Structural Health Monitoring Data Research Articles

Multiclass anomaly detection in imbalanced structural health monitoring data using convolutional neural network

Structural health monitoring (SHM) system aims to monitor the in-service condition of civil infrastructures, incorporate proactive maintenance, and avoid potential safety risks. An SHM system involves the collection of large amounts of data and data transmission. However, due to the normal aging of sensors, exposure to outdoor weather conditions, accidental incidences, and various operational factors, sensors installed on civil infrastructures can get malfunctioned. A malfunctioned sensor induces significant multiclass anomalies in measured SHM data, requiring robust anomaly detection techniques as an essential data cleaning process. Moreover, civil infrastructure often has imbalanced anomaly data where most of the SHM data remain biased to a certain type of anomalies. This imbalanced time-series data causes significant challenges to the existing anomaly detection methods. Without proper data cleaning processes, the SHM technology does not provide useful insights even if advanced damage diagnostic techniques are applied. This paper proposes a hyperparameter-tuned convolutional neural network (CNN) for multiclass imbalanced anomaly detection (CNN-MIAD) modelling. The hyperparameters of the proposed model are tuned through a random search algorithm to optimize the performance. The effect of balancing the database is considered by augmenting the dataset. The proposed CNN-MIAD model is demonstrated with a multiclass time-series of anomaly data obtained from a real-life cable-stayed bridge under various cases of data imbalances. The study concludes that balancing the database with a time shift window to increase the database has generated the optimum results, with an overall accuracy of 97.74%.

Life-Cycle Assessment of Long-Span Bridge’s Wind Resistant Performance Considering Multisource Time-Variant Effects and Uncertainties

This paper examines the life-cycle wind resistant performance of a constructed long-span suspension bridge in the coastal region of China, aiming to quantify the multisource time-variant effects and uncertainties and offering a reference for designs of long-span bridges in the future. Randomness from modal frequencies, damping ratios, and identification uncertainty of flutter derivatives (FDs) was considered; then, their effects on probability of flutter failure and probability of exceeding the predefined buffeting response root-mean square (RMS) are discussed. Firstly, results of full-track tropical cyclone (TC) simulation under various climate warming scenarios are reviewed; then, the time-variant probability density function (PDF) of annual extreme wind speed is discussed. Secondly, 6-year modal frequencies and damping ratios of a long-span suspension bridge with a center-slotted section were extracted by fast Bayesian FFT method with structural health monitoring (SHM) data, which were utilized to explore the deterioration rules of structural properties. Thirdly, FDs were modeled from a probabilistic perspective based on complex Wishart distribution, which were identified in the turbulent flow and the frequency domain by Bayesian inference. The posterior distributions of FDs, namely identification uncertainty, were quantified by Markov chain Monte Carlo (MCMC) sampling. This paper finds that for flutter resistant performance, the time-variant effects (i.e., modal frequencies and PDFs of extreme wind speed) will make the flutter failure probability seven times larger than the initial value; for the probability of exceeding the predefined buffeting response RMS, however, the time-variant effects will make a negligible difference.

Response Prediction Based on Temporal and Spatial Deep Learning Model for Intelligent Structural Health Monitoring

Machine learning models have recently demonstrated the ability to mine structural health monitoring data. While existing machine learning models can provide better performance than the previous univariate time-series model, it is still an open challenge of fully mining the spatial and temporal characteristics of the structural response to increase their accuracy. In addition, the heterogeneous correlation is always missed in traditional models. This article proposes a heterogeneous structural response prediction (HSRP) framework based on the deep learning model to improve the performance. The HSRP framework cannot only make full use of spatial and temporal correlations but also mine the correlation between heterogeneous responses. Motivated by recent studies in machine learning, an attention module is introduced to learn the correlation between different responses and initial the weights of sensors and past response. The convolutional neural network is also implemented to extract the spatial features and the long short-term memory network is used to extract the weekly, daily, and hourly patterns of structural response. A real-world data set collected from a bridge is used to evaluate the performance of the proposed model on single-step prediction and multistep prediction. The experimental results show that the proposed model outperforms several widely used benchmark models. Furthermore, additional experiments and evaluations are implemented to investigate the sensitivity and robustness of the proposed model.

