• 한국산학기술학회논문지
• /
• 제19권5호
• /
• pp.611-617
• /
• 2018

대형구조물의 효과적인 구조 안전성 확보를 위해서는 결함탐지기술을 포함한 건전성 모니터링의 적용이 필요하다. 본 연구에서는 보 구조물에 발생하는 균열위치와 균열크기를 추정하기 위하여 다음과 같은 2단계의 균열추정방법을 제안한다. 우선, 보 구조물의 분포 국부 변형률 계측결과를 이용하여 변형률 모드형상을 구하고, 이에 대한 수정 라플라시안(Laplacian) 연산을 통하여 균열발생 영역을 추정한다. 이후, 가속도 측정을 통하여 구한 고유주파수와 신경망기법을 이용하여, 미리 추정된 균열발생 영역을 대상으로 균열위치와 균열크기를 추정한다. 이때, 신경망을 훈련시키기 위하여, 에너지법에 의해 유도된 균열보의 등가휨강성을 이용하여 균열보의 고유주파수를 해석적으로 구한다. 기법을 검증하기 위하여 알루미늄 캔틸레버 보에 대한 손상실험을 수행하였다. 인위적으로 실험체에 균열을 가한 후 자유진동실험을 수행하여 동적 변형률과 가속도를 계측하고 이를 이용하여 변형률 모드형상과 고유주파수를 구하였다. 변형률 모드형상에 대한 수정 라플라시안 연산을 통하여 균열발생 영역을 추정하고, 고유주파수와 신경망기법을 이용하여, 미리 추정된 균열발생 영역에 대하여 균열위치와 균열크기를 판정하였다. 3가지 손상경우에 대한 균열발생 영역의 추정결과는 실제 영역과 잘 일치하였으며, 균열위치와 균열크기 추정결과의 정확성을 상당히 향상시킬 수 있었다. 제안된 기법은 장대구조물에 대한 구조물 건전성 모니터링에 효과적으로 활용될 수 있을 것으로 판단된다.

• Smart Structures and Systems
• /
• 제14권4호
• /
• pp.679-697
• /
• 2014

The strain data acquired from structural health monitoring (SHM) systems play an important role in the state monitoring and damage identification of bridges. Due to the environmental complexity of civil structures, a better understanding of the actual strain data will help filling the gap between theoretical/laboratorial results and practical application. In the study, the multi-scale features of strain response are first revealed after abundant investigations on the actual data from two typical long-span bridges. Results show that, strain types at the three typical temporal scales of $10^5$, $10^2$ and $10^0$ sec are caused by temperature change, trains and heavy trucks, and have their respective cut-off frequency in the order of $10^{-2}$, $10^{-1}$ and $10^0$ Hz. Multi-resolution analysis and wavelet shrinkage are applied for separating and extracting these strain types. During the above process, two methods for determining thresholds are introduced. The excellent ability of wavelet transform on simultaneously time-frequency analysis leads to an effective information extraction. After extraction, the strain data will be compressed at an attractive ratio. This research may contribute to a further understanding of actual strain data of long-span bridges; also, the proposed extracting methodology is applicable on actual SHM systems.

• Smart Structures and Systems
• /
• 제10권2호
• /
• pp.131-154
• /
• 2012

In this paper, it is aimed to determine the seismic behaviour of highway bridges by nondestructive testing using ambient vibration measurements. Eynel Highway Bridge which has arch type structural system with a total length of 216 m and located in the Ayvaclk county of Samsun, Turkey is selected as an application. The bridge connects the villages which are separated with Suat U$\breve{g}$urlu Dam Lake. A three dimensional finite element model is first established for a highway bridge using project drawings and an analytical modal analysis is then performed to generate natural frequencies and mode shapes in the three orthogonal directions. The ambient vibration measurements are carried out on the bridge deck under natural excitation such as traffic, human walking and wind loads using Operational Modal Analysis. Sensitive seismic accelerometers are used to collect signals obtained from the experimental tests. To obtain experimental dynamic characteristics, two output-only system identification techniques are employed namely, Enhanced Frequency Domain Decomposition technique in the frequency domain and Stochastic Subspace Identification technique in time domain. Analytical and experimental dynamic characteristic are compared with each other and finite element model of the bridge is updated by changing of boundary conditions to reduce the differences between the results. It is demonstrated that the ambient vibration measurements are enough to identify the most significant modes of highway bridges. After finite element model updating, maximum differences between the natural frequencies are reduced averagely from 23% to 3%. The updated finite element model reflects the dynamic characteristics of the bridge better, and it can be used to predict the dynamic response under complex external forces. It is also helpful for further damage identification and health condition monitoring. Analytical model of the bridge before and after model updating is analyzed using 1992 Erzincan earthquake record to determine the seismic behaviour. It can be seen from the analysis results that displacements increase by the height of bridge columns and along to middle point of the deck and main arches. Bending moments have an increasing trend along to first and last 50 m and have a decreasing trend long to the middle of the main arches.

