• Structural Engineering and Mechanics
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• 제19권2호
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• pp.123-139
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• 2005

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm for identifying modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers permanently installed on the cable-stayed Ting Kau Bridge. With the continuously identified results, variability in modal vectors due to varying environmental conditions and measurement errors is observed. Such an observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring use.

• Smart Structures and Systems
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• 제19권3호
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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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• 제23권3호
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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.

• Smart Structures and Systems
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• 제19권3호
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• pp.269-277
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• 2017

Recently, there has been significant interest in structural health monitoring for civil engineering applications. In this research, a specially designed tendon, proposed by embedding FBG sensors into the center king cable of a 7-wire strand tendon, was applied for long-term health monitoring of tensile forces on a ground anchor. To make temperature independent sensors, the effective temperature compensation of FBG sensors must be considered. The temperature sensitivity coefficient ${\beta}^{\prime}$ of the FBG sensors embedded tendon was successfully determined to be $2.0{\times}10^{-5}^{\circ}C^{-1}$ through calibrated tests in both a model rock body and a laboratory heat chamber. Furthermore, the obtained result for ${\beta}^{\prime}$ was formally verified through the ground temperature measurement test, expectedly. As a result, the ground temperature measured by a thermometer showed good agreement compared to that measured by the proposed FBG sensor, which was calibrated considering to the temperature sensitivity coefficient ${\beta}^{\prime}$. Finally, four prototype ground anchors including two tension ground anchors and two compression ground anchors made by replacing a tendon with the proposed smart tendon were installed into an actual slope at the Yeosu site. Tensile forces, after temperature compensation was taken into account using the verified temperature sensitivity coefficient ${\beta}^{\prime}$ and ground temperature obtained from the Korean Meteorological Administration (KMA) have been monitored for over one year, and the results were very consistent to those measured from the load cell, interestingly.

• 국제초고층학회논문집
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• 제1권1호
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• pp.37-51
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• 2012

New generation of tall and complex buildings systems are now introduced that are reflective of the latest development in materials, design, sustainability, construction, and IT technologies. While the complexity in design is being overcome by the availability and advances in structural analysis tools and readily advanced software, the design of these buildings are still reliant on minimum code requirements that yet to be validated in full scale. The involvement of the author in the design and construction planning of Burj Khalifa since its inception until its completion prompted the author to conceptually develop an extensive survey and real-time structural health monitoring program to validate all the fundamental assumptions mad for the design and construction planning of the tower. The Burj Khalifa Project is the tallest structure ever built by man; the tower is 828 meters tall and comprises of 162 floors above grade and 3 basement levels. Early integration of aerodynamic shaping and wind engineering played a major role in the architectural massing and design of this multi-use tower, where mitigating and taming the dynamic wind effects was one of the most important design criteria established at the onset of the project design. Understanding the structural and foundation system behaviors of the tower are the key fundamental drivers for the development and execution of a state-of-the-art survey and structural health monitoring (SHM) programs. Therefore, the focus of this paper is to discuss the execution of the survey and real-time structural health monitoring programs to confirm the structural behavioral response of the tower during construction stage and during its service life; the monitoring programs included 1) monitoring the tower's foundation system, 2) monitoring the foundation settlement, 3) measuring the strains of the tower vertical elements, 4) measuring the wall and column vertical shortening due to elastic, shrinkage and creep effects, 5) measuring the lateral displacement of the tower under its own gravity loads (including asymmetrical effects) resulting from immediate elastic and long term creep effects, 6) measuring the building lateral movements and dynamic characteristic in real time during construction, 7) measuring the building displacements, accelerations, dynamic characteristics, and structural behavior in real time under building permanent conditions, 8) and monitoring the Pinnacle dynamic behavior and fatigue characteristics. This extensive SHM program has resulted in extensive insight into the structural response of the tower, allowed control the construction process, allowed for the evaluation of the structural response in effective and immediate manner and it allowed for immediate correlation between the measured and the predicted behavior. The survey and SHM programs developed for Burj Khalifa will with no doubt pioneer the use of new survey techniques and the execution of new SHM program concepts as part of the fundamental design of building structures. Moreover, this survey and SHM programs will be benchmarked as a model for the development of future generation of SHM programs for all critical and essential facilities, however, but with much improved devices and technologies, which are now being considered by the author for another tall and complex building development, that is presently under construction.