Monitoring abnormal vibration and structural health conditions of an in-service structure from its SHM data

Vortex-induced vibration occurred recently in several important engineering structures in China. Very large mono-frequency periodic vibration was noted. This study reports on the analysis of recorded data from the structural health monitoring (SHM) system of a suspension bridge with the aim to develop some indicators that may be useful to evaluate if there is any abnormal vibration or change of health conditions of a structure in future. The power spectrum of measured acceleration responses is plotted in three-dimensions of time, frequency and power. Acceleration responses from 16 sensors on the target bridge deck in 233 days are analyzed. The evolution of different dynamic parameters in the period encompassing the occurrences of large vortex-induced vibration are reported. The changes in these parameters are identified to be closely associated with some temporary installations on the deck and temporary measures for vibration suppression. The changes in vibration power at different natural frequencies of the structure can be used to demonstrate the evolution of the unexpected event. The trend and abrupt changes of natural frequencies and modal components show the flow of vibration energy between different vibration modes. In addition to the intuitive three-dimension power spectral plot, two state vectors are also developed from the power spectrum. The identification of abnormal vibration and structural state can then be carried out automatically with the novelty detection method and a threshold. The analysis of the SHM acceleration responses of the bridge deck demonstrates the implementation procedure and the effectiveness of the proposed method.

Detection of Partially Structural Collapse Using Long-Term Small Displacement Data from Satellite Images.

The development of satellite sensors and interferometry synthetic aperture radar (InSAR) technology has enabled the exploitation of their benefits for long-term structural health monitoring (SHM). However, some restrictions cause this process to provide a small number of images leading to the problem of small data for SAR-based SHM. Conversely, the major challenge of the long-term monitoring of civil structures pertains to variations in their inherent properties by environmental and/or operational variability. This article aims to propose new hybrid unsupervised learning methods for addressing these challenges. The methods in this work contain three main parts: (i) data augmentation by the Markov Chain Monte Carlo algorithm, (ii) feature normalization, and (iii) decision making via Mahalanobis-squared distance. The first method presented in this work develops an artificial neural network-based feature normalization by proposing an iterative hyperparameter selection of hidden neurons of the network. The second method is a novel unsupervised teacher–student learning by combining an undercomplete deep neural network and an overcomplete single-layer neural network. A small set of long-term displacement samples extracted from a few SAR images of TerraSAR-X is applied to validate the proposed methods. The results show that the methods can effectively deal with the major challenges in the SAR-based SHM applications.

Damping ratio identification using attenuation responses extracted by time series semantic segmentation

Damping ratio is an essential dynamic characteristic of bridge structures, and its properties have still not been well understood due to the difficulties in accurate calculation with structural health monitoring (SHM) data. It is widely accepted that the exponential attenuation (EA) method using free attenuation responses is a reliable way to calculate damping ratio. To identify the damping ratio from SHM data with EA method, a time series semantic segmentation method is proposed in this paper to extract free attenuation segments from the massive data of monitored acceleration responses. A labeled semantic dataset is firstly constructed based on the long-term monitoring data of the Z24 bridge which is a continuous bridge widely used as a benchmark model for SHM, and the labeling standard is introduced to illustrate the preference of selecting desirable attenuation segments. Then an adapted U-net model is developed and trained with the constructed dataset, and key hyper-parameters including the configuration of convolutional kernel and decision boundary are investigated and determined to meet the requirement of processing time series data of acceleration responses. A large number of attenuation segments have then been extracted by the well-trained model, based on which the damping ratio of the 1st vibration mode in 10 months is obtained with the EA method. The identified damping ratios are found reliable as compared with those identified by the stochastic subspace identification method, thus enabling further investigation of the properties of damping ratio. Finally, the influence of the temperature variation is explored, revealing an obvious correlation relationship between the temperature and the damping ratio. The results of this study not only provide a novel method to calculate damping ratio with SHM data but also could deepen the understanding of damping ratio properties and serve as a reference for more reasonable utilization of the damping ratio in structural dynamic analysis or condition assessment.

