• Structural Engineering and Mechanics
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• 제15권3호
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• pp.285-297
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• 2003

Complementing recent advances made in the field of structural health monitoring and damage detection, the concept of a wireless sensing network with distributed computational power is proposed. The fundamental building block of the proposed sensing network is a wireless sensing unit capable of acquiring measurement data, interrogating the data and transmitting the data in real time. The computational core of a prototype wireless sensing unit can potentially be utilized for execution of embedded engineering analyses such as damage detection and system identification. To illustrate the computational capabilities of the proposed wireless sensing unit, the fast Fourier transform and auto-regressive time-series modeling are locally executed by the unit. Fast Fourier transforms and auto-regressive models are two important techniques that have been previously used for the identification of damage in structural systems. Their embedment illustrates the computational capabilities of the prototype wireless sensing unit and suggests strong potential for unit installation in automated structural health monitoring systems.

• 전기학회논문지
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• 제67권7호
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• pp.884-891
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• 2018

In this paper, we propose an automatic earthquake P-wave detection algorithm based on the variations of the impact momentum derived from the seismic acceleration signals. The amount of change in the impact momentum induced by the acceleration refers to the influence of buildings or facilities on the earthquake, The proposed algorithm can effectively detect the seismic P-wave by simultaneously considering the amplitude and the frequency change of the seismic wave when the earthquake occurs. Computer simulations using the observed seismic signals were performed to evaluate the validity of the induced impact momentum variation and the superiority of the proposed algorithm.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1999년도 추계학술발표회요약집
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• pp.365.2-365
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• 1999

• Structural Engineering and Mechanics
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• 제91권5호
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• pp.443-457
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• 2024

At present, the traditional concrete surface inspection methods based on artificial vision have the problems of high cost and insecurity, while the computer vision methods rely on artificial selection features in the case of sensitive environmental changes and difficult promotion. In order to solve these problems, this paper introduces deep learning technology in the field of computer vision to achieve automatic feature extraction of structural damage, with excellent detection speed and strong generalization ability. The main contents of this study are as follows: (1) A method based on DeepLabV3+ convolutional neural network model is proposed for surface detection of post-earthquake structural damage, including surface damage such as concrete cracks, spaling and exposed steel bars. The key semantic information is extracted by different backbone networks, and the data sets containing various surface damage are trained, tested and evaluated. The intersection ratios of 54.4%, 44.2%, and 89.9% in the test set demonstrate the network's capability to accurately identify different types of structural surface damages in pixel-level segmentation, highlighting its effectiveness in varied testing scenarios. (2) A semantic segmentation model based on DeepLabV3+ convolutional neural network is proposed for the detection and evaluation of post-earthquake structural components. Using a dataset that includes building structural components and their damage degrees for training, testing, and evaluation, semantic segmentation detection accuracies were recorded at 98.5% and 56.9%. To provide a comprehensive assessment that considers both false positives and false negatives, the Mean Intersection over Union (Mean IoU) was employed as the primary evaluation metric. This choice ensures that the network's performance in detecting and evaluating pixel-level damage in post-earthquake structural components is evaluated uniformly across all experiments. By incorporating deep learning technology, this study not only offers an innovative solution for accurately identifying post-earthquake damage in civil engineering structures but also contributes significantly to empirical research in automated detection and evaluation within the field of structural health monitoring.

• Smart Structures and Systems
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• 제15권1호
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• pp.1-13
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• 2015

This paper proposes an analytical mode decomposition (AMD) and Hilbert transform method for structural nonlinearity quantification and damage detection under earthquake loads. The measured structural response is first decomposed into several intrinsic mode functions (IMF) using the proposed AMD method. Each IMF is an amplitude modulated-frequency modulated signal with narrow frequency bandwidth. Then, the instantaneous frequencies of the decomposed IMF can be defined with Hilbert transform. However, for a nonlinear structure, the defined instantaneous frequencies from the decomposed IMF are not equal to the instantaneous frequencies of the structure itself. The theoretical derivation in this paper indicates that the instantaneous frequency of the decomposed measured response includes a slowly-varying part which represents the instantaneous frequency of the structure and rapidly-varying part for a nonlinear structure subjected to earthquake excitations. To eliminate the rapidly-varying part effects, the instantaneous frequency is integrated over time duration. Then the degree of nonlinearity index, which represents the damage severity of structure, is defined based on the integrated instantaneous frequency in this paper. A one-story hysteretic nonlinear structure with various earthquake excitations are simulated as numerical examples and the degree of nonlinearity index is obtained. Finally, the degree of nonlinearity index is estimated from the experimental data of a seven-story building under four earthquake excitations. The index values for the building subjected to a low intensity earthquake excitation, two medium intensity earthquake excitations, and a large intensity earthquake excitation are calculated as 12.8%, 23.0%, 23.2%, and 39.5%, respectively.

