• 한국구조물진단유지관리공학회 논문집
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• 제24권2호
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• pp.23-32
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• 2020

압축센싱 기술인 CAFB는 대상 구조물의 원시신호를 목적된 주파수 범위의 신호로 압축하여 획득하도록 개발되었다[27]. 이때 압축센싱을 위해 CAFB는 대상 구조물의 목적된 주파수 범위에 따라 다양한 기준신호로 최적화 될 수 있다. 또한, 최적화된 CAFB는 지진과 같은 돌발/위험상황에서도 대상 구조물의 유효한 구조응답을 효율적으로 압축할 수 있어야 한다. 본 논문에서는 상대적으로 유연한 구조물의 효율적인 구조 건전도 모니터링을 위하여 목적된 주파수 범위를 10Hz 미만으로 설정하고, 이를 위한 CAFB의 최적화 방법과 지진상황에서 CAFB의 지진응답성능을실험적으로 평가하였다. 이를 위해 본 논문에서는, 먼저 Kobe 지진파형을 이용하여 CAFB를 최적화하였고, 이를 자체 개발한 무선 IDAQ 시스템에 임베디드 하였다. 그리고, Kobe 지진파형을 이용하여 2경간 교량에 대한 지진응답실험을 수행하였다. 마지막으로 CAFB가 내장된 IDAQ 시스템을 이용하여 실시간으로 2경간 교량의 지진응답을 무선으로 획득하고, 획득된 압축신호는 원시신호와 상호 비교하였다. 실험의 결과로부터 압축신호는 원시신호와 대비하여 우수한 응답성능과 데이터 압축효과를 보였고, 또한 CAFB는 지진상황에서도 구조물의 유효한 구조응답을 효과적으로 압축센싱할 수 있었다. 최종적으로 본 논문에서는 목적된 주파수 범위(10Hz 미만)에 적합하도록 CAFB의 최적화 방법을 제시하였고, CAFB는 지진상황의 계측-모니터링을 위해 경제적이고 효율적인 데이터 압축센싱 기술임을 증명하였다.

• Structural Engineering and Mechanics
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• 제64권4호
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• pp.449-459
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• 2017

High-rise buildings are generally sensitive to strong winds. The evaluation of wind loads for the structural design, structural health monitoring (SHM), and vibration control of high-rise buildings is of primary importance. Nevertheless, it is difficult or even infeasible to measure the wind loads on an existing building directly. In this regard, a new inverse method for evaluating wind loads on high-rise buildings is developed in this study based on a discrete-time Kalman filter. The unknown structural responses are identified in conjunction with the wind loads on the basis of limited structural response measurements. The algorithm is applicable for estimating wind loads using different types of wind-induced response. The performance of the method is comprehensively investigated based on wind tunnel testing results of two high-rise buildings with typical external shapes. The stability of the proposed algorithm is evaluated. Furthermore, the effects of crucial factors such as cross-section shapes of building, the wind-induced response type, errors of structural modal parameters, covariance matrix of noise, noise levels in the response measurements and number of vibration modes on the identification accuracy are examined through a detailed parametric study. The research outputs of the proposed study will provide valuable information to enhance our understanding of the effects of wind on high-rise buildings and improve codes of practice.

• 한국지진공학회논문집
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• 제27권6호
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• pp.231-236
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• 2023

The importance of Structural Health Monitoring (SHM) in the industry is increasing due to various loads, such as earthquakes and wind, having a significant impact on the performance of structures and equipment. Estimating responses is crucial for the effective health management of these assets. However, using numerous sensors in facilities and equipment for response estimation causes economic challenges. Additionally, it could require a response from locations where sensors cannot be attached. Digital twin technology has garnered significant attention in the industry to address these challenges. This paper constructs a digital twin system utilizing the Long Short-Term Memory (LSTM) model to estimate responses in a pipe system under simultaneous seismic load and arbitrary loads. The performance of the data-driven digital twin system was verified through a comparative analysis of experimental data, demonstrating that the constructed digital twin system successfully estimated the responses.

• 비파괴검사학회지
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• 제36권6호
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• pp.460-465
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• 2016

배관 구조물은 긴 길이를 가지며, 일정한 거리에 위치한 고정부에 설치되거나, 지중에 매설된다. 따라서 자중 또는 지반의 움직임으로 변형과 처짐이 발생하기 쉽다. 이러한 배관의 건전성 평가에는 형상 감시 기법이 매우 유용할 수 있다. 광섬유 브래그 격자 센서 (fiber Bragg grating, FBG)는 다중화의 장점이 있어 배관과 같이 긴 길이를 가지는 구조물의 여러 지점에서 변형률 측정에 매우 유용하다. 본 연구에서는 배관의 건전성 평가를 위하여 변형률 기반의 형상추정기법을 제안하였다. 제안된 기법의 유용성을 확인하기 위하여 실험을 통한 검증을 수행하였다. 실험 결과 제안된 FBG를 이용한 형상추정기법이 시험편의 변형에 따라 유사한 형상을 표현할 수 있음을 확인하였다. 또한, 형상추정기법을 통해 도출된 처짐량이 실제 배관에 가해진 처짐과 동일하게 계산됨을 확인하였다.