Leveraging Structural Health Monitoring Data Through Avatars to Extend the Service Life of Mass Timber Buildings

Mass timber construction systems, incorporating engineered wood products as structural elements, are gaining acceptance as a sustainable alternative to multi-story concrete or steel-frame structures. The relative novelty of these systems brings uncertainties on whether these buildings perform long-term as expected. Consequently, several structural health monitoring (SHM) projects have recently emerged to document their behavior. A wide and systematic use of this data by the mass timber industry is currently hindered by limitations of SHM programs. These limitations include scalability, difficulty of data integration, diverse strategies for data collection, scarcity of relevant data, complexity of data analysis, and limited usability of predictive tools. This perspective paper envisions the use of avatars as a Web-based layer on top of sensing devices to support SHM data and protocol interoperability, analysis, and reasoning capability and to improve life cycle management of mass timber buildings. The proposed approach supports robustness, high level and large-scale interoperability and data processing by leveraging the Web protocol stack, overcoming many limitations of conventional centralized SHM systems. The design of avatars is applied in an exemplary scenario of hygrothermal data reconstruction, and use of this data to compare different mold growth prediction models. The proposed approach demonstrates the ability of avatars to efficiently filter and enrich data from heterogeneous sensors, thus overcoming problems due to data gaps or insufficient spatial distribution of sensors. In addition, the designed avatars can provide prediction or reasoning capability about the building, thus acting as a digital twin solution to support building lifecycle management.

A change-point detection method for detecting and locating the abrupt changes in distributions of damage-sensitive features of SHM data, with application to structural condition assessment

Damage detection or structural condition assessment is an important objective of structural health monitoring (SHM). The damages or adverse changes in structural conditions can usually be manifested as pattern changes in damage-sensitive features (DSFs) extracted from SHM data; this enables us to shift damage detection to DSF change detection. Online monitoring can accumulate huge amounts of data, finding the changes from the massive DSF data through manual inspection is impractical; thus, automatic detection tools are required. If possible, relevant significance test is also desired to make a rational judgment on the existence of a change. In this sense, the change-point detection technique is an attractive choice, which is increasingly proved to be a powerful change detection tool in various SHM applications. However, existing change-point detection methods in SHM are mainly used for scalar or vector data, thus incapable of detecting changes in features represented by complex data, for example, the probability density functions (PDFs). Detecting abrupt changes in the distributions (represented by PDFs) of the DSF data is of crucial concern in structural condition assessment. However, relevant automatic diagnostic tools have not been well developed in the SHM community. To this end, a novel change-point detection method is developed in the functional data analytic framework for this task. The proposed approach has advantages in detecting changes for massive data and directly handling general PDFs. Considering that the major challenge in PDF-valued data analysis comes from the nonlinear constraints of PDFs, the PDFs are embedded into the Bayes space to develop the detection methodology by using the linear structure of the Bayes space. Comprehensive simulation studies are conducted to validate the effectiveness of the proposed method as well as demonstrate its superiority over the competing method. Finally, a case study involving cable condition assessment of a long-span bridge demonstrates its practical utility in SHM.

An MPPCA-based approach for anomaly detection of structures under multiple operational conditions and missing data

Structural anomaly detection based on the structural health monitoring (SHM) data has attracted significant attention owing to its important role in the early warning of structural damage to existing civil structures. Data-driven approaches, where damage-sensitive features are extracted directly from the SHM data using statistical pattern recognition (SPR) techniques without physical models of structures, have been widely studied. Principal component analysis (PCA) and probabilistic PCA (PPCA) are powerful and efficient SPR methods for linear or weakly nonlinear cases. However, some special structures may be subjected to multiple operational conditions, wherein structural configurations such as geometry and mass distribution may change due to the movement of parts or the whole structure, as in retractable roof structures. These changes may give erroneous results in the SPR of the SHM data and eventually in the anomaly detection by a single PCA or PPCA model. This paper presents an improved approach using a mixture of probabilistic principal component analysis (MPPCA) for the anomaly detection of structures under multiple operational conditions with missing measurement data. First, the baseline MPPCA model was constructed for stress data collected under healthy conditions, where the estimation of the MPPCA parameters was reformulated for the missing data cases. Second, three anomaly statistics were presented for newly monitored incomplete data to detect and localize structural anomalies. The probability distributions of the anomaly statistics were estimated to obtain thresholds for outlier detection. Finally, the effectiveness of the MPPCA-based method was investigated by applying the method to the anomaly detection of a retractable roof structure with numerically simulated and real monitored stress data.

