• Smart Structures and Systems
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• 제29권2호
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• pp.361-374
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• 2022

As civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research.

• 국제초고층학회논문집
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• 제11권3호
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• pp.221-226
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• 2022

Installation of Structural Health Monitoring (SHM) system is a legal obligation for high-rise buildings over 200 m or 50-floor high in South Korea. CNP Dongyang has developed key technologies for SHM system design, installation, and data analyzing. Also, CNP Dongyang has applied SHM technology to a plenty of South Korea's representative high-rise buildings. The SHM technology, also, could be used in safety management of construction phase, evaluation of structural performance, etc. In this paper, state of the art SHM technologies and their application examples are introduced to give insight for future research and practical use of SHM.

• Smart Structures and Systems
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• 제15권6호
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• pp.1625-1642
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• 2015

We propose an electrically self-reliant structural health monitoring (SHM) system that is able to wirelessly transmit sensing data using electrical power generated by vibration without the need for additional external power sources. The provision of reliable electricity to wireless SHM systems is a highly important issue that has often been ignored, and to expand the applicability of various wireless SHM innovations, it will be necessary to develop comprehensive wireless SHM devices including stable electricity sources. In light of this need, we propose a new, highly efficient vibration-powered generator based on a tuned-mass-damper (TMD) mechanism that is quite suitable for vibration-based SHM. The charging time of the TMD generator is shorter than that of conventional generators based on the impedance matching method, and the proposed TMD generator can harvest 16 times the amount of energy that a conventional generator can. The charging time of an SHM wireless transmitter is quantitatively formulated. We conduct wireless monitoring experiments to validate a wireless SHM system composed of a self-reliant SHM and a vibration-powered TMD generator.

• Smart Structures and Systems
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• 제11권6호
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• pp.637-662
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• 2013

The Jiangyin Bridge is a suspension bridge with a main span of 1385 m over the Yangtze River in Jiangsu Province, China. Being the first bridge with a main span exceeding 1 km in Chinese mainland, it had been instrumented with a structural health monitoring (SHM) system when completed in 1999. After operation for several years, it was found with malfunction in sensors and data acquisition units, and insufficient sensors to provide necessary information for structural health evaluation. This study reports the SHM system upgrade project on the Jiangyin Bridge. Although implementations of SHM system have been reported worldwide, few studies are available on the upgrade of SHM system so far. Recognizing this, the upgrade of original SHM system for the bridge is first discussed in detail. Especially, lessons learned from the original SHM system are applied to the design of upgraded SHM system right away. Then, performance assessment of the bridge, including: (i) characterization of temperature profiles and effects; (ii) recognition of wind characteristics and effects; and (iii) identification of modal properties, is carried out by making use of the long-term monitoring data obtained from the upgraded SHM system. Emphasis is placed on the verification of design assumptions and prediction of bridge behavior or extreme responses. The results may provide the baseline for structural health evaluation.

• 한국철도학회논문집
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• 제12권3호
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• pp.412-419
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• 2009

구조물 건전성 모니터 링에서 구조물에 발생한 손상을 어떠한 방법으로 가장 효율적이고 정확하게 탐지하느냐는 매우 중요한 연구과제이다. 기존의 대부분 SHM기술에서는 구조물에 손상이 발생하기 이전에 측정해 놓은 탄성파신호를 손상 검출을 위한 기준 데이터로서 활용하고 있다. 본 연구에서는 Lamb파를 이용하는 pitch-catch (PC)-기법을 기반으로 기준 데이터를 필요로 하지 않는 새로운 SHM기술을 제안하였다. 또한 손상 신호에 이미지화 기법을 적용하여 손상의 위치를 이미지화 함으로써 손쉽게 파악할 수 있도록 하였다. 제안된 SHM기술은 알루미늄 평판 시편에 대한 실험을 통해 응용 가능성을 고찰하였다.

