• 한국방재학회 논문집
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• 제9권5호
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• pp.9-13
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• 2009

현재까지 구조건전성 모니터링과 관련하여 제안된 대부분의 손상인식기법은 모달영역응답을 이용하고 있으나, 모달영역응답은 별도의 후처리가 필요하며 추출과정에서 오차를 포함하게 되므로 손상인식의 정확성을 저하시키는 요인이 되어왔다. 본 논문에서는 이동하중응답을 직접 이용하는 손상인식기법의 적용성을 실내 실험을 통하여 검증하였다. 실험은 강재로 만든 보에 이동하중을 재하시켜 수행하였으며, 보의 응답은 변위계를 이용하여 측정하였다. 이동하중은 쇠구슬과 활강장치를 이용하여 모사하였으며, 주기성과 비주기성 이동하중으로 구분하여 재하하였다. 계측된 응답을 이용한 손상인식 결과, 이동하중을 이용한 손상인식기법은 구조물의 손상을 성공적으로 탐지하는 것으로 나타났다.

• 대한토목학회논문집
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• 제28권1A호
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• pp.135-146
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• 2008

본 논문에서는 가속도 및 임피던스 신호를 이용하여 프리스트레스트 콘크리트(PSC) 거더교에 적합한 하이브리드 손상 모니터링 체계를 제안하였다. PSC 거더교의 주된 손상유형으로 텐던의 긴장력 감소와 콘크리트 거더의 휨 강성 저하를 고려하였다. 제안된 하이브리드 체계는 손상경보, 손상분류 및 손상평가와 같이 크게 3단계로 구성하였다. 첫 번째 단계에서는 가속도 특성 변화를 모니터링하여 전역적인 손상의 발생을 경보한다. 두 번째 단계에서는 임피던스 특성 변화를 모니터링하여 손상유형이 긴장력 감소인지 휨 강성 저하인지를 분류한다. 세 번째 단계에서는 손상유형에 적합한 손상평가기법을 이용하여 손상의 위치와 크기를 평가한다. 손상유형이 휨 강성 저하인 경우에서는 모드형상기반 손상검색 기법을 적용하였고, 손상유형이 긴장력 감소인 경우에서는 고유진동수기반 긴장력 추정 기법을 적용하였다. 모형 PSC 거더 실험을 통해 제안된 하이브리드 손상모니터링 체계의 유용성을 평가하였다.

• Structural Engineering and Mechanics
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• 제77권1호
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• pp.47-56
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• 2021

This paper deals with damage detection using a Gapped Smoothing Method (GSM) combined with deep learning. Convolutional Neural Network (CNN) is a model of deep learning. CNN has an input layer, an output layer, and a number of hidden layers that consist of convolutional layers. The input layer is a tensor with shape (number of images) × (image width) × (image height) × (image depth). An activation function is applied each time to this tensor passing through a hidden layer and the last layer is the fully connected layer. After the fully connected layer, the output layer, which is the final layer, is predicted by CNN. In this paper, a complete machine learning system is introduced. The training data was taken from a Finite Element (FE) model. The input images are the contour plots of curvature gapped smooth damage index. A free-free beam is used as a case study. In the first step, the FE model of the beam was used to generate data. The collected data were then divided into two parts, i.e. 70% for training and 30% for validation. In the second step, the proposed CNN was trained using training data and then validated using available data. Furthermore, a vibration experiment on steel damaged beam in free-free support condition was carried out in the laboratory to test the method. A total number of 15 accelerometers were set up to measure the mode shapes and calculate the curvature gapped smooth of the damaged beam. Two scenarios were introduced with different severities of the damage. The results showed that the trained CNN was successful in detecting the location as well as the severity of the damage in the experimental damaged beam.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.243-245
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• 2005

Condition monitoring is desirable for increasing machinery availability, reducing consequential damage, and improving operational efficiency. In this paper, a model-based method using neural network modeling of induction noter in vibration spectra is proposed for machine fault detection and diagnosis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals to continuous spectra so that the neural network model can be trained with vibration spectra. And the faults are detected from changes in the expectation of vibration spectra modeling error. The effectiveness of the proposed method is demonstrated through experimental results.

• Structural Engineering and Mechanics
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• 제54권1호
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• pp.105-119
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• 2015

Structural damage detection using modal strain energy (MSE) is one of the most efficient and reliable structural health monitoring techniques. However, some of the existing MSE methods have been validated for special types of structures such as beams or steel truss bridges which demands improving the available methods. The purpose of this study is to improve an efficient modal strain energy method to detect and quantify the damage in complex structures at early stage of formation. In this paper, a modal strain energy method was mathematically developed and then numerically applied to a fixed-end beam and a three-story frame including single and multiple damage scenarios in absence and presence of up to five per cent noise. For each damage scenario, all mode shapes and natural frequencies of intact structures and the first five mode shapes of assumed damaged structures were obtained using STRAND7. The derived mode shapes of each intact and damaged structure at any damage scenario were then separately used in the improved formulation using MATLAB to detect the location and quantify the severity of damage as compared to those obtained from previous method. It was found that the improved method is more accurate, efficient and convergent than its predecessors. The outcomes of this study can be safely and inexpensively used for structural health monitoring to minimize the loss of lives and property by identifying the unforeseen structural damages.

