• 대한토목학회논문집
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• 제31권4A호
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• pp.287-293
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• 2011

본 연구에서는 교량의 진동을 이용한 압전 외팔보 에너지 수확장치의 적용성을 연구하였다. 이를 위하여 압전 소자의 구성방정식과 외팔보의 진동방정식을 결합하여 외팔보의 단일 모드에 대한 연성 방정식을 행렬 형태로 구성하였다. 그리고 에너지 수확장치의 가진기 실험을 통하여 해석 모델의 타당성을 검증하였다. KTX, 새마을, 무궁화 열차가 주행할 때 측정된 교량 가속도를 바탕으로 수치해석을 통하여 산정한 에너지 수확장치의 최대 전력은 각각 28.5 mW, 0.65 mW, 0.51 mW로 나타났다. 이를 볼 때 철도와 같은 이동하중에 의한 교량의 진동은 가진 진동수와 가속도가 낮고 지속시간이 짧아서 에너지 공급원으로서 효율성이 떨어지는 것으로 판단된다.

• 대한토목학회논문집
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• 제40권2호
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• pp.233-238
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• 2020

구조물 건전도 모니터링 시스템을 기반하는 교량 딥러닝 손상 추정 기법들은 대부분 지도학습을 기반으로 하고 있다. 지도학습의 특성상 손상 위치 추정 딥러닝 모델의 학습을 위해 교량의 손상 위치를 나타내는 라벨(Label) 데이터와 이에 따른 교량의 거동 데이터가 필요하다. 하지만 실제 현장에서 손상 위치 라벨 데이터를 정확히 얻어내는 것은 매우 어려운 일이므로, 지도학습 기반 딥러닝은 현장 적용성이 떨어진다는 한계가 있다. 반면에, 비지도학습 기반 딥러닝은 이러한 라벨 데이터 없이도 학습이 가능하다는 장점이 있다. 이러한 점에 착안하여 본 연구에서는 비지도 학습의 대표적인 딥러닝 기법인 Variational Autoencoder를 활용한 교량 손상 위치 추정의 방법을 제안하고 검증하였으며, 그 결과, 교량 손상 위치 추정을 위한 VAE의 적용 가능성을 보였다.

• Smart Structures and Systems
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• 제15권3호
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• pp.665-682
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• 2015

Structural identification or St-Id is 'the parametric correlation of structural response characteristics predicted by a mathematical model with analogous characteristics derived from experimental measurements'. This paper describes a St-Id exercise on Humber Bridge that adopted a novel two-stage approach to first calibrate and then validate a mathematical model. This model was then used to predict effects of wind and temperature loads on global static deformation that would be practically impossible to observe. The first stage of the process was an ambient vibration survey in 2008 that used operational modal analysis to estimate a set of modes classified as vertical, torsional or lateral. In the more recent second stage a finite element model (FEM) was developed with an appropriate level of refinement to provide a corresponding set of modal properties. A series of manual adjustments to modal parameters such as cable tension and bearing stiffness resulted in a FEM that produced excellent correspondence for vertical and torsional modes, along with correspondence for the lower frequency lateral modes. In the third stage traffic, wind and temperature data along with deformation measurements from a sparse structural health monitoring system installed in 2011 were compared with equivalent predictions from the partially validated FEM. The match of static response between FEM and SHM data proved good enough for the FEM to be used to predict the un-measurable global deformed shape of the bridge due to vehicle and temperature effects but the FEM had limited capability to reproduce static effects of wind. In addition the FEM was used to show internal forces due to a heavy vehicle to to estimate the worst-case bearing movements under extreme combinations of wind, traffic and temperature loads. The paper shows that in this case, but with limitations, such a two-stage FEM calibration/validation process can be an effective tool for performance prognosis.

