• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 춘계학술대회 논문집
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• pp.1735-1741
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• 2011

The safety of station structures among railway infrastructures should be considered as a top priority because hundreds of thousands passengers a day take a subway. The station structures, which have been being operated since the 1970s, are especially vulnerable to the earthquake and long-term vibrations such as ambient train vibrations as well. This is why the structural-health monitoring system of station structures should be required. For these reason, Korean government has made an effort to develop the structural health-monitoring system of them, which can evaluate the health-state of station structures as well as can monitor the vulnerable structural members in real-time. Then, through the monitoring system, the vulnerable structural members could be retrofitted. For the development of health-state evaluation method for station structures with the real-time sensing data measured in the fields, authors carried out the numerical simulations to develop evaluation algorithms based on statistical pattern recognition techniques. In this study, the dynamic behavior of Chungmuro station in Seoul was numerically analyzed and then critical members were chosen. Damages were artificially simulated at the selected critical members of the numerical model. And, the supervised and unsupervised learning based pattern recognition algorithms were applied to quantify and localize the structural defects.

• Smart Structures and Systems
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• 제24권3호
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• pp.319-332
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• 2019

Structural health monitoring (SHM) is of great importance to super high-rise buildings. The Shanghai Tower is currently the tallest building in China, and a complete SHM system was simultaneously constructed at the beginning of the construction of the tower. Due to the variety of sensor types and the large number of measurement points in the SHM system, an online automatic structural health assessment method with few computations and no manual intervention is needed. This paper introduces a structural health assessment method for the Shanghai Tower that uses the coefficients of an autoregressive (AR) time series model as structural state indicators. An analysis of collected data indicates that the coefficients of the AR model are affected by environmental factors, and the principal component analysis method is used to remove the influence of environmental factors. Finally, the control chart method is used to track the changes in structural state indicators, and a plan for online automatic structure health state evaluation is proposed. This method is applied to long-term acceleration and inclination data from the Shanghai Tower and successfully identifies the changes in the structural state. Overall, the structural state indicators of the Shanghai Tower are stable, and the structure is in a healthy state.

• Smart Structures and Systems
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• 제29권4호
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• pp.561-576
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• 2022

Heritage timber structures represent the history and culture of a nation. These structures have been inherited from previous generations; however, they inevitably exhibit deterioration over time, potentially leading to structural deficiencies. Structural Health Monitoring (SHM) offers the potential to assess operational anomalies, deterioration, and damage through processing and analysis of data collected from transducers and sensors mounted on the structure. This paper reports on the design and implementation of a long-term SHM system on the Feiyun Wooden Pavilion in China, a three-story timber building built more than 500 years ago. The principles and features of the design and implementation of SHM systems for heritage timber buildings are systematically discussed. In total, 104 sensors of 6 different types are deployed on the structure to monitor the environmental effects and structural responses, including air temperature and humidity, wind speed and direction, structural temperatures, strain, inclination, and acceleration. In addition, integrated data acquisition and transmission subsystem using a newly developed software platform are implemented. Selected preliminary statistical and correlation analysis using one year of monitoring data are presented to demonstrate the condition assessment capability of the system based on the monitoring data.

• Wind and Structures
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• 제36권6호
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• pp.379-392
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• 2023

Structural health monitoring is used to ensure the well-being of civil structures by detecting damage and estimating deterioration. Wind flow applies external loads to high-rise buildings, with the horizontal force component of the wind causing structural displacements in high-rise buildings. This study proposes a deep learning-based predictive model for measuring lateral displacement response in high-rise buildings. The proposed long short-term memory model functions as a sequence generator to generate displacements on building floors depending on the displacement statistics collected on the top floor. The model was trained with wind-induced displacement data for the top floor of a high-rise building as input. The outcomes demonstrate that the model can forecast wind-induced displacement on the remaining floors of a building. Further, displacement was predicted for each floor of the high-rise buildings at wind flow angles of 0° and 45°. The proposed model accurately predicted a high-rise building model's story drift and lateral displacement. The outcomes of this proposed work are anticipated to serve as a guide for assessing the overall lateral displacement of high-rise buildings.

• 한국방재안전학회논문집
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• 제16권4호
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• pp.147-156
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• 2023

케이블 교량의 거동은 동적 응답에 의해 지배적이며 상대적으로 복잡하므로 교량의 상태를 평가하기 위한 장단기 현장 계측이 요구되는 경우가 빈번하다. 영구적인 SHMS(Structural Health Monitoring System)가 설치되지 않은 경우 성능평가를 위해 이동식 모니터링 시스템이 필요하다. 이 경우 교량의 위치와 형태에 따라 전력, 통신 등의 제한된 여건으로 인해 이동식 모니터링 시스템 운영에 어려움이 발생할 수 있다. 본 연구에서는 국내는 물론 동남아 지역 교량의 장 ‧ 단기 모니터링에 효과적으로 활용될 수 있는 포터블 기반의 스마트 구조응답 모니터링 시스템을 개발하였다. 개발된 시스템은 현장에서 자체 전원 공급 시스템을 이용하여 장시간 운용이 가능한 다채널 휴대용 데이터 수집 및 분석 장비이며, 실시간 데이터를 이용하여 케이블 교량의 동적 특성을 자동으로 분석할 수 있는 알고리즘을 탑재하고 있다. 개발된 시스템의 현장 적용성을 평가하기 위해 한국과 베트남의 케이블 교량에서 현장 실증을 수행하였으며, 이를 통해 개발된 시스템의 현장 운영의 신뢰성과 효율성을 확인하였고, 추가적으로 케이블 교량 모니터링 분야에서의 해외 시장 적용 가능성을 확인하였다.

