• 한국구조물진단유지관리공학회 논문집
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• 제27권4호
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• pp.86-93
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• 2023

국내 교량들의 노후화 진행에 따라 구조물의 지속적인 안전관리를 위한 실시간 계측 기반의 교량 관리시스템이 필요하다. 현재 교량 계측시스템 기술은 대형 단일 교량의 계측을 중심으로 발전하여 유선을 기반으로 전원을 공급하고 계측 데이터를 수집한다. 하지만 산발적으로 분포하는 중소교량에는 위치적 문제로 인해 유선 기반 계측시스템을 적용하기 어렵다. 본 연구에서는 중소교량을 대상으로 무선 기반 계측시스템을 구축하는 방안을 제안하였다. 제안한 무선 기반 계측시스템은 기존 유선 기반의 계측시스템의 한계를 극복하기 위해 태양광 발전을 통해 무선 전원을 확보하였으며, LTE 통신을 활용하여 데이터를 송출하게 하였다. 또한, 교량 계측시스템의 관리를 위한 시스템 원격 제어 방안과 전원 관리 방안도 제안되었다. 제안한 계측시스템의 검증을 위해 실제 지방도상의 교량 32개소에 설치되었으며, 1년간의 장기 계측데이터를 수집하였다. 설치된 테스트 베드에서 80.6%의 계측데이터 취득이 가능함을 확인하여 제안한 계측시스템의 운용 가능성을 검증하였다. 제안된 시스템 구축방안은 지방도상 노후 교량들의 안전감시에 활용 가능할 것으로 기대된다.

• Smart Structures and Systems
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• 제14권5호
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• pp.917-942
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• 2014

Multivariate statistics based damage detection algorithms employed in conjunction with novel sensing technologies are attracting more attention for long term Structural Health Monitoring of civil infrastructure. In this study, two practical data driven methods are investigated utilizing strain data captured from a 4-span bridge model by Fiber Bragg Grating (FBG) sensors as part of a bridge health monitoring study. The most common and critical bridge damage scenarios were simulated on the representative bridge model equipped with FBG sensors. A high speed FBG interrogator system is developed by the authors to collect the strain responses under moving vehicle loads using FBG sensors. Two data driven methods, Moving Principal Component Analysis (MPCA) and Moving Cross Correlation Analysis (MCCA), are coded and implemented to handle and process the large amount of data. The efficiency of the SHM system with FBG sensors, MPCA and MCCA methods for detecting and localizing damage is explored with several experiments. Based on the findings presented in this paper, the MPCA and MCCA coupled with FBG sensors can be deemed to deliver promising results to detect both local and global damage implemented on the bridge structure.

• 한국구조물진단유지관리공학회 논문집
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• 제3권4호
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• pp.199-204
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• 1999

The continuing development of the sensors for the measurement of the safety of structures has been making a turning point in measuring and evaluating the larger civil structural system as well. However, there are still remaining problems to be solved for the extremely large structure because the natural damages of those structures are not so simple to be monitored for the reason of their locational and structural conditions. One of the most significant problems is that a number of cables which connect the measuring system to the analyzer are liable to distort actual data. This paper presents a new monitoring system for large structures by means of a local wireless communication technique which would eliminate the possibility of the distortion of data by noise in cables. This new monitoring system employs the wireless system and the software for data communication, along with the strain sensor and accelerometers which have been already used in the past. It makes it possible for the data, which have been chosen by the central controling system from the various sensors placed in the large civil structures, to be wirelessly delivered and then analyzed and evaluated by decision making system of the structures.

• Smart Structures and Systems
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• 제18권3호
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• pp.501-523
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• 2016

Electromechanical impedance (EMI) based structural health monitoring is performed by measuring the variation in the impedance due to the structural local damage. The impedance signals are acquired from the piezoelectric patches that are bonded on the structural surface. The impedance variation, which is directly related to the mechanical properties of the structure, indicates the presence of local structural damage. Two traditional EMI-based damage detection methods are based on calculating the difference between the measured impedance signals in the frequency domain from the baseline and the current structures. In this paper, a new structural damage detection approach by analyzing the time domain impedance responses is proposed. The measured time domain responses from the piezoelectric transducers will be used for analysis. With the use of the Time Frequency Autoregressive Moving Average (TFARMA) model, a damage index based on Singular Value Decomposition (SVD) is defined to identify the existence of the structural local damage. Experimental studies on a space steel truss bridge model in the laboratory are conducted to verify the proposed approach. Four piezoelectric transducers are attached at different locations and excited by a sweep-frequency signal. The impedance responses at different locations are analyzed with TFARMA model to investigate the effectiveness and performance of the proposed approach. The results demonstrate that the proposed approach is very sensitive and robust in detecting the bolt damage in the gusset plates of steel truss bridges.