• Smart Structures and Systems
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• 제22권4호
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• pp.481-493
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• 2018

Ultrasonic guided waves have attracted increasing attention for non-destructive testing (NDT) and structural health monitoring (SHM) of bridge cables. They offer advantages like single measurement, wide coverage of acoustical field, and long-range propagation capability. To design defect detection systems, it is essential to understand how guided waves propagate in cables and how to select the optimal excitation frequency and mode. However, certain cable characteristics such as multiple wires, anchorage, and polyethylene (PE) sheath increase the complexity in analyzing the guided wave propagation. In this study, guided wave modes for multi-wire bridge cables are identified by using a semi-analytical finite element (SAFE) technique to obtain relevant dispersion curves. Numerical results indicated that the number of guided wave modes increases, the length of the flat region with a low frequency of L(0,1) mode becomes shorter, and the cutoff frequency for high order longitudinal wave modes becomes lower, as the number of steel wires in a cable increases. These findings were used in design of transducers for defect detection and selection of the optimal wave mode and frequency for subsequent experiments. A magnetostrictive transducer system was used to excite and detect the guided waves. The applicability of the proposed approach for detecting and locating wire breakages was demonstrated for a cable with 37 wires. The present ultrasonic guided wave method has been found to be very responsive to the number of brokenwires and is thus capable of detecting defects with varying sizes.

• Smart Structures and Systems
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• 제32권4호
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• pp.267-279
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• 2023

Data modelling and interpretation for structural health monitoring (SHM) field data are critical for evaluating structural performance and quantifying the vulnerability of infrastructure systems. In order to improve the data modelling accuracy, and extend the application range from data regression analysis to out-of-sample forecasting analysis, an improved most likely heteroscedastic Gaussian process (iMLHGP) methodology is proposed in this study by the incorporation of the outof-sample forecasting algorithm. The proposed iMLHGP method overcomes this limitation of constant variance of Gaussian process (GP), and can be used for estimating non-stationary typhoon-induced response statistics with high volatility. The first attempt at performing data regression and forecasting analysis on structural responses using the proposed iMLHGP method has been presented by applying it to real-world filed SHM data from an instrumented cable-stay bridge during typhoon events. Uncertainty quantification and correlation analysis were also carried out to investigate the influence of typhoons on bridge strain data. Results show that the iMLHGP method has high accuracy in both regression and out-of-sample forecasting. The iMLHGP framework takes both data heteroscedasticity and accurate analytical processing of noise variance (replace with a point estimation on the most likely value) into account to avoid the intensive computational effort. According to uncertainty quantification and correlation analysis results, the uncertainties of strain measurements are affected by both traffic and wind speed. The overall change of bridge strain is affected by temperature, and the local fluctuation is greatly affected by wind speed in typhoon conditions.

• 한국컴퓨터정보학회논문지
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• 제16권9호
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• pp.45-55
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• 2011

사용자가 필요로 하는 서비스를 제공하기 위해서는, 일상생활 곳곳에 편재된 센서가 수집한 각종 환경 정보를 효율적으로 상호 공유하여 주변 상황을 인식하는 기술이 필요하다. 그러나 이를 위한 센서의 종류는 다양하고 각각의 센서는 고유의 특성과 서로 다른 방식으로 통신을 하기 때문에 센서의 활용에 제한이 있다. 이에 센서와 네트워크 계층과의 통신 프로토콜 표준화를 위한 IEEE 1451이 발표되었다. 그러나 IEEE 1451은 트랜스듀서(Transducer)의 표준화를 위한 프로토콜이므로 미들웨어에 접속되지 않는다. 본 논문에서는 XML을 이용하여 주변상황 정보를 얻기 위한 프로토콜을 정의함으로써 상황인식 미들웨어에 연결되는 트랜스듀서 인터페이스 및 응용 인터페이스 프로토콜을 제안한다. 그리고 서로 다른 센서와 응용 프로그램을 이용한 교량 건전성 감시 시스템과 철로 감시 시스템을 구현하고 제안한 인터페이스 프로토콜의 효용성을 확인하였다.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2009년도 세계 도시지반공학 심포지엄
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• pp.117-129
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• 2009

