• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.

• Smart Structures and Systems
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• v.2 no.3
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• pp.253-274
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• 2006

The structural monitoring of multi-wire strands is of importance to prestressed concrete structures and cable-stayed or suspension bridges. This paper addresses the monitoring of strands by ultrasonic guided waves with emphasis on the signal processing and automatic defect classification. The detection of notch-like defects in the strands is based on the reflections of guided waves that are excited and detected by magnetostrictive ultrasonic transducers. The Discrete Wavelet Transform was used to extract damage-sensitive features from the detected signals and to construct a multi-dimensional Damage Index vector. The Damage Index vector was then fed to an Artificial Neural Network to provide the automatic classification of (a) the size of the notch and (b) the location of the notch from the receiving sensor. Following an optimization study of the network, it was determined that five damage-sensitive features provided the best defect classification performance with an overall success rate of 90.8%. It was thus demonstrated that the wavelet-based multidimensional analysis can provide excellent classification performance for notch-type defects in strands.

• Computers and Concrete
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• v.4 no.3
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• pp.243-257
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• 2007

A novel method for recognition, characterization, and quantification of deterioration in bridge components and laboratory concrete samples is presented in this paper. The proposed scheme is based on grey level co-occurrence matrix texture analysis using Haar's discrete wavelet transform on concrete imagery. Each image is described by a subset of band-filtered images containing wavelet coefficients, and then reconstructed images are employed in characterizing the texture, using grey level co-occurrence matrices, of the different types and degrees of damage: map-cracking, spalling and steel corrosion. A comparative study was conducted to evaluate the efficiency of the supervised maximum likelihood and unsupervised K-means classification techniques, in order to classify and quantify the deterioration and its extent. Experimental results show both methods are relatively effective in characterizing and quantifying damage; however, the supervised technique produced more accurate results, with overall classification accuracies ranging from 76.8% to 79.1%.

• 한국방재학회:학술대회논문집
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• 2007.02a
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• pp.526-529
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• 2007

Storm and flood damage management systems in national disaster management system(NDMS) were organized into three operation systems. They are prevention, preparation, response, and recovery systems. Disaster resources in each system must be promptly and exactly applied to minimize casualties and loss of properties. However, the disaster resources in current management system can not be immediately used in calamity situation due to the lack of efficiency in statistical data. In this study, the classification system of the disaster resources in storm and flood damage systems was examined to develop the a standard technology in disaster resources management. Problems and reformation points of the classification system were also presented to improve the classification technique and to construct the data base.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.763-765
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• 2003

We made a hazard map using GIS and remote sensing for he greatest inundation damage that happened for the 20th century. We calculated the land cover classification using Landsat from 1983 to 2000. We calculated it from a damage report and an aerial photo for a flood. We considered relation of both land cover classification and the damage. We expected the inundation damage in the future and made a hazard map.

• Journal of Korean Association for Spatial Structures
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• v.18 no.2
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• pp.87-97
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• 2018

Stone pagoda structures have continued to be aged due to the combination of various damage factors. However, some studies on nonstructural damage have been carried out, but assessment studies on structural damage have not been done in various ways. Therefore, in this study, structural and nonstructural influencing factors according to the damage types are classified and the damage assessment according to the structural influencing factors affecting the behavior of the stone pagoda structure is performed. In addition, the damage rating classification criteria for each type of structural damages or damage locations are presented, and the damage index is calculated by providing the criteria for the classification of damage according to the degree of damage to which the damage is caused. Therefore, this study can evaluate quantitatively the damage status of stone pagoda structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.10 no.6
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• pp.107-111
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• 2010

The damage of tire by damage material classification method is researched as used ultrasonic wave signal to occur at a tire during vehicle driving. Auto-correlation function after having passed through an envelope detecting preprocess is used for detecting periodicity because of occurring periodic ultrasonic waves signal with tire revolution. One revolution cycle time of a damaged tire and period that calculated auto-correlation function appeared equally in experiment. The result that can classification whether or not there was a tire damage is established.

• Smart Structures and Systems
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• v.13 no.5
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• pp.849-868
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• 2014

An indirect approach is explored for structural health bridge monitoring allowing for wide, yet cost-effective, bridge stock coverage. The detection capability of the approach is tested in a laboratory setting for three different reversible proxy types of damage scenarios: changes in the support conditions (rotational restraint), additional damping, and an added mass at the midspan. A set of frequency features is used in conjunction with a support vector machine classifier on data measured from a passing vehicle at the wheel and suspension levels, and directly from the bridge structure for comparison. For each type of damage, four levels of severity were explored. The results show that for each damage type, the classification accuracy based on data measured from the passing vehicle is, on average, as good as or better than the classification accuracy based on data measured from the bridge. Classification accuracy showed a steady trend for low (1-1.75 m/s) and high vehicle speeds (2-2.75 m/s), with a decrease of about 7% for the latter. These results show promise towards a highly mobile structural health bridge monitoring system for wide and cost-effective bridge stock coverage.

• Earthquakes and Structures
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• v.4 no.3
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• pp.299-324
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• 2013

The current research focuses on the seismic vulnerability assessment of typical Southern Europe buildings, based on processing of a large set of observational damage data. The presented study constitutes a sequel of a previous research. The damage statistics have been enriched and a wider damage database (178578 buildings) is created compared to the one of the first presented paper (73468 buildings) with Damage Probability Matrices (DPMs) after the elaboration of the results from post-earthquake surveys carried out in the area struck by the 7-9-1999 near field Athens earthquake. The dataset comprises buildings which developed damage in several degree, type and extent. Two different parameters are estimated for the description of the seismic demand. After the classification of damaged buildings into structural types they are further categorized according to the level of damage and macroseismic intensity. The relative and the cumulative frequencies of the different damage states, for each structural type and each intensity level, are computed and presented, in terms of damage ratio. Damage Probability Matrices (DPMs) are obtained for typical structural types and they are compared to existing matrices derived from regions with similar building stock and soil conditions. A procedure is presented for the classification of those buildings which initially could not be discriminated into structural types due to restricted information and hence they had been disregarded. New proportional DPMs are developed and a correlation analysis is fulfilled with the existing vulnerability relations.

• Journal of the Korean Society for Railway
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• v.9 no.4 s.35
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• pp.335-342
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• 2006

Disaster management system should be built for minimizing damage factor that affects to construction facility from natural disaster. It could be classified by three categories such as disaster prevention, damage survey and recovery phases. For an integrated disaster management system, a disaster information classification system(DICS) is necessary for the reasonable disaster information management. This study suggests an integrated DICS that includes disaster type classification, facility type classification and information type classification for disaster management service. The applicability of suggested DICS is verified by railway facility and the research result could be used as a basic information system for national disaster management system.