• Smart Structures and Systems
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• 제15권2호
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• pp.425-445
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• 2015

Parametric identification of structures is one of the important aspects of structural health monitoring. Most of the techniques available in the literature have been proved to be effective for structures with small degree of freedoms. However, the problem becomes challenging when the structure system is large, such as bridge structures. Therefore, it is highly desirable to develop parametric identification methods that are applicable to complex structures. In this paper, the LSE based techniques will be combined with the substructure approach for identifying the parameters of a cable-stayed bridge with large degree of freedoms. Numerical analysis has been carried out for substructures extracted from the 2-dimentional (2D) finite element model of a cable-stayed bridge. Only vertical white noise excitations are applied to the structure, and two different cases are considered where the structural damping is not included or included. Simulation results demonstrate that the proposed approach is capable of identifying the structural parameters with high accuracy without measurement noises.

• Smart Structures and Systems
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• 제25권5호
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• pp.593-604
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• 2020

Temperature may have more significant influences on structural responses than operational loads or structural damage. Therefore, a comprehensive understanding of temperature distributions has great significance for proper design and maintenance of bridges. In this study, the temperature distribution of the steel box girder is systematically investigated based on the structural health monitoring system (SHMS) of the Sutong Cable-stayed Bridge. Specifically, the characteristics of the temperature and temperature difference between different measurement points are studied based on field temperature measurements. Accordingly, the probability density distributions of the temperature and temperature difference are calculated statistically, which are further described by the general formulas. The results indicate that: (1) the temperature and temperature difference exhibit distinct seasonal characteristics and strong periodicity, and the temperature and temperature difference among different measurement points are strongly correlated, respectively; (2) the probability density of the temperature difference distribution presents strong non-Gaussian characteristics; (3) the probability density function of temperature can be described by the weighted sum of four Normal distributions. Meanwhile, the temperature difference can be described by the weighted sum of Weibull distribution and Normal distribution.

• Smart Structures and Systems
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• 제29권1호
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• pp.53-62
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• 2022

The deployment of advanced structural health monitoring (SHM) systems in large-scale civil structures collects large amounts of data. Note that these data may contain multiple types of anomalies (e.g., missing, minor, outlier, etc.) caused by harsh environment, sensor faults, transfer omission and other factors. These anomalies seriously affect the evaluation of structural performance. Therefore, the effective analysis and mining of SHM data is an extremely important task. Inspired by the deep learning paradigm, this study develops a novel generative adversarial network (GAN) and convolutional neural network (CNN)-based data anomaly detection approach for SHM. The framework of the proposed approach includes three modules : (a) A three-channel input is established based on fast Fourier transform (FFT) and Gramian angular field (GAF) method; (b) A GANomaly is introduced and trained to extract features from normal samples alone for class-imbalanced problems; (c) Based on the output of GANomaly, a CNN is employed to distinguish the types of anomalies. In addition, a dataset-oriented method (i.e., multistage sampling) is adopted to obtain the optimal sampling ratios between all different samples. The proposed approach is tested with acceleration data from an SHM system of a long-span bridge. The results show that the proposed approach has a higher accuracy in detecting the multi-pattern anomalies of SHM data.

• Smart Structures and Systems
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• 제9권3호
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• pp.287-301
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• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• Structural Engineering and Mechanics
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• 제17권3_4호
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• pp.393-408
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• 2004

There exists a clear need to monitor the performance of civil structures over their operational lives. Current commercial monitoring systems suffer from various technological and economic limitations that prevent their widespread adoption. The wires used to route measurements from system sensors to the centralized data server represent one of the greatest limitations since they are physically vulnerable and expensive from an installation and maintenance standpoint. In lieu of cables, the introduction of low-cost wireless communications is proposed. The result is the design of a prototype wireless sensing unit that can serve as the fundamental building block of wireless modular monitoring systems (WiMMS). An additional feature of the wireless sensing unit is the incorporation of computational power in the form of state-of-art microcontrollers. The prototype unit is validated with a series of laboratory and field tests. The Alamosa Canyon Bridge is employed to serve as a full-scale benchmark structure to validate the performance of the wireless sensing unit in the field. A traditional cable-based monitoring system is installed in parallel with the wireless sensing units for performance comparison.