This paper studied the safety management network system of infrastructure which constructed smart sensors, closed-circuit television(CCTV) and monitoring system. This safety management of infrastructure applied to bridge, cut slop and tunnel, embankment etc. The system applied to technologies of standardization guidelines, data acquirement technologies, data analysis and judgment technologies, system integration setup technology, and IT technologies. It was constructed safety management network system of various infrastructure to improve efficient management and operation for many infrastructure. Integrated safety management network system of infrastructure consisted of the real-time structural health monitoring system of each infrastructure, integrated control center, measured data transmission using i of tet web-based, collecting data using sf ver, early alarm system which the dangerous event of infrastructure occurred. Integrated control center consisted of conference room, control room to manage and analysis the data, server room to present the measured data and to collect the raw data. Early alarm system proposed realization of warning and response within 5 minute or less through development of sensor-based progress report and propagation automation system using the media such as MMS, VMS, EMS, FMS, SMS and web services of report and propagation. Based on this, the most effective u-Infrastructure Safety Management System is expected to be stably established at a less cost, thus making people's life more comfortable. Information obtained from such systems could be useful for maintenance or structural safety evaluation of existing structures, rapid evaluation of conditions of damaged structures after an earthquake, estimation of residual life of structures, repair and retrofitting of structures, maintenance, management or rehabilitation of historical structures.

• Smart Structures and Systems
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• 제33권2호
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• pp.93-103
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• 2024

Despite the rapid development of sensors, structural health monitoring (SHM) still faces challenges in monitoring due to the degradation of devices and harsh environmental loads. These challenges can lead to measurement errors, missing data, or outliers, which can affect the accuracy and reliability of SHM systems. To address this problem, this study proposes a classification method that detects anomaly patterns in sensor data. The proposed classification method involves several steps. First, data scaling is conducted to adjust the scale of the raw data, which may have different magnitudes and ranges. This step ensures that the data is on the same scale, facilitating the comparison of data across different sensors. Next, informative features in the time and frequency domains are extracted and used as input for a deep neural network model. The model can effectively detect the most probable anomaly pattern, allowing for the timely identification of potential issues. To demonstrate the effectiveness of the proposed method, it was applied to actual data obtained from a long-span cable-stayed bridge in China. The results of the study have successfully verified the proposed method's applicability to practical SHM systems for civil infrastructures. The method has the potential to significantly enhance the safety and reliability of civil infrastructures by detecting potential issues and anomalies at an early stage.

• Smart Structures and Systems
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• 제29권1호
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• 한국전산구조공학회논문집
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• 제34권5호
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• pp.301-308
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• 2021

사회기반 시설물의 노후화에 대응해 이상 징후를 파악하고 유지보수를 위한 최적의 의사결정을 내리기 위해선 디지털 기반 SOC 시설물 유지관리 시스템의 개발이 필수적인데, 디지털 SOC 시스템은 장기간 구조물 계측을 위한 IoT 센서 시스템과 축적 데이터 처리를 위한 클라우드 컴퓨팅 기술을 요구한다. 본 연구에서는 구조물의 다물리량을 장기간 측정할 수 있는 IoT센서와 클라우드 컴퓨팅을 위한 서버 시스템을 개발하였다. 개발 IoT센서는 총 3축 가속도 및 3채널의 변형률 측정이 가능하고 24비트의 높은 해상도로 정밀한 데이터 수집을 수행한다. 또한 저전력 LTE-CAT M1 통신을 통해 데이터를 실시간으로 서버에 전송하여 별도의 중계기가 필요 없는 장점이 있다. 개발된 클라우드 서버는 센서로부터 다물리량 데이터를 수신하고 가속도, 변형률 기반 변위 융합 알고리즘을 내장하여 센서에서의 연산 없이 고성능 연산을 수행한다. 제안 방법의 검증은 2개소의 실제 교량에서 변위계와의 계측 결과 비교, 장기간 운영 테스트를 통해 이뤄졌다.