• 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.

• 한국음향학회지
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• 제38권1호
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• pp.23-29
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• 2019

본 논문에서는 국내에서 발생한 지진 신호를 검출 및 식별하기 위한 방법을 다루었다. 국내에서 발생한 지진 신호들을 분석해 본 결과 서로 다른 주파수 대역 신호의 특징들이 각각 분류를 위한 특징으로 적절함을 확인할 수 있었다. 이러한 분석 결과를 바탕으로 지진 신호에서 추출한 다중 주파수 대역 특징을 기반으로 하는 CNN(Convolutional Neural Network) 기법에 대해서 제안하였다. 제안하는 다중 주파수 대역 CNN 기법은 지진 신호에서 추출한 멜 스펙트럼에 대해서 각각 필터를 적용하여 서로 다른 주파수 대역(저/중/고 주파수)의 신호를 추출하였다. 추출된 신호들을 바탕으로 각각 CNN 기반 분류를 수행하였고, 수행된 결과를 융합하여 최종적으로 지진 이벤트에 대해 식별하였다. 2018년 동안 대한민국에서 발생한 실제 지진데이터를 기반으로 하는 실험을 통해 제안하는 기법에 대한 효용성을 검증하였다.

• 전기학회논문지
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• 제61권8호
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• pp.1179-1185
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• 2012

This paper presents a new approach for the detection of seismic events using accumulated changes on time-frequency domain and variable threshold. To detect seismic P-wave arrivals with rapidness and accuracy, it is that the changes on the time and the frequency domains are simultaneously used. Their changes are parameters appropriated to reflect characteristics of earthquakes over moderate magnitude(${\geq}$ magnitude 4.0) and microearthquakes. In addition, adaptively controlled threshold values can prevent false P-wave detections due to low SNR. We tested our method on real earthquakes those have various magnitudes. The proposed algorithm gives a good detection performance and it is also comparable to STA/LTA algorithm in computational complexity. Computer simulation results shows that the proposed algorithm is superior to the conventional popular algorithm (STA/LTA) in the seismic P-wave detection.

• Journal of Astronomy and Space Sciences
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• 제32권2호
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• pp.137-140
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• 2015

The results of the study of ionospheric variations in the summer months of 1998-2002 at an ionospheric station of vertical sounding "Petropavlovsk-Kamchatsky" are presented. Anomalous variations of virtual sporadic-E height (h'Es), Es blanketing frequency (fbEs), and the critical frequency of the ionospheric F2 layer (foF2) (which can be attributed to the possible earthquake precursors) are selected. The high efficiency of the selection of ionospheric earthquake precursors based on the several parameters of Es and F2 layers is shown. The empirical dependence, which reflects the connection between the lead-time of the earthquake moment, the distance to the epicenter from the observation point, and the magnitude of the earthquake are obtained. This empirical dependence is consistent with the results of the detection of earthquake precursors by measuring the physical parameters of the Earth's crust in the same region.

• 한국지반공학회논문집
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• 제36권10호
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• pp.41-56
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• 2020

지진 경보 시스템이 빠르고 정확하게 가동하기 위해서는 충분한 수량의 계측 시스템 확보와 더불어서 적절한 계측 데이터 해석기술 개발이 요구된다. 신규 지진계를 설치시 많은 비용이 소모되기 때문에, 열차 내 가속도계 등을 대체재로 지진 경보 시스템에 활용하는 것이 효율적이다. 그러나 열차에 설치된 가속도계의 경우, 지진계와는 달리 열차 주행시 진동 데이터가 포함되어 있다. 또한, 지진 발생시 성토구간에 의해서 변화된 지진응답을 계측하게 된다. 본 연구에서는 위의 특성들이 포함된 열차 가속도계 데이터에 기반한 지진감지 기술을 제안하고자 한다. 우선, 성토구간의 지진응답 해석기법을 활용하여 열차가 성토구간을 지날 때 지진이 발생하는 것을 구현한 가상의 열차 가속도 데이터를 구축하였다. 구축한 가속도 데이터를 Short time Fourier Transform(STFT)와 Wavelet Transform(WT)을 활용하여 시간-주파수 분석을 수행하였다. 분석 결과, STFT가 장주기 지진 감지에 적합한 반면, WT의 경우 단주기 지진 감지에 유용함을 확인하였다.