• 조명전기설비학회논문지
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• 제25권9호
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• pp.86-93
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• 2011

The necessity of Power Quality(PQ) Management grow bigger because the disturbance strength in power system is increasing, on the other hand, the tolerance of electrical components is reducing with widely use of the IT and digital equipments. The domestic management standards on power quality are enacted based on the IEC standards in recent. Since the IEC standards defines the measurement and evaluation point on PQ as PCC(Point of Common Coupling) which is normally the point of penetration between customer and power company, the PQ monitoring system(PQMS) should be made to measure and manage the PQ of custom effectively. In this paper, therefore, the economical construction plan on PQMS using the wireless communication channel in distribution automation system is suggested, and the usefulness of the PQMS are shown by analyzing the PQM data with IEC standards.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.21-32
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• 2003

Developments and applications of the structural health monitoring (SHM) systems have become active particularity for long-span bridges in Korea. They are composed of sensors, data acquisition system, data transmission system, information processing, damage assessment, and information management. In this paper, current status of research and application activities on SHM systems for civil infra-structures in Korea are briefly introduced by 4 parts: (1) current status of bridge monitoring systems on existing and newly constructed bridges, (2) research and development activities on smart sensors such as optical fiber sensors and piezo-electric sensors, (3) structural damage detection methods using measured data, and (4) a test road project for pavement design verification and enhancement by the Korea Highway Corporation. Finally the R＆D activities of a new engineering research center entitled Smart Infra-Structure Technology Center at Korea Advanced Institute of Science and Technology are also briefly described.

• 대한토목학회논문집
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• 제28권5A호
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• pp.631-639
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• 2008

본 논문은 서해대교 사장교의 보강형에 대한 고해상도 다점 모드형상을 TDD기법을 이용하여 추출한 사례를 소개한다. 상시진동에 대한 진동시험이 실시되었으며, 총 72개의 센서위치에서 수직방향 가속도 시간응답을 계측할 수 있는 계측 통신망이 구축되었다. 계측된 가속도 시간응답에 대하여 TDD기법을 적용하여 총 24차 수직방향 모드형상이 추출되었다. 추출된 모드형상은 현재 운용중인 상시모니터링 시스템에서는 계측되지 못한 많은 모드들을 포함하고 있다. 장대교량과 같이 대형구조물의 고해상도 모드형상 추출에는 다른 여러 가지 동특성 추출기법보다 TDD기법이 매우 효과적인 것을 확인 할 수 있었다.

• 한국지진공학회논문집
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• 제24권2호
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• pp.67-76
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• 2020

This study aims to optimize the cochlea-inspired artificial filter bank (CAFB) using El-Centro seismic waveforms and test its performance through a shaking table test on a two-span bridge model. In the process of optimizing the CAFB, El-Centro seismic waveforms were used for the purpose of evaluating how they would affect the optimizing process. Next, the optimized CAFB was embedded in the developed wireless-based intelligent data acquisition (IDAQ) system to enable response measurement in real-time. For its performance evaluation to obtain a seismic response in real-time using the optimized CAFB, a two-span bridge (model structures) was installed in a large shaking table, and a seismic response experiment was carried out on it with El-Centro seismic waveforms. The CAFB optimized in this experiment was able to obtain the seismic response in real-time by compressing it using the embedded wireless-based IDAQ system while the obtained compressed signals were compared with the original signal (un-compressed signal). The results of the experiment showed that the compressed signals were superior to the raw signal in response performance, as well as in data compression effect. They also proved that the CAFB was able to compress response signals effectively in real-time even under seismic conditions. Therefore, this paper established that the CAFB optimized by being embedded in the wireless-based IDAQ system was an economical and efficient data compression sensing technology for measuring and monitoring the seismic response in real-time from structures based on the wireless sensor networks (WSNs).

• Smart Structures and Systems
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• 제29권1호
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• pp.93-103
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• 2022

With the wider availability of sensor technology through easily affordable sensor devices, several Structural Health Monitoring (SHM) systems are deployed to monitor vital civil infrastructure. The continuous monitoring provides valuable information about the health of the structure that can help provide a decision support system for retrofits and other structural modifications. However, when the sensors are exposed to harsh environmental conditions, the data measured by the SHM systems tend to be affected by multiple anomalies caused by faulty or broken sensors. Given a deluge of high-dimensional data collected continuously over time, research into using machine learning methods to detect anomalies are a topic of great interest to the SHM community. This paper contributes to this effort by proposing a relatively new time series representation named "Shapelet Transform" in combination with a Random Forest classifier to autonomously identify anomalies in SHM data. The shapelet transform is a unique time series representation based solely on the shape of the time series data. Considering the individual characteristics unique to every anomaly, the application of this transform yields a new shape-based feature representation that can be combined with any standard machine learning algorithm to detect anomalous data with no manual intervention. For the present study, the anomaly detection framework consists of three steps: identifying unique shapes from anomalous data, using these shapes to transform the SHM data into a local-shape space and training machine learning algorithms on this transformed data to identify anomalies. The efficacy of this method is demonstrated by the identification of anomalies in acceleration data from an SHM system installed on a long-span bridge in China. The results show that multiple data anomalies in SHM data can be automatically detected with high accuracy using the proposed method.