• KSCE Journal of Civil and Environmental Engineering Research
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• v.31 no.5A
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• pp.351-360
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• 2011

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for offshore structure, there are few cases to apply structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure under changes in temperature and transverse loadings. Principal component analysis (PCA) was applied using conventional damage index to eliminate principal components sensitive to environmental change. It was found that the proposed PCA-base approach is an effective tool for long-term SHM under significant environmental changes.

• Smart Structures and Systems
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• v.9 no.6
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• pp.473-487
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• 2012

GPS has become an established technique in structural health monitoring. This paper presents the application of an on-line GPS RTK system on the Zhujiang Huangpu Bridge (China) for monitoring bridge deck and towers movements. In this study, both the form and functions of movements of the deck and towers of the bridge under affecting loads were monitored in lateral, longitudinal and vertical directions. Such movements were described in time and frequency domains by determining the trend, torsion, periodical of the series using probability density function (PDF). The results of the time series GPS data are practical and useful to bridge health monitoring.

• Structural Monitoring and Maintenance
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• v.1 no.1
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• pp.69-87
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• 2014

A new output only modal analysis method is developed in this paper. This method uses continuous wavelet transform to modify a popular blind source separation algorithm, second order blind identification (SOBI). The wavelet modified SOBI (WMSOBI) method replaces original time domain signal with selected time-frequency domain wavelet coefficients, which overcomes the shortcomings of SOBI. Both numerical and experimental studies on bridge models are carried out when there are limited number of sensors. Identified modal properties from WMSOBI are analyzed and compared with fast Fourier transform (FFT), SOBI and eigensystem realization algorithm (ERA). The comparison shows WMSOBI can identify as many results as FFT and ERA. Further case study of structural health monitoring (SHM) on an arch bridge verifies the capability to detect damages by combining WMSOBI with incomplete flexibility difference method.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.71-90
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• 2016

The paper reports a wide overview of the scientific activities on Structural Health Monitoring (SHM) in Italy. They are classified on three different conceptual scales: national territory (macro); regional area (medium); single structure (small). In the latter case differences have been pointed out between permanent installation and short-term experimental campaigns. A particular focus has been dedicated to applications devoted to cultural heritage which have an important historic, strategic and economic value for Italy. Two specific cases, the first related to the permanent monitoring of an historical Basilica and the second regarding the dynamic testing of a modern structure, have been presented as a basis for a general discussion.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.687-691
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• 2010

구조물의 건전도를 평가하기 위해 상시 구조물 계측을 이용한 Structural Health Monitoring (SHM) 시스템을 적용하게 된다. SHM 시스템의 궁극적 목적은 계측된 데이터를 이용하여 구조물의 손상위치 및 손상정도를 분석하여 거주자에게 유지관리정보와 대처요령 신속하게 제공하는 것이다. 따라서 본 연구에서는 구조물의 손상탐지를 위해 인공신경망(Artificial Neural Network)을 도입한 알고리즘을 수립하고, 이를 3층 실대 RC Mock-up 구조물에 적용하여 성능을 평가하였다. 먼저 인공신경망의 학습을 위해 구조해석 프로그램을 이용하여 구조물의 손상에 따른 동적특성 변화 데이터베이스를 구축하였다. 그리고 학습된 인공망에 실제 구조물에서 추출한 동특성의 변화를 입력하여 손상탐지를 실시하였다. 이를 통해 인공신경망의 학습방법, 학습데이터의 정규화 방법 등을 규명하고 인공신경망을 이용한 손상탐지의 효과를 분석하였다.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.32-38
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• 2020

The deployment of geographically distributed wireless sensors has greatly elevated the capability of monitoring structural health in social-overhead capital (SOC) public infrastructures. This paper deals with the utilization of a distributed mobility management (DMM) approach for the deployment of wireless sensing devices in a structural health monitoring system (SHM). Then, a wireless sensing mechanism utilizing low-energy adaptive clustering hierarchy (LEACH)-based clustering algorithm for smart sensors has been analyzed to support the seamless data transmission of structural health information which is essentially important to guarantee public safety. The clustering of smart sensors will be able to provide real-time monitoring of structural health and a filtering algorithm to boost the transmission of critical information over heterogeneous wireless and mobile networks.