• 한국소음진동공학회:학술대회논문집
• /
• 한국소음진동공학회 2008년도 춘계학술대회논문집
• /
• pp.647-652
• /
• 2008

This paper focused on the application of finite element model updating technique to evaluate the structural properties of the reinforced concrete specimen using the data collected from shaking table tests. The specimen was subjected to six El Centro(NS, 1942) ground motion histories with different Peak Ground Acceleration(PGA) ranging from 0.06g to 0.50g. For model updating, flexural stiffness values of structural members(walls and slabs) were chosen as the updating parameters so that the converged results have direct physical interpretations. Initial values for finite element model were determined from the member dimensions and material properties. Frequency response functions(i.e. transfer functions), natural frequencies and mode shapes were obtained using the acceleration measurement at each floor and given ground acceleration history. The weighting factors were used to account for the relative confidence in different types of inputs for updating(i.e. transfer function and natural frequencies). The constraints based on upper/lower bound of parameters and sensitivity-based constraints were implemented to the updating procedure in this study using standard bounded variable least-squares(BVLS) method. The veracity of the updated finite element model was investigated by comparing the predicted and measured responses. The results indicated that the updated model replicates the dynamic behavior of the specimens reasonably well. At each stage of shaking, severity of damage that results from cracking of the reinforced concrete member was quantified from the updated parameters(i.e. flexural stiffness values).

• 한국소음진동공학회논문집
• /
• 제18권7호
• /
• pp.725-731
• /
• 2008

This paper focused on the application of finite element model updating technique to evaluate the structural properties of the reinforced concrete specimen using the data collected from shaking table tests. The specimen was subjected to six El Centre (NS, 1942) ground motion histories with different peak ground acceleration (PGA) ranging from 0.06 g to 0.50 g. For model updating, flexural stiffness values of structural members (walls and slabs) were chosen as the updating parameters so that the converged results have direct physical interpretations. Initial values for finite element model were determined from the member dimensions and material properties. Frequency response functions (i.e. transfer functions), natural frequencies and mode shapes were obtained using the acceleration measurement at each floor and given ground acceleration history. The weighting factors were used to account for the relative confidence in different types of Inputs for updating (j.e. transfer function and natural frequencies) The constraints based on upper/lower bound of parameters and sensitivity-based constraints were implemented to the updating procedure in this study using standard bounded variable least-squares(BVLS) method. The veracity of the updated finite element model was investigated by comparing the predicted and measured responses. The results indicated that the updated model replicates the dynamic behavior of the specimens reasonably well. At each stage of shaking, severity of damage that results from cracking of the reinforced concrete member was quantified from the updated parameters (i.e. flexural stiffness values).

• 한국구조물진단유지관리공학회 논문집
• /
• 제13권5호통권57호
• /
• pp.169-179
• /
• 2009

본 논문의 목적은 열화상 비파괴 검사기법을 적용시 손상된 콘크리트 구조물의 표면온도를 증폭시키기 위해 사용되는 외부 열원의 효율성을 알아보기 위함이다. 원적외선램프와 할로겐램프의 적용성과 효율성을 서로 비교하였다. 이를 위해 전술한 두 개의 열원을 콘크리트의 내부공극과 FRP쉬트의 비부착 결함 시험체에 적용하였다. 본 연구결과, 원적외선램프가 할로겐램프보다 더 효율적인 것으로 파악되었다. 또한, 손상영역을 효과적으로 검출하기위해서 가우스 필터와 프리윗 마스크 화상처리기법을 혼합한 알고리즘을 제안하였다.