• Smart Structures and Systems
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• 제15권2호
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• pp.283-297
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• 2015

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 an offshore structure, there are few cases to apply a 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 in Korea under changes in temperature and transverse loadings. Principal component analysis (PCA)-based approach was applied with a conventional damage index to eliminate environmental changes by removing principal components sensitive to them. Experimental results showed that the proposed approach is an effective tool for long-term SHM under significant environmental changes.

• Smart Structures and Systems
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• 제15권3호
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• pp.523-541
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• 2015

Structural health monitoring of steel truss bridge based on changes in modal properties was investigated in this study. Vibration measurements with five sensors were conducted at an existing Warren truss bridge with partial fractures in diagonal members before and after an emergency repair work. Modal properties identified by the Eigensystem Realization Algorithm showed evidences of increases in modal damping due to the damage in diagonal member. In order to understand the dynamic behavior of the bridge and possible mechanism of those increases in modal damping, theoretical modal analysis was conducted with three dimensional frame models. It was found that vibrations of the main truss could be coupled internally with local vibrations of diagonal members and the degree of coupling could change with structural changes in diagonal members. Additional vibration measurements with fifteen sensors were then conducted so as to understand the consistency of those theoretical findings with the actual dynamic behavior. Modal properties experimentally identified showed that the damping change caused by the damage in diagonal member described above could have occurred in a diagonal-coupled mode. The results in this study imply that damages in diagonal members could be detected from changes in modal damping of diagonal-coupled modes.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.21-26
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• 2007

This paper presents an impedance-based structural health monitoring (SHM) technique considering temperature effects. The temperature variation results in a significant impedance variation, particularly both horizontal and vertical shifts in the frequency domain, which may lead to erroneous diagnostic results of real structures. A new damage detection strategy has been proposed based on the correlation coefficient (CC) between the reference impedance data and a concurrent impedance data with an effective frequency shift which is defined as the shift causing the maximum correlation. The proposed technique was applied to a lab-sized steel truss bridge member under the temperature varying environment. From an experimental study, it has been demonstrated that a narrow cut inflicted artificially to the steel structure was successfully detected using the proposed SHM strategy.

• Structural Engineering and Mechanics
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• 제40권5호
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• pp.719-743
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• 2011

A hybrid damage monitoring scheme using parallel acceleration-impedance approaches is proposed to detect girder damage and support damage in steel plate-girder bridges which are under ambient train-induced excitations. The hybrid scheme consists of three phases: global and local damage monitoring in parallel manner, damage occurrence alarming and local damage identification, and detailed damage estimation. In the first phase, damage occurrence in a structure is globally monitored by changes in vibration features and, at the same moment, damage occurrence in local critical members is monitored by changes in impedance features. In the second phase, the occurrence of damage is alarmed and the type of damage is locally identified by recognizing patterns of vibration and impedance features. In the final phase, the location and severity of the locally identified damage are estimated by using modal strain energy-based damage index methods. The feasibility of the proposed scheme is evaluated on a steel plate-girder bridge model which was experimentally tested under model train-induced excitations. Acceleration responses and electro-mechanical impedance signatures were measured for several damage scenarios of girder damage and support damage.

• Structural Monitoring and Maintenance
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• 제10권4호
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• pp.299-314
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• 2023

Long-gauge carbon fiber line (CFL) sensors have received considerable attention in the past decade. However, there is still a need for an in-depth investigation of their measuring accuracy. This study investigates the accuracy of carbon fiber line sensors to monitor and differentiate the flexural behavior of two beams, one reinforced with steel bars alone and the other reinforced with steel and basalt fiber-reinforced polymer bars. A distributed set of long-gauge carbon fiber line, Fiber Bragg Grating (FBG), and traditional strain gauge sensors was mounted on the tensile concrete surface of the studied beams to compare the results and assess the accuracies of the proposed sensors. The test beams were loaded monotonically under four-point bending loading until failure. Results indicated the importance of using long-gauge sensors in providing useful, accurate, and reliable information regarding global structural behavior, while point sensors are affected by local damage and strain concentrations. Furthermore, long-gauge carbon fiber line sensors demonstrated good agreement with the corresponding Fiber Bragg Grating sensors with acceptable accuracy, thereby exhibiting potential for application in monitoring the health of large-scale structures.

• 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.