• Coupled systems mechanics
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• 제9권2호
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• pp.91-110
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• 2020

The reliability of structures is affected by various impacts that generally have a negative effect, from extreme weather conditions, due to climate change to natural or man-made hazards. In recent years, extreme loading has had an enormous impact on the resilience of structures as one of the most important characteristics of the sound design of structures, besides the structural integrity and robustness. Resilience can be defined as the ability of the structure to absorb or avoid damage without suffering complete failure, and it can be chosen as the main objective of design, maintenance and restoration for structures and infrastructure. The latter needs further clarification (which is done in this paper), to achieve the clarity of goals compared to robustness which is defined in Eurocode EN 1991-1-7 as: "the ability of a structure to withstand events like fire, explosions, impact or the consequences of human error, without being damaged to an extent disproportionate to the original cause". Many existing structures are more vulnerable to the natural or man-made hazards due to their material deterioration, and a further decrease of its loadbearing capacity, modifying the structural performance and functionality and, subsequently, the system resilience. Due to currently frequent extreme events, the design philosophy is shifting from Performance-Based Design to Resilience-Based Design and from unit to system (community) resilience. The paper provides an overview of such design evolution with indicative needs for Resilience-Based Design giving few conducted examples.

• Smart Structures and Systems
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• 제18권2호
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• pp.217-231
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• 2016

The present work aims to develop piezoresistive sensors of excellent piezoresistive response attributable to change in nanoscale structures of multi-wall carbon nanotube (MWNT) embedded in cement. MWNT was distributed in a cement matrix by means of polymer wrapping method in tandem with the ultrasonication process. DC conductivity of the prepared samples exhibited the electrical percolation behavior and therefore the dispersion method adopted in this study was deemed effective. The integrity of piezoresistive response of the sensors was assessed in terms of stability, the maximum electrical resistance change rate, and sensitivity. A composite sensor with MWNT 0.2 wt.% showed the lowest stability and sensitivity, while the maximum electrical resistance change rate exhibited by this sample was the highest (96 %) among others and even higher than those found in the literature. This observation was presumably attributed by the percolation threshold and the tunneling effect. As a result of the MWNT content (0.2 wt.%) of the sensor being near the percolation threshold (0.25 wt.%), MWNTs were close to each other to trigger tunneling in response of external loading. The sensor with MWNT 0.2 wt.% was able to maintain the repeatable sensing capability while sustaining a vehicular loading on road, demonstrating the feasibility in traffic flow sensing application.

• 한국측량학회지
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• 제39권5호
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• pp.329-341
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• 2021

This study derives the risk-Influence factors for subway structures, the basis for the transition from the current subway disaster recovery-oriented maintenance system to a preemptive disaster management system, to reduce risk factors for existing subway structures. To apply reasonable risk assessment techniques, risk influence factors for subway underground structures using statistical information(spatial information) and risk influence factors according to frequency of accidents were selected to derive the risk factors. The significant risk factors were verified through ground subsidence (SI: Subsidence Impact)-based correlation analysis. This process confirmed that the subsidence of the ground was a risk influence factor for the subway structure. The main result of this study is that derive the risk factors to improve the risk factors of subway structures due to the rapid increase in disaster risk factors. The derived risk factors that were expected to affect the depression around subway stations and track structures did not show a noticeable correlation, but the cause of this may be that there is no physical connection between them, but on the other hand, the accumulated data may not accurately record the surrounding depression. Accordingly, in order to evaluate the risk of depression around the station and track, more intensive observation and data accumulation around the structure are required.

• Smart Structures and Systems
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• 제30권1호
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• pp.17-34
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• 2022

For steel structures, fatigue cracks are critical damage induced by long-term cycle loading and distortion effects. Vision-based crack detection can be a solution to ensure structural integrity and performance by continuous monitoring and non-destructive assessment. A critical issue is to distinguish cracks from other features in captured images which possibly consist of complex backgrounds such as handwritings and marks, which were made to record crack patterns and lengths during periodic visual inspections. This study presents a parametric study on image-based crack identification for orthotropic steel bridge decks using captured images with complicated backgrounds. Firstly, a framework for vision-based crack segmentation using the atrous convolution-based Deeplapv3+ network (ACDN) is designed. Secondly, features on crack images are labeled to build three databanks by consideration of objects in the backgrounds. Thirdly, evaluation metrics computed from the trained ACDN models are utilized to evaluate the effects of obstacles on crack detection results. Finally, various training parameters, including image sizes, hyper-parameters, and the number of training images, are optimized for the ACDN model of crack detection. The result demonstrated that fatigue cracks could be identified by the trained ACDN models, and the accuracy of the crack-detection result was improved by optimizing the training parameters. It enables the applicability of the vision-based technique for early detecting tiny fatigue cracks in steel structures.

• 한국공간구조학회논문집
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• 제22권2호
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• pp.39-46
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• 2022

Recently, machine learning is widely used to solve optimization problems in various engineering fields. In this study, machine learning is applied to development of a control algorithm for a smart control device for reduction of seismic responses. For this purpose, Deep Q-network (DQN) out of reinforcement learning algorithms was employed to develop control algorithm. A single degree of freedom (SDOF) structure with a smart tuned mass damper (TMD) was used as an example structure. A smart TMD system was composed of MR (magnetorheological) damper instead of passive damper. Reward design of reinforcement learning mainly affects the control performance of the smart TMD. Various hyper-parameters were investigated to optimize the control performance of DQN-based control algorithm. Usually, decrease of the time step for numerical simulation is desirable to increase the accuracy of simulation results. However, the numerical simulation results presented that decrease of the time step for reward calculation might decrease the control performance of DQN-based control algorithm. Therefore, a proper time step for reward calculation should be selected in a DQN training process.

