• Smart Structures and Systems
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• v.25 no.1
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• pp.23-35
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• 2020

It is important to take into account the thermal behavior in assessing the structural condition of bridges. An effective method of studying the temperature effect of long-span bridges is numerical simulation based on the solar radiation models. This study aims to develop a time-varying solar radiation model which can consider the real-time weather changes, such as a cloud cover. A statistical analysis of the long-term monitoring data is first performed, especially on the temperature data between the south and north anchors of the bridge, to confirm that temperature difference can be used to describe real-time weather changes. Second, a defect in the traditional solar radiation model is detected in the temperature field simulation, whereby the value of the turbidity coefficient t_u is subjective and cannot be used to describe the weather changes in real-time. Therefore, a new solar radiation model with modified turbidity coefficient γ is first established on the temperature difference between the south and north anchors. Third, the temperature data of several days are selected for model validation, with the results showing that the simulated temperature distribution is in good agreement with the measured temperature, while the calculated results by the traditional model had minor errors because the turbidity coefficient t_u is uncertainty. In addition, the vertical and transverse temperature gradient of a typical cross-section and the temperature distribution of the tower are also studied.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1A
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• pp.61-73
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• 2009

In this paper, hybrid health monitoring techniques using acceleration-impedance features are newly proposed to detect two damage-type in steel plate-girder bridges, which are girder's stiffness-loss and support perturbation. The hybrid techniques mainly consists of three sequential phases: 1) to alarm the occurrence of damage in global manner, 2) to classify the alarmed damage into subsystems of the structure, and 3) to estimate the classified damage in detail using methods suitable for the subsystems. In the first phase, the global occurrence of damage is alarmed by monitoring changes in acceleration features. In the second phase, the alarmed damage is classified into subsystems by recognizing patterns of impedance features. In the final phase, the location and the extent of damage are estimated by using modal strain energy-based damage index method and root mean square deviation (RMSD) method. The feasibility of the proposed hybrid technique is evaluated on a laboratory-scaled steel plate-girder bridge model for which hybrid acceleration-impedance signatures were measured for several damage scenarios. Also, the effect of temperature on the accuracy of the impedance-based damage monitoring results are experimentally examined from combined scenarios of support damage cases and temperature changes.

• Journal of the Korea institute for structural maintenance and inspection
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• v.1 no.2
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• pp.87-94
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• 1997

This research aims to develop an algorithm of optimal transducer placement using Kinetic Energy of the structural system. The structural vibration response-based health monitoring is considered one of the best for the system which requires a long-term, continuous monitoring. In its experimental modal testing, however, it is difficult to decide on the measurement locations and their number, especially for complex structures, which have a major influence on the quality of the results. In order to minimize the number of sensing operations and optimize the transducer location while maximizing the accuracy of results, this paper discusses about an optimum transducer placement criterion suitable for the identification of structural damage. As a criterion algorithm, it proposes the Kinetic Energy Optimization Technique (EOT), and then addresses the numerical issues which are subsequently applicable to actual experiment where a bridge model is used. By using the experimental data, it compares the EOT with the EIM (Effective Independence Method) which is generally used to optimize the transducer placement for the damage identification and control purposes. The comparison conclusively shows that the EOT algorithm proposed in this paper is preferable when a structure is to be instrumented with fewer sensors.

• Structural Monitoring and Maintenance
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• v.2 no.4
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• pp.399-418
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• 2015

Automated damage detection through Structural Health Monitoring (SHM) techniques has become an active area of research in the bridge engineering community but widespread implementation on in-service infrastructure still presents some challenges. In the meantime, visual inspection remains as the most common method for condition assessment even though collected information is highly subjective and certain types of damage can be overlooked by the inspector. In this article, a Frequency Response Functions-based model updating algorithm is evaluated using experimentally collected data from the University of Central Florida (UCF)-Benchmark Structure. A protocol for measurement selection and a regularization technique are presented in this work in order to provide the most well-conditioned model updating scenario for the target structure. The proposed technique is composed of two main stages. First, the initial finite element model (FEM) is calibrated through model updating so that it captures the dynamic signature of the UCF Benchmark Structure in its healthy condition. Second, based upon collected data from the damaged condition, the updating process is repeated on the baseline (healthy) FEM. The difference between the updated parameters from subsequent stages revealed both location and extent of damage in a "blind" scenario, without any previous information about type and location of damage.