• Smart Structures and Systems
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• 제20권6호
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• pp.769-780
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• 2017

Cable force monitoring is an essential and critical part of the safety evaluation of cable-supported bridges. A reasonable cable force monitoring scheme, particularly, sensor placement related to accurate safety assessment and budget cost-saving becomes a major concern of bridge administrative authorities. This paper presents optimal sensor placement for cable force monitoring by selecting representative sensor positions, which consider the spatial correlativeness existing in the cable group. The limited sensors would be utilized for maximizing useful information from the monitored bridges. The maximum information coefficient (MIC), mutual information (MI) based kernel density estimation, as well as Pearson coefficients, were all employed to detect potential spatial correlation in the cable group. Compared with the Pearson coefficient and MIC, the mutual information is more suitable for identifying the association existing in cable group and thus, is selected to describe the spatial relevance in this study. Then, the bond energy algorithm, which collects clusters based on the relationship of surrounding elements, is used for the optimal placement of cable sensors. Several optimal placement strategies are discussed with different correlation thresholds for the cable group of Nanjing No.3 Yangtze River Bridge, verifying the effectiveness of the proposed method.

• 비파괴검사학회지
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• 제34권2호
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• pp.165-170
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• 2014

수평횡파(SH-wave)는 배관과 같은 박판 구조물에서 판파 모드로 전파하고 분산성이 단순하여 구조물의 건전성 평가에 유용하다. 전자기초음파탐촉자(EMAT)는 비접촉식으로 자석과 코일 배열을 조절하여 수평횡파를 발생하기 용이하다. 따라서 수평횡파 전자기초음파탐촉자(SH-EMAT)를 이용한 자동화 검사 시스템은 박판 구조물 건전성 감시에 매우 유용하다. 하지만 발전설비 또는 자동화 장비 등에서는 전자기노이즈가 상당히 많이 발생하고 EMAT 수신 센서는 전자기노이즈에 취약한 면이 있다. 광섬유 센서는 빛을 이용하여 전자기노이즈 환경에서 매우 유용하게 활용될 수 있다. 본 연구에서는 이러한 환경적 제약을 고려하여 광섬유 패브리-페롯 간섭계(FFPI)를 SH-EMAT으로 발생되는 초음파의 수신용 센서로 제안하였다. 평판시험편에서 SH-EMAT의 신호를 FFPI 센서를 이용하여 수신하고 이 신호에 대한 분석을 하였다. 제안된 FFPI 센서가 EMAT으로 가진된 SH wave의 신호를 명료하게 수신할 수 있음을 확인하였다.

• Smart Structures and Systems
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• 제31권4호
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• pp.351-363
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• 2023

Visual structural inspections are an inseparable part of post-earthquake damage assessments. With unmanned aerial vehicles (UAVs) establishing a new frontier in visual inspections, there are major computational challenges in processing the collected massive amounts of high-resolution visual data. We propose twin deep learning models that can provide accurate high-resolution structural components and damage segmentation masks efficiently. The traditional approach to cope with high memory computational demands is to either uniformly downsample the raw images at the price of losing fine local details or cropping smaller parts of the images leading to a loss of global contextual information. Therefore, our twin models comprising Trainable Resizing for high-resolution Segmentation Network (TRS-Net) and DmgFormer approaches the global and local semantics from different perspectives. TRS-Net is a compound, high-resolution segmentation architecture equipped with learnable downsampler and upsampler modules to minimize information loss for optimal performance and efficiency. DmgFormer utilizes a transformer backbone and a convolutional decoder head with skip connections on a grid of crops aiming for high precision learning without downsizing. An augmented inference technique is used to boost performance further and reduce the possible loss of context due to grid cropping. Comprehensive experiments have been performed on the 3D physics-based graphics models (PBGMs) synthetic environments in the QuakeCity dataset. The proposed framework is evaluated using several metrics on three segmentation tasks: component type, component damage state, and global damage (crack, rebar, spalling). The models were developed as part of the 2^nd International Competition for Structural Health Monitoring.

• 한국지진공학회논문집
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• 제10권6호
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• pp.115-123
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• 2006

본 논문에서는 구조건전성모니터링(SHM)시스템이 설치된 케이블교량을 대상으로 장기적인 동적거동 특성을 분석하였다. 3차원 유한요소모델을 사용한 모드해석을 통해 모드변수를 추출하였다. 이를 교량에서 측정된 상시진동신호에 대해 주파수영역에서 분석한 고유진동수와 비교하여 정확한 기저모델이 구축되었음을 알 수 있었다. 지난 5년간의 고유진동수와 온도를 통계분석하여 고유진동수가 온도에 선형 반비례하고 있음을 확인하였고, 이러한 온도효과에 대한 평가를 수행하였다. 또한 상시진동신호를 시간영역에서 TDD기법을 적용하여 모드형상을 추출하였으며, 모드해석 결과와의 비교를 통해 케이블교량에 적용이 가능함을 검증하였다.

• Smart Structures and Systems
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• 제29권1호
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• pp.17-28
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• 2022

The cable component of cable-stayed bridges is gradually impacted by weather conditions, vehicle loads, and material corrosion. The stayed cable is a critical load-carrying part that closely affects the operational stability of a cable-stayed bridge. Damaged cables might lead to the bridge collapse due to their tension capacity reduction. Thus, it is necessary to develop structural health monitoring (SHM) techniques that accurately identify damaged cables. In this work, a combinational identification method of three efficient techniques, including statistical analysis, clustering, and neural network models, is proposed to detect the damaged cable in a cable-stayed bridge. The measured dataset from the bridge was initially preprocessed to remove the outlier channels. Then, the theory and application of each technique for damage detection were introduced. In general, the statistical approach extracts the parameters representing the damage within time series, and the clustering approach identifies the outliers from the data signals as damaged members, while the deep learning approach uses the nonlinear data dependencies in SHM for the training model. The performance of these approaches in classifying the damaged cable was assessed, and the combinational identification method was obtained using the voting ensemble. Finally, the combination method was compared with an existing outlier detection algorithm, support vector machines (SVM). The results demonstrate that the proposed method is robust and provides higher accuracy for the damaged cable detection in the cable-stayed bridge.

• 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.