Multi-index probabilistic anomaly detection for large span bridges using Bayesian estimation and evidential reasoning

To measure uncertainties within anomaly detection and distinguish sensor faults from anomalous events, a multi-index probabilistic anomaly detection approach is proposed for large span bridges based on Bayesian estimation and evidential reasoning. To avoid false detection raised by signal spikes, an energy index is first defined and extracted from pre-processed measurements, including missing data recovery and thermal response separation. Then, a probabilistic index, namely, certainty degree, is derived from probability density functions of detection triggers – extreme values predicted by using Bayesian estimation of the generalized Pareto distribution. To distinguish sensor faults from anomalous scenarios, evidential reasoning is applied to incorporate multiple certainty degrees into a joint one under the assumption that the probability of multi-sensor failing simultaneously is extremely low. Specifically, a large joint certainty degree indicates a high occurrence probability of anomalous scenarios, while a small one together with a large individual certainty degree depicts a high probability of sensor faults. Finally, the effectiveness of the proposed anomaly detection method is validated through structural health monitoring data from the Nanjing Dashengguan Yangtze River Bridge. Measurements from four sensors, that is, three cable forces and one deflection, are selected to detect anomalies based on their high pair-wise correlations. Two case studies are presented, namely, sensor fault detection and snow disaster detection. The sensor fault is detected through a certainty degree of almost 100% for the individual index and a joint certainty degree of nearly 0. The snowstorm is detected by a joint certainty degree of 36.82%.

Deep learning-based indirect bridge damage identification system

With the growing number of well-aged bridges and the urgency in developing reliable, (pseudo-) real-time monitoring of structural safety and integrity, there is a worldwide and widespread campaign toward transforming structural health monitoring practice. Among these attempts, the application of data-driven approaches in developing damage identification techniques has received particular attention in recent years. Given the growing volume of structural health monitoring data, the power of data-driven approaches has been further exploited. These efforts have been predominantly focused on building and training algorithms using direct measurements from bridges. Although recent years have seen transformative technologies in producing cheap and wireless sensors, network-wide bridge instrumentation is logistically difficult and expensive. This has led to a new group of structural health monitoring systems entitled indirect or drive-by approaches. In drive-by systems, measurements from an instrumented vehicle are used to extract structural damage signatures. In other words, in these systems, the instrumented vehicle acts as both actuator and receiver while passing over a bridge. The main challenge in deploying drive-by approaches for damage identification purposes is that the signals collected on drive-by vehicles also embody signatures from the vehicle, road/rail profile and are easily contaminated by environmental and operational conditions. Furthermore, the majority of current drive-by damage identification systems rely on prior knowledge of vehicle or bridge dynamic characteristics which has led to limited application of the concept in practice so far. To address these challenges, this study employs a powerful class of deep learning algorithm to develop a damage identification system using measurements on an instrumented travelling train. The proposed algorithm is capable of automatically extracting damage signatures from train-borne measurements only. To demonstrate the algorithm’s capability, the method is applied to measurements collected on a model instrumented train travelling on a simply supported model steel bridge. For this purpose, a deep convolutional neural network is built, optimised, trained and tested to detect damage using acceleration signals collected on the instrumented train only. The hyperparameters of the algorithm are optimised using the Bayesian optimisation technique. The accuracy of the algorithm is experimentally tested for four positive damage scenarios (combination of two different locations and intensity) and three different travelling speeds. This is the first demonstration of the data-driven drive-by damage identification system under scaled operational environment conditions. The performance of the proposed method is discussed under different travelling speeds and different damage states. The result shows that the proposed method can accurately and automatically detect and classify damage under varying speed, rail irregularities and operational noise using train-borne measurements only and offers a great promise in transforming the future of bridge damage identification system.

Autoencoder and Mahalanobis distance for novelty detection in structural health monitoring data of an offshore wind turbine.

Structural Health Monitoring (SHM) has seen an explosion in data gathering in the last few years. This is illustrated in the offshore wind industry through an increase in the amount of placed offshore wind turbines (OWT), a higher rate of SHM instrumented OWTs and an increase in the sampling rate. The growing data gathering has led to the interest of big data techniques in the SHM industry. This paper introduces a new more robust unsupervised novelty detection pipeline combining an autoencoder and the Mahalanobis distance and comparing this combination to both methods separately. This reduces the high dimensional input to a one dimensional novelty index for detecting anomalies in the OWT. Additionally the research considers the challenges that the downtime of non operationally essential sensors poses, a method is introduced to guarantee high model availability without losing the benefit of high fidelity.

