• Smart Structures and Systems
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• v.24 no.5
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).

• Journal of the Korean Society for Nondestructive Testing
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• v.21 no.4
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• pp.425-433
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• 2001

A correlation between fatigue damage and acousto-ultrasonic (AU) parameters has been obtained from signals acquired during fatigue loading of the single-lap joints of a carbon-fiber reinforced plastic (CFRP) laminates and A16061 plate. The correlation showed an analogy to those representing the stiffness reduction $(E/E_0)$ of polymer matrix composites by the accumulation of fatigue damage. This has been attributed to the transmission characteristics of acoustic wave energy through bonded joints with delamination-type defects and their influence on the change of spectral content of AU signals. Another correlation between fatigue cycles and the spectral magnitude of acoustic emission (AE) signals has also been found during the final stage of fatigue loading. Both AU and AE can be applied almost in real-time to monitor the evolution of damage during fatigue loading.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.121-128
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• 2019

Computing systems have various vulnerabilities to cyber attacks. In particular, various cyber attacks that are intelligent in the information society have caused serious social problems and economic losses. Traditional security systems are based on misuse-based technology, which requires the continuous updating of new attack patterns and the real-time analysis of vast amounts of data generated by numerous security devices in order to accurately detect. However, traditional security systems are unable to respond through detection and analysis in real time, which can delay the recognition of intrusions and cause a lot of damage. Therefore, there is a need for a new security system that can quickly detect, analyze, and predict the ever-increasing cyber security threats based on machine learning and big data analysis models. In this paper, we present a IDS model that combines machine learning and big data technology.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.146-146
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• 2022

A flash flood is one of the most hazardous natural events caused by heavy rainfall in a short period of time in mountainous areas with steep slopes. Early warning of flash flood is vital to minimize damage, but challenges remain in the enhancing accuracy and reliability of flash flood forecasts. The forecasters can easily determine whether flash flood is occurred using the flash flood guidance (FFG) comparing to rainfall volume of the same duration. In terms of this, the hydrological model that can consider the basin characteristics in real time can increase the accuracy of flash flood forecasting. Also, the predicted radar rainfall has a strength for short-lead time can be useful for flash flood forecasting. Therefore, using both hydrological models and radar rainfall forecasts can improve the accuracy of flash flood forecasts. In this study, FFG was applied to simulate some flash flood events in the Taehwa river basin by using of SURR model to consider soil moisture, and applied to the flash flood forecasting using predicted radar rainfall. The hydrometeorological data are gathered from 2011 to 2021. Furthermore, radar rainfall is forecasted up to 6-hours has been used to forecast flash flood during heavy rain in August 2021, Wulsan area. The accuracy of the predicted rainfall is evaluated and the correlation between observed and predicted rainfall is analyzed for quantitative evaluation. The results show that with a short lead time (1-3hr) the result of forecast flash flood events was very close to collected information, but with a larger lead time big difference was observed. The results obtained from this study are expected to use for set up the emergency planning to prevent the damage of flash flood.

• Journal of the Earthquake Engineering Society of Korea
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• v.18 no.6
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• pp.271-278
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• 2014

Seismic intensity deduced from instrumental data has been evaluated using the empirical relationship between intensity and peak ground acceleration (PGA) during an earthquake. The Japan Meteorological Agency (JMA) developed a seismic intensity meter, which can estimate the real-time seismic intensity from seismic motions observed at a local site to evaluate the damage during the earthquake more correctly. This paper proposes a practical application of the JMA intensity to dams during the 2013 earthquake in Yeongcheon, Korea. In the present paper, seismic intensity was estimated from the relationships between accelerations observed at Yeongcheon Dam. Estimated seismic intensities were in the range of 0 to 3, which was verified from the displacements of dams and the variation of the ground water level observed at Yeongcheon dam during the earthquake. The JMA intensity, which is determined by considering the frequency, duration of cyclic loading, etc., was 0 (zero) and there was no damage to Yeoncheon dam during the earthquake.

• Structural Engineering and Mechanics
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• v.47 no.6
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• pp.857-879
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• 2013

Numerical damage assessment of Van train station building consisting of three RC blocks due to 2011 Van Earthquakes by nonlinear dynamic analysis is presented. The structural model is created with rigid-end offsets and plastic hinges for nonlinear analysis. Rigid-end offsets are considered for connection areas and proposed for wall-supported elements. In wall-supported elements, walls take place in a limited part of the columns. Nonlinear dynamic analysis of the building with and without rigid-end offsets is performed by using real earthquake records and results are compared. The results show that rigid-end offsets have significant effects on the seismic behavior of the structures.

• Steel and Composite Structures
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• v.28 no.2
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• pp.123-138
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• 2018

Eccentrically braced frames (EBF) represent an optimal structural solution for seismic prone areas, being able to provide high dissipative capacity and good elastic stiffness, to withstand strong seismic events without significant loss of bearing capacity and to avoid damage to non-structural elements in case of low and moderate earthquakes. The accurate knowledge of the cyclic behaviour of the dissipative links, characterizing the whole performance of EBFs, is required to optimize the structural properties and to refine the design techniques adopted for multi-storey buildings' analysis. Reliable numerical models for the links, at the same time requiring a limited computational effort, are then needed. The present work shows the results of a wide experimental test campaign executed on real-scale one storey/one bay frames with horizontal and vertical links, together with the elaboration of a simple semi-analytical model for the quick representation of the cyclic behaviour of shear links.

• Smart Structures and Systems
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• v.25 no.3
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• pp.285-299
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• 2020

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.609-621
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• 2022

Purpose: Natural Disaster has well formed framework more than social disaster, because natural disaster is controlled by one department, such as MOIS, but social disaster is distributed. This study is on the design of the integrated service platform for the social diaster data. and then, apply to the local governments. Method: Firstly, we design DB templates for the incident cases considering the incident investigation reports. For the risk management, life-damage oriented social disaster risk assessment is defined. In case of the real-time incident data from NDMS, AI system provides the prediction information in the life damage and the cause of the incident. Result: We design the structured and unstructured incident data management system, and design the integrated social disaster and safety incident management system. Conclusion: The integrated social disaster and safety incident management system may be used in the local governments

• Structural Engineering and Mechanics
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• v.74 no.6
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• pp.789-807
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• 2020

Multiple earthquakes that occur during short seismic intervals affect the inelastic behavior of the structures. Sequential ground motions against the single earthquake event cause the building structure to face loss in stiffness and its strength. Although, numerous research studies had been conducted in this research area but still significant limitations exist such as: 1) use of traditional design procedure which usually considers single seismic excitation; 2) selecting a seismic excitation data based on earthquake events occurred at another place and time. Therefore, it is important to study the effects of successive ground motions on the framed structures. The objective of this study is to overcome the aforementioned limitations through testing a two storey RC building structural model scaled down to 1/10 ratio through a similitude relation. The scaled model is examined using a shaking table. Thereafter, the experimental model results are validated with simulated results using ETABS software. The test framed specimen is subjected to sequential five artificial and four real-time earthquake motions. Dynamic response history analysis has been conducted to investigate the i) observed response and crack pattern; ii) maximum displacement; iii) residual displacement; iv) Interstorey drift ratio and damage limitation. The results of the study conclude that the low-rise building model has ability to resist successive artificial ground motion from its strength. Sequential artificial ground motions cause the framed structure to displace each storey twice in correlation with vary first artificial seismic vibration. The displacement parameters showed that real-time successive ground motions have a limited impact on the low-rise reinforced concrete model. The finding shows that traditional seismic design EC8 requires to reconsider the traditional design procedure.