• Smart Structures and Systems
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• 제25권1호
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• pp.111-122
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• 2020

Researchers up to date have introduced several Structural Health Monitoring (SHM) techniques with varying advantages and drawbacks for each. Satellite positioning systems (GPS, GLONASS and GALILEO) based techniques proved to be promising, especially for high natural period structures. Particularly, the GPS has proved sufficient performance and reasonable accuracy in tracking real time dynamic displacements of flexible structures independent of atmospheric conditions, temperature variations and visibility of the monitored object. Tall structures are particularly sensitive to oscillations produced by different sources of dynamic actions; such as typhoons. Wind forces induce in the structure both longitudinal and perpendicular displacements with respect to the wind direction, resulting in torsional effects, which are usually more complex to be detected. To efficiently track the horizontal rotations of the in-plane sections of such flexible structures, two main issues have to be considered: a suitable sensor topology (i.e., location, installation, and combination of sensors), and the methodology used to process the data recorded by sensors. This paper reports the contributions of the measurements recorded from dual frequency GPS receivers and uni-axial accelerometers in a full-scale experimental campaign. The Canton tower in Guangzhou-China is the case study of this research, which is instrumented with a long-term structural health monitoring system deploying both accelerometers and GPS receivers. The elaboration of combining the obtained rather long records provided by these two types of sensors in detecting the torsional behavior of the tower under ambient vibration condition and during strong wind events is discussed in this paper. Results confirmed the reliability of GPS receivers in obtaining the dynamic characteristics of the system, and its ability to capture the torsional response of the tower when used alone or when they are combined with accelerometers integrated data.

• 대한토목학회논문집
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• 제31권5A호
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• pp.351-360
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• 2011

일반적으로 해양개발을 위하여 사용되는 재킷식 해양 구조물은 염분, 조류, 파도, 바람 등 극심한 환경적 요인에 의해 육상 시설물에 비해 열악한 환경에 노출되어 있으나, 이러한 구조물의 구조적 안전성 및 성능 확보를 위한 모니터링 시스템의 적용사례는 그다지 많지 않다. 임피던스 기반의 구조물 건전성 모니터링은 구조물의 국부적인 손상을 모니터링하는 기술로서 최근 실 구조물에의 적용에 대한 연구가 국내외에서 활발하게 이루어지고 있으나, 온도, 하중 등의 환경적 요인에 의한 영향을 최소화하고 손상에 의한 특성만을 효과적으로 추출하는 기술은 미흡한 상태이다. 따라서 본 연구에서는 재킷식 조류발전 지지구조물의 축소모형을 제작하여 온도와 정적 하중이 변하는 환경 하에서 임피던스 기반의 국부적 손상 모니터링에 관한 연구를 수행하였다. 환경에 의한 영향을 보상하기 위하여 기존의 손상지수에 대해 주성분 분석을 수행하였으며 환경 변화에 민감하게 반응하는 주성분을 선택하여 제거하였다. 이 후 손상지수를 모니터링한 결과, 제안된 주성분 분석 기반 접근법이 환경 영향 하에서의 장기 모니터링에 유효하게 사용될 수 있음을 확인하였다.

• Structural Monitoring and Maintenance
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• 제2권4호
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• pp.339-355
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• 2015

Corrosion of prestressed concrete structures is one of the main challenges that engineers face today. In response to this national need, this paper presents the results of a long-term project that aims at developing a structural health monitoring (SHM) technology for the nondestructive evaluation of prestressed structures. In this paper, the use of permanently installed low profile piezoelectric transducers (PZT) is proposed in order to record the acoustic emissions (AE) along the length of the strand. The results of an accelerated corrosion test are presented and k-means clustering is applied via principal component analysis (PCA) of AE features to provide an accurate diagnosis of the strand health. The proposed approach shows good correlation between acoustic emissions features and strand failure. Moreover, a clustering technique for the identification of false alarms is proposed.

• 한국지진공학회논문집
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• 제10권6호
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• pp.9-18
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• 2006

영종대교에 설치된 교량 모니터링 시스템의 구성특징을 살펴보고 이로부터 계측, 수집된 각 신호들의 특성을 조사 분석하였다. 3차원 자정식 현수교인 영종대교에 설치, 운영되는 모니터링 시스템의 구성은 센서-현장하드웨어-계측서버-관리자로 연결되는 자동화된 시스템으로써 개통 이후부터 교량의 거동 및 하중효과를 대변하는 물리량을 측정하고 있다. 이 논문에서는 시스템의 구성 및 측정항목에 대한 소개와 온도변화에 의한 시그널에의 영향을 감시 할 수 있는 알고리즘의 개발과정을 언급한다. 또한, 행어 케이블의 장력측정 방법의 일환으로 길이가 짧고 장력이 큰 케이블에 대해 정적으로 장력산정이 가능한 장치 및 알고리즘의 개발에 대해 소개한다. 특히 이 교량의 공용중에 이루어진 철도통행을 위한 설비의 추가로 교량 구조계의 변화를 계측 신호를 바탕으로 분석, 제시하였다. 이러한 각종 계측 및 모니터링 결과는 향후 교량의 상태판정의 기본자료로 활용되어 효율적 유지관리를 가능하게 할 것으로 기대된다.

• Smart Structures and Systems
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• 제29권1호
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.