Bridge deformation prediction based on SHM data using improved VMD and conditional KDE

Deformation is a paramount index of bridge health monitoring. Accurate prediction of bridge deformation is of great significance to evaluate bridge performance. However, owing to the complex mechanical mechanism of bridge performance evolution, it is arduous to obtain a satisfactory forecasting result by simple data-driven methods. To this end, this paper proposes an innovative data-driven method based on an improved variational mode decomposition (IVMD) and conditional kernel density estimation (CKDE). Specifically, the raw deformation data are firstly pretreated by IVMD. This newly developed technique could not only adaptively optimize the decomposition level number through Hilbert transform and empirical mode decomposition, but also hinder the disturbance of irrelevant components by Kullback-Leibler divergence and Gram-Schmidt orthogonal. Then, auto-regression integrated moving average model is established to excavate the linear feature hidden in the data. Finally, CKDE in conjunction with Gaussian distribution assumption is designed to describe the above modeling errors, by which both point and interval predictions are generated. Obviously, this method avoids exploring the complex internal mechanism of structural behavior evolution. Three case studies based on structural health monitoring data are used to systematically evaluate its effectiveness. The results manifest that this method possesses higher reliability in comparison with the other concerned models, and could lay a foundation for early warning purpose.

Investigation of Time-Varying Cable Tension of Bridges Using Time-Frequency Reassignment Techniques Based on Structural Health Monitoring Data

Cables have been increasingly utilized in modern long-span or tied-arch bridges as the main bearing structures. Real-time identification of time-varying cable tension is essential for assessing the service performance of bridges. Vibration-based methods have been an increasing research focus in recent decades. However, a long time interval is needed to estimate structural frequency using vibration-based methods, increasing the calculating time of cable tension. The time-varying cable tension is thus difficult to extract. This study proposes a time-frequency reassignment-based algorithm to reduce the detection time to address this issue. Combined with a time-frequency analysis tool and vibration theory of cables, the algorithm can identify the time-varying frequency and further quickly calculate the time-varying cable tension within 12.8 s. The features of the proposed algorithm are mainly threefold: identifying the time-varying frequencies with high precision; without some prior knowledge of vibration; having no other requirements for sensor modes. Moreover, the experimental validation is conducted using a quasi-static loading in a workshop and a dynamic field test on Sutong Bridge, respectively. The results show that the proposed algorithm can be used to identify time-varying tension and assess the service performance of cables, providing a new path for real-time condition monitoring of bridges in service.

Bayesian dynamic linear model framework for structural health monitoring data forecasting and missing data imputation during typhoon events

A Bayesian dynamic linear model (BDLM) framework for data modeling and forecasting is proposed to evaluate the performance of an operational cable-stayed bridge, that is, Ting Kau Bridge in Hong Kong, by using SHM strain field data acquired. One of the major challenges in dealing with the existing in-service bridge under extreme typhoon loads is to forecast structural behavior using the typhoon response exhibiting non-stationarity, large data fluctuations and strong randomness. The first attempt for SHM data modeling during extreme events, that is, typhoons, using BDLM framework, was conducted in this study. The data from multiple sensors are analyzed for one-step, multi-step ahead forecasting and missing data imputation. The overall bridge behavior is incorporated into a forecasting model by superposition of forecasting results of trend (representing the structural baseline response), periodic component (response component evolving regularly over time), and autoregressive component (time-dependent error) through BDLM algorithm. The results demonstrate that the BDLM framework yielded more accurate calculations compared with Gaussian process and Variational Heteroscedasticity Gaussian Process methods with respect to one-step ahead forecasting for strain data under typhoons. Multi-step ahead forecasting was successfully carried out both for non-typhoon and typhoon responses within an acceptable precision range. The correlation between periodic component and temperature was also investigated. Regarding missing data imputation, BLDM algorithm can generate robust results due to making full use of the monitoring data both before and after the missing segments.

