• Structural Engineering and Mechanics
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• 제63권6호
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• pp.751-760
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• 2017

In recent years, multifractal-based analysis methods have been widely applied in engineering. Among these methods, multifractal detrended cross-correlation analysis (MFDXA), a branch of fractal analysis, has been successfully applied in the fields of finance and biomedicine. For its great potential in reflecting the subtle characteristic among signals, a structural health monitoring (SHM) system based on MFDXA is proposed. In this system, damage assessment is conducted by exploiting the concept of multifractal theory to quantify the complexity of the vibration signal measured from a structure. According to the proposed algorithm, the damage condition is first distinguished by multifractal detrended fluctuation analysis. Subsequently, the relationship between the q-order, q-order detrended covariance, and length of segment is further explored. The dissimilarity between damaged and undamaged cases is visualized on contour diagrams, and the damage location can thus be detected using signals measured from different floors. Moreover, a damage index is proposed to efficiently enhance the SHM process. A seven-story benchmark structure, located at the National Center for Research on Earthquake Engineering (NCREE), was employed for an experimental verification to demonstrate the performance of the proposed SHM algorithm. According to the results, the damage condition and orientation could be correctly identified using the MFDXA algorithm and the proposed damage index. Since only the ambient vibration signal is required along with a set of initial reference measurements, the proposed SHM system can provide a lower cost, efficient, and reliable monitoring process.

• Structural Monitoring and Maintenance
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• 제3권1호
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• pp.51-69
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• 2016

Monitoring the performance and estimating the remaining useful life of aging civil infrastructure in the United States has been identified as a major objective in the civil engineering community. Structural health monitoring has emerged as a central tool to fulfill this objective. This paper presents a review of the major structural monitoring programs that have been recently implemented in the United States, focusing on the integrity and performance assessment of large-scale structural systems. Applications where response data from a monitoring program have been used to detect and correct structural deficiencies are highlighted. These applications include (but are not limited to): i) Post-earthquake damage assessment of buildings and bridges; ii) Monitoring of cables vibration in cable-stayed bridges; iii) Evaluation of the effectiveness of technologies for retrofit and seismic protection, such as base isolation systems; and iv) Structural damage assessment of bridges after impact loads resulting from ship collisions. These and many other applications show that a structural health monitoring program is a powerful tool for structural damage and condition assessment, that can be used as part of a comprehensive decision-making process about possible actions that can be undertaken in a large-scale civil infrastructure system after potentially damaging events.

• 한국측량학회지
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• 제30권4호
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• pp.369-377
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• 2012

최근 IT기술의 발달과 공간정보기술의 고도화는 방재분야에 있어서 효과적인 피해조사 기술 개발의 필요성을 증대시키고 있다. 자연재해에 효과적으로 대응하고, 복구계획을 수립하기 위해서는 신속한 피해조사가 필요하며, 이러한 점에서 UAV는 신속한 피해조사를 위한 유용한 수단이 될 수 있다. 본 연구에서는 효율적인 자연재해 피해조사를 위한 UAV 기반 실시간 공중자료획득시스템의 활용성을 평가하고자 하였다. 시스템의 적용성 평가를 위해 정확도 분석을 수행하였으며, 국내 규정을 바탕으로 재난 유형을 구분하여 재해 유형별로 시스템을 적용한 피해조사의 활용성을 평가하였다. 연구 결과, 주택피해, 농경지 및 농림시설 피해, 공공시설 피해 등의 피해조사가 가능하였다. 향후 다양한 자연재해 현장을 대상으로 실시간공중자료획득시스템을 통해 취득된 영상자료를 활용함으로써 효율적인 자연재해 피해조사 및 복구계획 수립이 가능할 것이다.

• International Journal of Railway
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• 제7권4호
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• pp.94-99
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• 2014

Urban railway systems are located under populated areas and are mostly constructed for underground structures which demand high standards of structural safety. However, the damage progression of underground structures is hard to evaluate and damaged underground structures may not effectively stand against successive earthquakes. This study attempts to examine initial damage-stage and to access structural damage condition of the ground structures using Earthquake Damage Monitoring (EDM) system. For actual underground structure, vulnerable damaged member of Ulchiro-3ga station is chosen by finite element analysis using applied artificial earthquake load, and then damage pattern and history of damaged members is obtained from measured acceleration data introduced unsupervised learning recognition. The result showed damage index obtained by damage scenario establishment using acceleration response of selected vulnerable members is useful. Initial damage state is detected for selected vulnerable member according to established damage scenario. Stiffness degrading ratio is increasing whereas the value of reliability interval is decreasing.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2016년도 추계 학술논문 발표대회
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• pp.130-131
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• 2016

This study was performed for application of Structural Health Monitoring system of large structures. In order to evaluate damage of a structure, strain data of truss members that are changing with damage are gained by FEM analysis program. These data are used to train Artificial Neural Network(ANN), and this ANN algorithm can be used to analysis strain data for evaluating damage of the truss members.

• 비파괴검사학회지
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• 제32권6호
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• pp.678-685
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• 2012

The objective of this study is to monitor the health status of harbor caissons which have potential foundation damage. To obtain the objective, the following approaches are performed. Firstly, a structural damage monitoring(SDM) method is designed for interlocked multiple-caisson structures. The SDM method utilizes the change in modal strain energy to monitor the foundation damage in a target caisson unit. Secondly, a finite element model of a caisson system which consists of three caisson units is established to verify the feasibility of the proposed method. In the finite element simulation, the caisson units are constrained each other by shear-key connections. The health status of the caisson system against various levels of foundation damage is monitored by measuring relative modal displacements between the adjacent caissons.

• 풍력에너지저널
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• 제9권4호
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• pp.16-23
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• 2018

A damage detection method for the tripod support structure of offshore wind turbines is presented for structural health monitoring. A finite element model of a prototype tripod support structure is established and the modal properties are calculated. The degree and location of the damage are estimated based on the neural network technique using the changes of natural frequencies and mode shape due to the damage. The stress distribution occurring in the support structure is obtained by a dynamic analysis for the wind turbine system to select the output data of the neural network. The natural frequencies and mode shapes for 36 possible damage scenarios were used for the input data of the learned neural network for damage assessment. The estimated damages agreed reasonably well with the accurate ones. The presented method could be effectively applied for damage detection and structural health monitoring of various types of support structures of offshore wind turbines.

• 한국방재학회:학술대회논문집
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• 한국방재학회 2008년도 정기총회 및 학술발표대회
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• pp.481-484
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• 2008

The research was carried out to suggest the bridge health monitoring systems that have been composed damage detection algorithm and a system for evaluation load carrying capacity of bridge by traffic loads for the purpose of safety management of bridge structure in efficient and economic.

• 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.

• Structural Monitoring and Maintenance
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• 제1권2호
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• pp.159-171
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• 2014

Real-time near and far-field monitoring of concrete structural components gives enough information on the time and condition at which damage occurs, thereby facilitating damage detection while in the same time evaluate the cause of the damage. This paper experimentally investigates an integrated monitoring technique for near and far-field damage detection in concrete structures based on simultaneous use of electromechanical admittance technique in combination with guided wave propagation. The proposed sensing system does not measure the electromechanical admittance itself but detect time variations in output voltages of the response signal obtained across the electrodes of piezoelectric transducers bonded on surfaces of concrete structures. The damage identification is based on the spectral estimation MUSIC algorithm. Experimental results show the efficiency and performance of the proposed measuring technique.