• Smart Structures and Systems
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• 제12권3_4호
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• pp.441-463
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• 2013

In this paper, a simplified planar model is developed for damage estimation of interlocked caisson systems. Firstly, a conceptual dynamic model of the interlocked caisson system is designed on the basis of the characteristics of existing harbor caisson structures. A mass-spring-dashpot model allowing only the sway motion is formulated. To represent the condition of interlocking mechanisms, each caisson unit is connected to adjacent ones via springs and dashpots. Secondly, the accuracy of the planar model's vibration analysis is numerically evaluated on a 3-D FE model of the interlocked caisson system. Finally, the simplified planar model is employed for damage estimation in the interlocked caisson system. For localizing damaged caissons, a damage detection method based on modal strain energy is formulated for the caisson system.

• 한국소음진동공학회논문집
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• 제22권10호
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• pp.1010-1019
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• 2012

Non-contact detection of internal defects in a rail using ultrasonic waves is discussed in this paper. Cracks in a rail may be the cause of a serious railway accident such as derailment if left undetected. Concurrent rail inspection method based on ultrasonic testing using piezoelectric transducers has several limitations as it should keep physical contact with the rail. This work suggests a non-contact detection of internal defects in a rail using ElectroMagnetic Acoustic Transducers (EMAT) which can produce and measure ultrasonic waves in a rail without any couplant. The EMATs for rail inspection are designed and fabricated and their working performance is verified through a series of experiments on various types of internal defects in test rails. The effect of lift-off between the transducers and the rail on the generated signals is also discussed.

• Structural Monitoring and Maintenance
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• 제6권2호
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• pp.125-145
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• 2019

Bridge scour is one of the predominant causes of bridge failure. Current climate deterioration leads to increase of flooding frequency and severity and thus poses a higher risk of bridge scour failure than before. Recent studies have explored extensively the vibration-based scour monitoring technique by analyzing the structural modal properties before and after damage. However, the state-of-art of this area lacks a systematic approach with sufficient robustness and credibility for practical decision making. This paper attempts to develop a data-driven methodology for bridge scour monitoring using support vector machines. This study extracts features from the bridge dynamic responses based on a generic sensitivity study on the bridge's modal properties and selects the features that are significantly contributive to bridge scour detection. Results indicate that the proposed data-driven method can quantify the bridge scour damage with satisfactory accuracy for most cases. This paper provides an alternative methodology for bridge scour evaluation using the machine learning method. It has the potential to be practically applied for bridge safety assessment in case that scour happens.

• Smart Structures and Systems
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• 제6권9호
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• pp.1057-1077
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• 2010

An early detection of structural damages is critical for the decision making of repair and replacement maintenance in order to guarantee a specified structural reliability. Consequently, the structural damage detection, based on vibration data measured from the structural health monitoring (SHM) system, has received considerable attention recently. The traditional time-domain analysis techniques, such as the least square estimation (LSE) method and the extended Kalman filter (EKF) approach, require that all the external excitations (inputs) be available, which may not be the case for some SHM systems. Recently, these two approaches have been extended to cover the general case where some of the external excitations (inputs) are not measured, referred to as the adaptive LSE with unknown inputs (ALSE-UI) and the adaptive EKF with unknown inputs (AEKF-UI). Also, new analysis methods, referred to as the adaptive sequential non-linear least-square estimation with unknown inputs and unknown outputs (ASNLSE-UI-UO) and the adaptive quadratic sum-squares error with unknown inputs (AQSSE-UI), have been proposed for the damage tracking of structures when some of the acceleration responses are not measured and the external excitations are not available. In this paper, these newly proposed analysis methods will be compared in terms of accuracy, convergence and efficiency, for damage identification of structures based on experimental data obtained through a series of laboratory tests using a scaled 3-story building model with white noise excitations. The capability of the ALSE-UI, AEKF-UI, ASNLSE-UI-UO and AQSSE-UI approaches in tracking the structural damages will be demonstrated and compared.

• Structural Engineering and Mechanics
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• 제24권6호
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• pp.665-682
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• 2006

A vibration-based nondestructive damage evaluation technique for a curved thin beam is introduced. The proposed method is capable of detecting, locating, and sizing structural damage simultaneously by using a few of the lower natural frequencies and their corresponding mode shapes before and after a small damage event. The proposed approach utilizes modal flexibilities reconstructed from measured modal parameters. A rigorous system of equations governing damage and curvature of modal flexibility is derived in the context of elasticity. To solve the resulting system of governing equations, an efficient pseudo-inverse technique is introduced. The direct inspection of the resulting solutions provides the location and severity of damage in a curved thin beam. This study confirms that there is a strong linear relationship between the curvature of modal flexibility and flexural damage in the selected class of structures. Several numerical case studies are provided to justify the performance of the proposed approach. The proposed method introduces a way to avoid the singularity and mode selection problems from earlier attempts.