• Smart Structures and Systems
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• 제24권5호
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Smart Structures and Systems
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• 제5권4호
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• pp.317-328
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• 2009

Current maintenance operations and integrity checks on a wide array of structures require personnel entry into normally-inaccessible or hazardous areas to perform necessary nondestructive inspections. To gain access for these inspections, structure must be disassembled and removed or personnel must be transported to remote locations. The use of in-situ sensors, coupled with remote interrogation, can be employed to overcome a myriad of inspection impediments stemming from accessibility limitations, complex geometries, the location and depth of hidden damage, and the isolated location of the structure. Furthermore, prevention of unexpected flaw growth and structural failure could be improved if on-board health monitoring systems were used to more regularly assess structural integrity. A research program has been completed to develop and validate Comparative Vacuum Monitoring (CVM) Sensors for surface crack detection. Statistical methods using one-sided tolerance intervals were employed to derive Probability of Detection (POD) levels for a wide array of application scenarios. Multi-year field tests were also conducted to study the deployment and long-term operation of CVM sensors on aircraft. This paper presents the quantitative crack detection capabilities of the CVM sensor, its performance in actual flight environments, and the prospects for structural health monitoring applications on aircraft and other civil structures.

• Structural Engineering and Mechanics
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• 제41권1호
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• pp.139-155
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• 2012

Structural health monitoring systems are often installed on bridges to provide assessments of the need for structural maintenance and repair. Damage or deterioration may be detected by observation of changes in bridge characteristics evaluated from measured structural responses. However, construction materials such as concrete and steel cables exhibit certain time-dependent behaviour, which also results in changes in structural characteristics. If these are not accounted for properly, false alarms may arise. This paper proposes a systematic and efficient method to study the time-dependent effects on the dynamic properties of cable-stayed bridges. After establishing the finite element model of a cable-stayed bridge taking into account geometric nonlinearities and time-dependent behaviour, long-term time-dependent analysis is carried out by time integration. Then the dynamic properties of the bridge after a certain period can be obtained. The effects of time-dependent behaviour of construction materials on the dynamic properties of typical cable-stayed bridges are investigated in detail.

• 한국구조물진단유지관리공학회 논문집
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• 제27권4호
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• pp.86-93
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• 2023

국내 교량들의 노후화 진행에 따라 구조물의 지속적인 안전관리를 위한 실시간 계측 기반의 교량 관리시스템이 필요하다. 현재 교량 계측시스템 기술은 대형 단일 교량의 계측을 중심으로 발전하여 유선을 기반으로 전원을 공급하고 계측 데이터를 수집한다. 하지만 산발적으로 분포하는 중소교량에는 위치적 문제로 인해 유선 기반 계측시스템을 적용하기 어렵다. 본 연구에서는 중소교량을 대상으로 무선 기반 계측시스템을 구축하는 방안을 제안하였다. 제안한 무선 기반 계측시스템은 기존 유선 기반의 계측시스템의 한계를 극복하기 위해 태양광 발전을 통해 무선 전원을 확보하였으며, LTE 통신을 활용하여 데이터를 송출하게 하였다. 또한, 교량 계측시스템의 관리를 위한 시스템 원격 제어 방안과 전원 관리 방안도 제안되었다. 제안한 계측시스템의 검증을 위해 실제 지방도상의 교량 32개소에 설치되었으며, 1년간의 장기 계측데이터를 수집하였다. 설치된 테스트 베드에서 80.6%의 계측데이터 취득이 가능함을 확인하여 제안한 계측시스템의 운용 가능성을 검증하였다. 제안된 시스템 구축방안은 지방도상 노후 교량들의 안전감시에 활용 가능할 것으로 기대된다.

• Smart Structures and Systems
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• 제1권4호
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• pp.355-368
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• 2005

Large and complex structures are being built now-a-days and, they are required to be functional even under extreme loading and environmental conditions. In order to meet the safety and maintenance demands, there is a need to build sensors integrated structural system, which can sense and provide necessary information about the structural response to complex loading and environment. Sophisticated tools have been developed for the design and construction of civil engineering structures. However, very little has been accomplished in the area of monitoring and rehabilitation. The employment of appropriate sensor is therefore crucial, and efforts must be directed towards non-destructive testing techniques that remain functional throughout the life of the structure. Fiber optic sensors are emerging as a superior non-destructive tool for evaluating the health of civil engineering structures. Flexibility, small in size and corrosion resistance of optical fibers allow them to be directly embedded in concrete structures. The inherent advantages of fiber optic sensors over conventional sensors include high resolution, ability to work in difficult environment, immunity from electromagnetic interference, large band width of signal, low noise and high sensitivity. This paper brings out the potential and current status of technology of fiber optic sensors for civil engineering applications. The importance of employing fiber optic sensors for health monitoring of civil engineering structures has been highlighted. Details of laboratory studies carried out on fiber optic strain sensors to assess their suitability for civil engineering applications are also covered.