• Smart Structures and Systems
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• 제10권6호
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• pp.517-546
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• 2012

In this paper, vibration-based methods to monitor damage in foundation-structure interface of harbor caisson structure are presented. The following approaches are implemented to achieve the objective. Firstly, vibration-based damage monitoring methods utilizing a variety of vibration features are selected for harbor caisson structure. Autoregressive (AR) model for time-series analysis and power spectral density (PSD) for frequency-domain analysis are selected to detect the change in the caisson structure. Also, the changes in modal parameters such as natural frequency and mode shape are examined for damage monitoring in the structure. Secondly, the feasibility of damage monitoring methods is experimentally examined on an un-submerged lab-scaled mono-caisson. Finally, numerical analysis of un-submerged mono-caisson, submerged mono-caisson and un-submerged interlocked multiple-caissons are carried out to examine the effect of boundary-dependent parameters on the damage monitoring of harbor caisson structures.

• KIEE International Transactions on Power Engineering
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• 제4A권4호
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• pp.214-220
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• 2004

Power quality monitoring is the cornerstone for power quality analysis, diagnosis and improvement. The measurement of power quality (PQ) evolves from instantaneous metering to continuous monitoring. Furthermore, recent technologies enable us to construct more flexible, reliable, rapid and economical power quality monitoring system (PQMS). Therefore, this paper presents an improved PQMS with a new distributed monitoring structure. The proposed PQMS consists of a PQ meter, PQ analyzer and GUI. The PQ meter only collects raw data and the PQ analyzer performs power quality analysis. It has several advantages compared to conventional structures in economic efficiency, modularity, speed, etc. PQ monitoring algorithms to catch steady-state trends and to detect PQ events are also adapted to the proposed structure. Using the proposed structure and monitoring algorithm, a prototype PQMS is constructed and real-time testing is performed.

• Smart Structures and Systems
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• 제30권5호
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• pp.463-477
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• 2022

In this paper, a hybrid vibration-impedance-based damage monitoring approach is experimentally evaluated for prestressed concrete (PSC) structures with local strand breakage. Firstly, the hybrid monitoring scheme is designed to alert damage occurrence from changes in vibration characteristics and to localize strand breakage from changes in impedance signatures. Secondly, a full-scale PSC anchorage is experimented to measure global vibration responses and local impedance responses under a sequence of simulated strand-breakage events. Finally, the measured data are analyzed using the hybrid monitoring framework. The change of structural condition (i.e., damage extent) induced by the local strand breakage is estimated by changes in a few natural frequencies obtained from a few accelerometers in the structure. The damaged strand is locally identified by tomography analysis of impedance features measured via an array of PZT (lead-zirconate-titanate) sensors mounted on the anchorage. Experimental results demonstrate that the strand breakage in the PSC structure can be accurately assessed by using the combined vibration and impedance features.

• Smart Structures and Systems
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• 제7권2호
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• pp.83-102
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• 2011

As a testbed for various structural health monitoring (SHM) technologies, a super-tall structure - the 610 m-tall Guangzhou Television and Sightseeing Tower (GTST) in southern China - is currently under construction. This study aims to explore state-of-the-art wireless sensing technologies for monitoring the ambient vibration of such a super-tall structure during construction. The very nature of wireless sensing frees the system from the need for extensive cabling and renders the system suitable for use on construction sites where conditions continuously change. On the other hand, unique technical hurdles exist when deploying wireless sensors in real-life structural monitoring applications. For example, the low-frequency and low-amplitude ambient vibration of the GTST poses significant challenges to sensor signal conditioning and digitization. Reliable wireless transmission over long distances is another technical challenge when utilized in such a super-tall structure. In this study, wireless sensing measurements are conducted at multiple heights of the GTST tower. Data transmission between a wireless sensing device installed at the upper levels of the tower and a base station located at the ground level (a distance that exceeds 443 m) is implemented. To verify the quality of the wireless measurements, the wireless data is compared with data collected by a conventional cable-based monitoring system. This preliminary study demonstrates that wireless sensing technologies have the capability of monitoring the low-amplitude and low-frequency ambient vibration of a super-tall and slender structure like the GTST.