• Smart Structures and Systems
• /
• 제29권1호
• /
• pp.77-91
• /
• 2022

Various monitoring systems have been implemented in civil infrastructure to ensure structural safety and integrity. In long-term monitoring, these systems generate a large amount of data, where anomalies are not unusual and can pose unique challenges for structural health monitoring applications, such as system identification and damage detection. Therefore, developing efficient techniques is quite essential to recognize the anomalies in monitoring data. In this study, several machine learning techniques are explored and implemented to detect and classify various types of data anomalies. A field dataset, which consists of one month long acceleration data obtained from a long-span cable-stayed bridge in China, is employed to examine the machine learning techniques for automated data anomaly detection. These techniques include the statistic-based pattern recognition network, spectrogram-based convolutional neural network, image-based time history convolutional neural network, image-based time-frequency hybrid convolution neural network (GoogLeNet), and proposed ensemble neural network model. The ensemble model deliberately combines different machine learning models to enhance anomaly classification performance. The results show that all these techniques can successfully detect and classify six types of data anomalies (i.e., missing, minor, outlier, square, trend, drift). Moreover, both image-based time history convolutional neural network and GoogLeNet are further investigated for the capability of autonomous online anomaly classification and found to effectively classify anomalies with decent performance. As seen in comparison with accuracy, the proposed ensemble neural network model outperforms the other three machine learning techniques. This study also evaluates the proposed ensemble neural network model to a blind test dataset. As found in the results, this ensemble model is effective for data anomaly detection and applicable for the signal characteristics changing over time.

• Smart Structures and Systems
• /
• 제9권3호
• /
• pp.287-301
• /
• 2012

It is well known that overloaded vehicles may cause severe damages to bridges, and how to estimate and evaluate the status of the overloaded vehicles passing through bridges become a challenging problem. Therefore, based on the monitored strain data from a structural health monitoring system (SHM) installed on a bridge, a method is recommended to identify and analyze the probability of overloaded vehicles. Overloaded vehicle loads can cause abnormity in the monitored strains, though the abnormal strains may be small in a concrete continuous rigid frame bridge. Firstly, the abnormal strains are identified from the abundant strains in time sequence by taking the advantage of wavelet transform in abnormal signal identification; secondly, the abnormal strains induced by heavy vehicles are picked up by the comparison between the identified abnormal strains and the strain threshold gotten by finite element analysis of the normal heavy vehicle; finally, according to the determined abnormal strains induced by overloaded vehicles, the statistics of the overloaded vehicles passing through the bridge are summarized and the whole probability of the overloaded vehicles is analyzed. The research shows the feasibility of using the monitored strains from a long-term SHM to identify the information of overloaded vehicles passing through a bridge, which can help the traffic department to master the heavy truck information and do the damage analysis of bridges further.

• 콘크리트학회논문집
• /
• 제24권6호
• /
• pp.737-744
• /
• 2012

강재형 댐퍼는 주로 철골구조에서 많이 사용되어 왔으나 최근에 들어 철근콘크리트 건물에도 사용빈도가 증가하는 추세이다. 철근콘크리트 건물에 강재이력댐퍼를 적용하기 위해서는 댐퍼의 접합부재가 댐퍼의 지지능력을 보 및 벽체로 전달하기에 적합한 강도와 강성을 지녀야만 한다. 하지만 균열로 인한 철근콘크리트 요소의 손상은 부득이한 것으로, 댐퍼로부터 지지부재로의 하중전달 메커니즘과 댐퍼 지지부재 이력특성의 규명은 이러한 댐퍼의 거동을 평가하는데 매우 중요하다. 이에 이 연구에서는 EaSy 댐퍼와 같은 강재판형 이력댐퍼의 지지부재와 RC벽체와의 접합상세를 대상으로 실험을 실시하였다. 실험 결과 전단과 관련된 균열의 양과 패턴을 제외하고는 모든 실험체의 파괴패턴은 거의 동일한 것으로 나타났으며, 잘 분산된 균열을 지닌 HD-3 실험체가 에너지소산능력, 강성저하 그리고 강도저하 측면에서 좋은 거동을 보여주었다.

• 대한조선학회논문집
• /
• 제52권1호
• /
• pp.88-95
• /
• 2015

For the frequency-domain spectral fatigue analysis, the probability density function of stress range needs to be estimated based on the stress spectrum only, which is a frequency domain representation of the response. The probability distribution of the stress range of the narrow-band spectrum is known to follow the Rayleigh distribution, however the PDF of wide-band spectrum is difficult to define with clarity due to the complicated fluctuation pattern of spectrum. In this paper, efforts have been made to figure out the links between the probability density function of stress range to the structural response of wide-band Gaussian random process. An artificial neural network scheme, known as one of the most powerful system identification methods, was used to identify the multivariate functional relationship between the idealized wide-band spectrums and resulting probability density functions. To achieve this, the spectrums were idealized as a superposition of two triangles with arbitrary location, height and width, targeting to comprise wide-band spectrum, and the probability density functions were represented by the linear combination of equally spaced Gaussian basis functions. To train the network under supervision, varieties of different wide-band spectrums were assumed and the converged probability density function of the stress range was derived using the rainflow counting method and all these data sets were fed into the three layer perceptron model. This nonlinear least square problem was solved using Levenberg-Marquardt algorithm with regularization term included. It was proven that the network trained using the given data set could reproduce the probability density function of arbitrary wide-band spectrum of two triangles with great success.