• Smart Structures and Systems
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• v.17 no.6
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• pp.935-956
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• 2016

With the help of advanced image acquisition and processing technology, the vision-based measurement methods have been broadly applied to implement the structural monitoring and condition identification of civil engineering structures. Many noncontact approaches enabled by different digital image processing algorithms are developed to overcome the problems in conventional structural dynamic displacement measurement. This paper presents three kinds of image processing algorithms for structural dynamic displacement measurement, i.e., the grayscale pattern matching (GPM) algorithm, the color pattern matching (CPM) algorithm, and the mean shift tracking (MST) algorithm. A vision-based system programmed with the three image processing algorithms is developed for multi-point structural dynamic displacement measurement. The dynamic displacement time histories of multiple vision points are simultaneously measured by the vision-based system and the magnetostrictive displacement sensor (MDS) during the laboratory shaking table tests of a three-story steel frame model. The comparative analysis results indicate that the developed vision-based system exhibits excellent performance in structural dynamic displacement measurement by use of the three different image processing algorithms. The field application experiments are also carried out on an arch bridge for the measurement of displacement influence lines during the loading tests to validate the effectiveness of the vision-based system.

• Smart Structures and Systems
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• v.29 no.1
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• pp.77-91
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• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Journal of the Korea institute for structural maintenance and inspection
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• v.13 no.3 s.55
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• pp.135-144
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• 2009

The cable system of suspension bridges is a very important non-elastic element which caries an external load by a tension force of the cable, such that creates the integrity of a structure. It is not easy to find if cable system have been changed by the maintenance activities such as repairs or reinforcement. Sometimes the maintenance can cause structural deformations and changes of the tension force in cables. In most cases, the cable stayed bridges are managed by health monitering system, however, the main cable of suspension bridges need to develop more accurate and efficient monitoring system. The Namhee Bridge was constructed 35 years ago and it has been continually repaired and reinforced after then. This study describes the behavior of the cable system by analysing many of inspective reports and by using the results of hanger rope test and for the shape of main cables surveys.

• Smart Structures and Systems
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• v.17 no.3
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• pp.471-489
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• 2016

Modal identification based on ambient vibration data has attracted extensive attention in the past few decades. Since the excitation for ambient vibration tests is mainly from the environmental effects such as wind and traffic loading and no artificial excitation is applied, the signal to noise (s/n) ratio of the data acquired plays an important role in mode identifiability. Under ambient vibration conditions, certain modes may not be identifiable due to a low s/n ratio. This paper presents a study on the mode identifiability of an instrumented cable-stayed bridge with the use of acceleration response data measured by a long-term structural health monitoring system. A recently developed fast Bayesian FFT method is utilized to perform output-only modal identification. In addition to identifying the most probable values (MPVs) of modal parameters, the associated posterior uncertainties can be obtained by this method. Likewise, the power spectral density of modal force can be identified, and thus it is possible to obtain the modal s/n ratio. This provides an efficient way to investigate the mode identifiability. Three groups of data are utilized in this study: the first one is 10 data sets including six collected under normal wind conditions and four collected during typhoons; the second one is three data sets with wind speeds of about 7.5 m/s; and the third one is some blind data. The first two groups of data are used to perform ambient modal identification and help to estimate a critical value of the s/n ratio above which the deficient mode is identifiable, while the third group of data is used to perform verification. A couple of fundamental modes are identified, including the ones in the vertical and transverse directions respectively and coupled in both directions. The uncertainty and s/n ratio of the deficient mode are investigated and discussed. A critical value of the modal s/n ratio is suggested to evaluate the mode identifiability of the deficient mode. The work presented in this paper could provide a base for the vibration-based condition assessment in future.