Interval early warning method for state of engineering structures based on structural health monitoring data

Interval early warning method for state of engineering structures based on structural health monitoring data

Identifying the outlier in tunnel monitoring data: An integration model

Structural health monitoring (SHM) system based on the Internet of Things is an important method to evaluate the safety of tunnel operation through the real-time monitoring data analysis. Identifying the outlier in SHM data is a non-trivial task but it is challenging for the tunnel engineers because the measurements are quite complicated with the characteristics of time series, unlabeled, high-dimensional, inter-correlations between variables, etc. To detect the outliers, an integration model is developed based on the independent data analysis from the Probabilistic, Proximity-Based (global), Proximity-Based (local), Linear Model and Outlier Ensembles. The model is examined with the shuttle data set in the University of California Irvine (UCI) database and its precision rate is up to 94.5%, highlighting the favorable performance in identifying the outliers. This method is thus applied to the outlier detection of the SHM data in the Nanjing Yangtze River tunnel. 6698 data sets collected from SHM are evaluated and 270 groups of outliers are identified effectively. By eliminating these outliers, comparisons between the proposed integrated model and the single model (i.e. IForest, ABOD, KNN, LOF) are further conducted to discuss the model performance based on the regression analysis. Results show that the integrated model is better than the single model and it possesses the great potential to detect the outliers in SHM system.

Combined Use of Cointegration Analysis and Robust Outlier Statistics to Improve Damage Detection in Real-World Structures.

Due to the need for controlling many ageing and complex structures, structural health monitoring (SHM) has become increasingly common over the past few decades. However, one of the main limitations for the implementation of continuous monitoring systems in real-world structures is the effect that benign influences, such as environmental and operational variations (EOVs), have on damage sensitive features. These fluctuations may mask malign changes caused by structural damages, resulting in false structural condition assessment. When damage identification is implemented as novelty detection due to the lack of known damage states, outliers may be part of the data set as the result of the benign and malign factors mentioned above. Thanks to the developments in the field of robust outlier detection, the current paper presents a new data fusion method based on the use of cointegration and minimum covariance determinant estimator (MCD), which allows us to visualize and to classify outliers in SHM data, depending on their origin. To validate the effectiveness of this technique, the recent case study of the KW51 bridge has been considered, whose natural frequencies are subjected to variations due to both EOVs and a real structural change.

Load-Effect Separation of a Large-Span Prestressed Structure Based on an Enhanced EEMD-ICA Methodology

Prestressed structures have gained popularity in large-span buildings due to their great spanning capacity and light self-weights. This kind of structure is normally subjected to multiple types of loads, such as temperature load, wind load, and construction load. The determination of different load effects not only guides the design of similar structures but also helps reveal the damage-induced variation that would be concealed by the environmental loads. To determine the different load effects, separation of the load effects collected by structural health monitoring (SHM) is needed. This study presents an enhanced approach for load effect separation based on independent component analysis (ICA) and ensemble empirical mode decomposition (EEMD), called EEMD-ICA*. The proposed method is to minimize manual tuning of user-defined parameters, which makes the EEMD-ICA suitable for separating load effects from the different measured data. Specifically, an optimization method is developed to determine the appropriate level of the added white noise in the EEMD using the relative root-mean-square error (RMSE) index. A logarithm form of Bayesian information criterion (BIC) is employed for the robust estimation of the number of load effects in the ICA. Simulated structural responses from a square orthogonal cable-net are used to validate the effectiveness of the EEMD-ICA*. Then, the proposed methodology is employed to extract various load effects from the SHM data of the National Speed Skating Oval (NSSO), which is the largest single-layer cable-net structure in the world.

Active learning structural model updating of a multisensory system based on Kriging method and Bayesian inference

AbstractModel updating techniques are often applied to calibrate the numerical models of bridges using structural health monitoring data. The updated models can facilitate damage assessment and prediction of responses under extreme loading conditions. Some researchers have adopted surrogate models, for example, Kriging approach, to reduce the computations, while others have quantified uncertainties with Bayesian inference. It is desirable to further improve the efficiency and robustness of the Kriging‐based model updating approach and analytically evaluate its uncertainties. An active learning structural model updating method is proposed based on the Kriging method. The expected feasibility learning function is extended for model updating using a Bayesian objective function. The uncertainties can be quantified through a derived likelihood function. The case study for verification involves a multisensory vehicle‐bridge system comprising only two sensors, with one installed on a vehicle parked temporarily on the bridge and another mounted directly on the bridge. The proposed algorithm is utilized for damage detection of two beams numerically and an aluminum model beam experimentally. The proposed method can achieve satisfactory accuracy in identifying damage with much less data, compared with the general Kriging model updating technique. Both the computation and instrumentation can be reduced for structural health monitoring and model updating.