• 한국항행학회논문지
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• 제16권4호
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• pp.578-585
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• 2012

국제민간항공기구, ICAO(International Civil Aviation Organization)에서는 CNS/ATM(Communication Navigation Surveillance/Air Traffic Management)을 21세기 표준항행시스템으로 채택하기로 결의하였다. 이에 따라 ICAO 회원국은 관련 기술개발 및 인프라 구축에 박차를 가하고 있다. CNS/ATM의 항공 감시분야에서는 ADS-B(Automatic Dependent Surveillance-Broadcast) 시스템과 더불어 Multilateration이 구축되고 있다. Multilateration은 레이더 설치 및 운용이 곤란하거나 항공기 감시 사각지대를 보완하기 위해 설치하는 시스템으로 TDOA(Time Difference Of Arrival)를 이용하여 레이더에 비해 매우 정확하다. 본 논문에서는 레이더 시스템에 널리 사용되는 IMM(Interacting Multiple Model) 필터를 Multilateration에 적용하여 보다 정확한 항공기 위치 획득을 가능하게 하였다. 성능분석 결과, IMM 필터를 적용한 Multilateration이 기존의 Multilateration에 비해 공항 인근에서는 38.37%, 공항 10마일 부근에서는 20.86% 정확한 것으로 분석되었다.

• 한국구조물진단유지관리공학회 논문집
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• 제19권3호
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• pp.10-21
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• 2015

구조물 건전성 모니터링은 센서로부터 구조물의 응답을 수집하고 분석하여 구조물의 정확한 상태를 진단하는 기술이다. 최근 노후화된 구조물의 증가로 인하여, 지속가능한 사회 발전을 위해 더욱 발달된 구조물 건전성 모니터링 기술이 요구되고 있다. 최신 구조물 건전성 모니터링 기술 중 하나인 무선 스마트 센서와 센서 네트워크 기술은 기존의 유선 방식의 모니터링 시스템과 비교하여 더욱 효율적이며 경제적인 모니터링 시스템의 구축을 가능하게 하는 기술이다. 최근까지도 관련 연구자들은 스마트 센서의 성능 및 확장성 향상을 위하여 연구개발을 진행하고, 다양한 실내, 실외 실험을 통한 성능 테스트를 진행하였다. 본 논문에서는 최근 (2010년 이후를 중심으로)에 개발된 스마트 센서의 하드웨어, 소프트웨어, 그리고 응용 사례들을 정리함으로써, 구조물 건전성 모니터링을 위한 스마트 센서의 최신 연구동향에 대해 소개하고자 한다.

• Smart Structures and Systems
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• 제6권3호
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.

• Smart Structures and Systems
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• 제24권1호
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• pp.127-139
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• 2019

Residual drifts after an earthquake can incur huge repair costs and might need to replace the infrastructure because of its non-reparability. Proper functioning of bridges is also essential in the aftermath of an earthquake. In order to mitigate pounding and unseating damage of bridges subjected to earthquakes, a self-adaptive Ni-Ti shape memory alloy (SMA)-cable-based frictional sliding bearing (SMAFSB) is proposed considering self-adaptive centering, high energy dissipation, better fatigue, and corrosion resistance from SMA-cable component. The developed novel bearing is associated with the properties of modularity, replaceability, and earthquake isolation capacity, which could reduce the repair time and increase the resilience of highway bridges. To evaluate the super-elasticity of the SMA-cable, pseudo-static tests and numerical simulation on the SMA-cable specimens with a diameter of 7 mm are conducted and one dimensional (1D) constitutive hysteretic model of the SMAFSB is developed considering the effects of gap, self-centering, and high energy dissipation. Two types of the SMAFSB (i.e., movable and fixed SMAFSBs) are applied to a two-span continuous reinforced concrete (RC) bridge. The seismic vulnerabilities of the RC bridge, utilizing movable SMAFSB with the constant gap size of 60 mm and the fixed SMAFSBs with different gap sizes (e.g., 0, 30, and 60 mm), are assessed at component and system levels, respectively. It can be observed that the fixed SMAFSB with a gap of 30 mm gained the most retrofitting effect among the three cases.

• 대한토목학회논문집
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• 제39권3호
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• pp.391-398
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• 2019

일반적으로 화물차 사고는 일반 승용차 사고 대비 심각도가 높은 것으로 알려져 있으며, 최근 국내 화물차 사고 발생건수 및 사망률은 지속적으로 증가하고 있는 추세이다. 그러나 국내 화물차 사고 심각도 관련 연구는 매우 제한적으로 수행되었다. 이러한 배경 하에 본 연구는 국내 고속도로에서 과거 6년간 발생한 화물차 사고 심각도를 분석하여 화물차 사고 심각도에 영향을 미치는 인자를 도출하고자 한다. 분석을 위해 순서형 프로빗 모형이 적용되었으며 총 10개의 주요 인자가 도출되었다. 이중 8개 인자(나이가 많을수록, 졸음운전의 경우, 추돌 사고의 경우, 사고 후 전도나 전복이 된 경우, 사고 후 화재가 발생한 경우, 사고에 포함된 차량 수가 많을수록, 충돌 속도가 높을수록, 야간주행(0-6시)에 발생한 사고의 경우)는 사고 심각도가 높아지는 것으로, 2개 인자(눈이 오는 경우, 단독차량사고의 경우)는 감소시키는 것으로 나타났다. 본 연구 결과는 국내 고속도로 화물차 사고 심각도를 낮추기 위한 정책 수립 시 기반 자료로 활용할 수 있을 것으로 기대된다.