• 한국재난정보학회 논문집
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• 제16권1호
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• pp.10-20
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• 2020

연구목적: 터널구조물 유지관리계측의 현장점검 결과를 분석하여 개선방안을 제시한다. 연구방법: 도심지에서 건설되고 있는 서울지하철◯호선 14개 공구의 터널구조물을 대상으로 유지관리계측에 대한 다양한 항목의 문제점을 조사하고 분석을 실시하였다. 연구결과: 유지관리 계측 항목과 계측수량 및 설치위치는 개착터널과 굴착터널로 구분하여 개선방안을 제시하였다. 계측기기의 내구연한은 각종 기준을 검토하였고, 내구성 확인방법은 계측센서의 승인 및 검수, 검사 및 시험, 검정 및 교정 등의 개선방안을 제시하였다. 결론: 터널구조물 유지관리계측의 개선방안을 건설현장에 적용하면 유지관리계측의 효율성이 증대되고 건설계측 기술발전에 기여할 것이다.

• 한국수학교육학회지시리즈D:수학교육연구
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• 제13권1호
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• pp.31-48
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• 2009

Utilizing the path analysis method, the study explores the relationships among the influential factors in mathematics modeling academic achievement. The following conclusions are drawn: 1. Achievement motivation, creative inclination, cognitive style, the mathematical cognitive structure and mathematics modeling self-monitoring ability, those have significant correlation with mathematics modeling academic achievement; 2. Mathematical cognitive structure and mathematics modeling self-monitoring ability have significant and regressive effect on mathematics modeling academic achievement, and two factors can explain 55.8% variations of mathematics modeling academic achievement; 3. Achievement motivation, creative inclination, cognitive style, mathematical cognitive structure have significant and regressive effect on mathematics modeling self-monitoring ability, and four factors can explain 70.1% variations of mathematics modeling self-monitoring ability; 4. Achievement motivation, creative inclination, and cognitive style have significant and regressive effect on mathematical cognitive structure, and three factors can explain 40.9% variations of mathematical cognitive structure.

• 풍력에너지저널
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• 제9권4호
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• 정보통신설비학회논문지
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• 제10권1호
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• pp.1-6
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• 2011

The Underground pipeline facility is a general but most important facility in modern world, but its maintainability has been left behind. An automated and intelligent management technology is needed to prevent the wast of social resource and security. In this paper, we introduce Pipeline Health Monitoring(PHM) with Ubiquitous Sensor Network(USN) for inexpensive structure safety monitoring system, and improve its utility by inventing the advanced impedance converter.

• Smart Structures and Systems
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• 제4권6호
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• pp.759-777
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• 2008

A wireless sensing system is designed for application to structural monitoring and damage detection applications. Embedded in the wireless monitoring module is a two-tier prediction model, the auto-regressive (AR) and the autoregressive model with exogenous inputs (ARX), used to obtain damage sensitive features of a structure. To validate the performance of the proposed wireless monitoring and damage detection system, two near full scale single-story RC-frames, with and without brick wall system, are instrumented with the wireless monitoring system for real time damage detection during shaking table tests. White noise and seismic ground motion records are applied to the base of the structure using a shaking table. Pattern classification methods are then adopted to classify the structure as damaged or undamaged using time series coefficients as entities of a damage-sensitive feature vector. The demonstration of the damage detection methodology is shown to be capable of identifying damage using a wireless structural monitoring system. The accuracy and sensitivity of the MEMS-based wireless sensors employed are also verified through comparison to data recorded using a traditional wired monitoring system.

• Computers and Concrete
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• 제8권3호
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• pp.293-309
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• 2011

A long-term structural health monitoring (SHM) system comprising over 700 sensors of sixteen types has been implemented on the Guangzhou Television and Sightseeing Tower (GTST) of 610 m high for real-time monitoring of the structure at both construction and service stages. As part of this sophisticated SHM system, 48 temperature sensors have been deployed at 12 cross-sections of the reinforced concrete inner structure of the GTST to provide on-line monitoring via a wireless data transmission system. In this paper, the differential temperature profiles in the reinforced concrete inner structure of the GTST, which are mainly caused by solar radiation, are recognized from the monitoring data with the purpose of understanding the temperature-induced structural internal forces and deformations. After a careful examination of the pre-classified temperature measurement data obtained under sunny days and non-sunny days, common characteristic of the daily temperature variation is observed from the data acquired in sunny days. Making use of 60-day temperature measurement data obtained in sunny days, statistical patterns of the daily rising temperature and daily descending temperature are synthesized, and temperature distribution models of the reinforced concrete inner structure of the GTST are formulated using linear regression analysis. The developed monitoring-based temperature distribution models will serve as a reliable input for numerical prediction of the temperature-induced deformations and provide a robust basis to facilitate the design and construction of similar structures in consideration of thermal effects.