• Journal of the Korea institute for structural maintenance and inspection
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• v.20 no.4
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• pp.1-8
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• 2016

Recently, buildings tend to be large size, complex shape and functional. As the size of buildings is becoming massive, the need for structural health monitoring(SHM) technique is ever-increasing. Various SHM techniques have been studied for buildings which have different dynamic characteristics and are influenced by various external loads. Generally, the visual inspection and non-destructive test for an accessible point of structures are performed by experts. But nowadays, the system is required which is online measurement and detect risk elements automatically without blind spots on structures. In this study, in order to consider the response of non-linear structures, proposed a signal feature extraction and the adaptive threshold setting algorithm utilized to determine the abnormal behavior by using statistical methods such as control chart, root mean square deviation, generalized extremely distribution. And the performance of that was validated by using the acceleration response of structures during earthquakes measuring system of forced vibration tests and actual operation.

• Smart Structures and Systems
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• v.19 no.3
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• pp.335-340
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• 2017

Fiber Bragg grating (FBG) sensors are applied to structural health monitoring (SHM) in many areas due to their unique advantages such as ease of multiplexing and capability of absolute measurement. However, they are exposed to cyclic thermal load, generally in the temperature range of $-20^{\circ}C$ to $60^{\circ}C$, in railways during a long-term SHM and the cyclic thermal load can affect the mechanical strength of FBGs. In this paper, the effects of both cyclic thermal load and the reflectivity of FBGs on the mechanical strength are investigated though tension tests of FBG specimens after they are aged in a thermal chamber with temperature changes in a range from $-20^{\circ}C$ to $60^{\circ}C$ for 300 cycles. Results from tension tests reveal that the mechanical strength of FBGs decreases about 8% as the thermal cycle increases to 100 cycles; the mechanical strength then remains steady until 300 cycles. Otherwise, the mechanical strength of FBGs with reflectivity of 6dB (70%) and 10dB (90%) exhibits degradation values of about 6% and 12%, respectively, compared to that with reflectivity of 3dB (50%) at 300 cycles. SEM photos of the Bragg grating parts also show defects that cause their strength degradation. Consequently, it should be considered that mechanical strength of FBGs can be degraded by both thermal cycles and the reflectivity if the FBGs are exposed to repetitive thermal load during a long-term SHM.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.461-480
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• 2010

This paper analyses the data collected from the $2^{nd}$ Jindo Bridge, a cable-stayed bridge in Korea that is a structural health monitoring (SHM) international test bed for advanced wireless smart sensors network (WSSN) technology. The SHM system consists of a total of 70 wireless smart sensor nodes deployed underneath of the deck, on the pylons, and on the cables to capture the vibration of the bridge excited by traffic and environmental loadings. Analysis of the data is performed in both the time and frequency domains. Modal properties of the bridge are identified using the frequency domain decomposition and the stochastic subspace identification methods based on the output-only measurements, and the results are compared with those obtained from a detailed finite element model. Tension forces for the 10 instrumented stay cables are also estimated from the ambient acceleration data and compared both with those from the initial design and with those obtained during two previous regular inspections. The results of the data analyses demonstrate that the WSSN-based SHM system performs effectively for this cable-stayed bridge, giving direct access to the physical status of the bridge.

• Smart Structures and Systems
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• v.23 no.3
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• pp.295-306
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• 2019

Shanghai Tower is a 632-meter super high-rise building located in an area with wind and active earthquake. A sophisticated structural health monitoring (SHM) system consisting of more than 400 sensors has been built to carry out a long-term monitoring for its operational safety. In this paper, a reduced-order model including 31 elements was generated from a full model of this super tall building. An iterative regularized matrix method was proposed to tune the system parameters, making the dynamic characteristic of the reduced-order model be consistent with those in the full model. The updating reduced-order model can be regarded as a benchmark model for further analysis. A long-term monitoring for structural dynamic characteristics of Shanghai Tower under different construction stages was also investigated. The identified results, including natural frequency and damping ratio, were discussed. Based on the data collected from the SHM system, the dynamic characteristics of the whole structure was investigated. Compared with the result of the finite element model, a good agreement can be observed. The result provides a valuable reference for examining the evolution of future dynamic characteristics of this super tall building.