properties caused by changing environmental and operational conditions have been lacking. A thorough understanding of this variability is necessary so that changes in vibration response resulting from damage can be discriminated from changes resulting from such variability. In this paper the variability in modal properties of the Alamosa Canyon Bridge in southern New Mexico will be discussed.
Thesis (Ph. D.)--University of Bristol, Jun 2000.