• Wind and Structures
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• v.30 no.2
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• pp.181-198
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• 2020

In the traditional buffeting response analysis method, the spanwise incomplete correlation of buffeting forces is always assumed to be same as that of the incident wind turbulence and the action of the signature turbulence is ignored. In this paper, three typical bridge decks usually adopted in the real bridge engineering, a single flat box deck, a central slotted box deck and a two-separated paralleled box deck, were employed as the investigated objects. The wind induced pressure on these bridge decks were measured via a series of wind tunnel pressure tests of the sectional models. The influences of the wind speed in the tests, the angle of attack, the turbulence intensity and the characteristic distance were taken into account and discussed. The spanwise root coherence of buffeting forces was also compared with that of the incidence turbulence. The signature turbulence effect on the spanwise root coherence function was decomposed and explained by a new empirical method with a double-variable model. Finally, the formula of a sum of rational fractions that accounted for the signature turbulence effect was proposed in order to fit the results of the spanwise root coherence function. The results show that, the spanwise root coherence of the drag force agrees with that of incidence turbulence in some range of the reduced frequency but disagree in the mostly reduced frequency. The spanwise root coherence of the lift force and the torsional moment is much larger than that of the incidence turbulence. The influences of the wind speed and the angle of attack are slight, and they can be ignored in the wind tunnel test. The spanwise coherence function often involves several narrow peaks due to the signature turbulence effect in the high reduced frequency zone. The spanwise coherence function is related to the spanwise separation distance and the spanwise integral length scales, and the signature turbulence effect is related to the deck-width-related reduced frequency.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.445-448
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• 2011

최근 재료 및 시공 기술의 지속적 발전으로 인하여, 사장교, 현수교 등 장대교량의 건설이 증가하고 있다. 이러한 장대교량들은 사회적 및 경제적인 중요성이 매우 크므로 교량 완공 후 유지관리 및 구조적 건전성을 모니터링하기 위하여 교량 각 주요 구조부재에 다양한 센서를 설치함으로써 교량모니터링시스템을 구축하여 외부하중에 의한 교량구조물의 거동을 감지, 수집, 분석하여 교량의 건전성을 파악하기 위하여 장대교량의 공용성과 안전성 확보에 많은 연구가 진행 되고 있으며 현재 국내외 다수의 장대교량들에는 다양한 센서로 이루어진 구조 건전성 모니터링 시스템이 설치, 운용되고 있다. 실제로 서해대교, 광안대교, 홍콩 TsingMa Bridge, 미국 Bill Emerson Memorial Bridge와 같은 실제 교량에도 적용된 바 있다. 하지만, 이상의 구조 건전성 모니터링 시스템 기술이 널리 활용되는 데는 여러 가지 장애물이 있는데, 그 중에서 가장 큰 것이 시스템을 구축하는 데 과다한 비용이 소요된다는 점이다. 예로 홍콩의 Tsing Ma 대교의 경우 350개의 센서를 설치하는데 약 8백만불이 넘는 금액이 들었으며(Farrar 등, 2004), Celeby(2002)의 보고에 의하면 각 센서의 채널 당 케이블의 설치 비용으로 약 5000불의 비용이 소모되고 있다. 이에 본 연구에서는 이러한 불편함을 극복하기 위하여 무선 센서를 개발하고, 이를 한강 상 교량 중 올림픽대교에 적용, 사장교의 구조 건전성을 저비용 및 높은 편의성으로 모니터링 하기 위한 테스트베드를 구축하여 무선 계측 시스템의 정확성 및 적용성을 확인하였다.