• Smart Structures and Systems
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• 제8권1호
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• pp.119-137
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• 2011

A bio-inspired two-mode structural health monitoring (SHM) system based on the Na$\ddot{i}$ve Bayes (NB) classification method is discussed in this paper. To implement the molecular biology based Deoxyribonucleic acid (DNA) array concept in structural health monitoring, which has been demonstrated to be superior in disease detection, two types of array expression data have been proposed for the development of the SHM algorithm. For the micro-vibration mode, a two-tier auto-regression with exogenous (AR-ARX) process is used to extract the expression array from the recorded structural time history while an ARX process is applied for the analysis of the earthquake mode. The health condition of the structure is then determined using the NB classification method. In addition, the union concept in probability is used to improve the accuracy of the system. To verify the performance and reliability of the SHM algorithm, a downscaled eight-storey steel building located at the shaking table of the National Center for Research on Earthquake Engineering (NCREE) was used as the benchmark structure. The structural response from different damage levels and locations was collected and incorporated in the database to aid the structural health monitoring process. Preliminary verification has demonstrated that the structure health condition can be precisely detected by the proposed algorithm. To implement the developed SHM system in a practical application, a SHM prototype consisting of the input sensing module, the transmission module, and the SHM platform was developed. The vibration data were first measured by the deployed sensor, and subsequently the SHM mode corresponding to the desired excitation is chosen automatically to quickly evaluate the health condition of the structure. Test results from the ambient vibration and shaking table test showed that the condition and location of the benchmark structure damage can be successfully detected by the proposed SHM prototype system, and the information is instantaneously transmitted to a remote server to facilitate real-time monitoring. Implementing the bio-inspired two-mode SHM practically has been successfully demonstrated.

• 동아시아식생활학회지
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• 제15권4호
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• pp.405-412
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• 2005

Quality characteristics of squid-sikhae prepared by four different methods(SHM: sikhae method, SHM-LA; sikhae method added with L plantarum, MM; mixed method of sikhae method and salting method, MM-LA; MM method added with L plantarum) were investigated during fermentation at $20^{\circ}C$. The pHs of all the 6-days fermented sikhae samples were in the range of 4.01-3.76, meaning that there were no significant difference in pH according to the preparation methods. Number of total microbes(TM) were decreased, while the ratio of lactic acid bacteria against TM in SHM-LA and MM-LA was higher than those of SHM and MM. There were no differences in acid protease activity, while $NH_2-N$ content of SHM and MM were higher than those of SHM-LA and MM-LA. Amylase activity was the lowest in MM-LA. Proteins separated by SDS-PAGE belonged to 7-200 kDa, the major proteins (153<94<41 kDa) of the sikhae in all plots were disappeared at 6 days fermentation. In sensory evaluation, sour taste of MM was the highest, while it was the lowest in SHM-LA. Sweet taste, bitter taste, salty taste and hot taste were not significantly different Off-flavor was decreased in lactic acid bacteria added products. Scores of the softness and overall acceptability were the highest in SHM-LA. These results indicated that SHM-LA was the best method for the preparation of squid sikhae because of the enhancement of lactic acid fermentation and overall acceptability.

• 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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• 제15권2호
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• pp.283-297
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• 2015

Jacket-type offshore structures are always exposed to severe environmental conditions such as salt, high speed of current, wave, and wind compared with other onshore structures. In spite of the importance of maintaining the structural integrity for an offshore structure, there are few cases to apply a structural health monitoring (SHM) system in practice. The impedance-based SHM is a kind of local SHM techniques and to date, numerous techniques and algorithms have been proposed for local SHM of real-scale structures. However, it still requires a significant challenge for practical applications to compensate unknown environmental effects and to extract only damage features from impedance signals. In this study, the impedance-based SHM was carried out on a 1/20-scaled model of an Uldolmok current power plant structure in Korea under changes in temperature and transverse loadings. Principal component analysis (PCA)-based approach was applied with a conventional damage index to eliminate environmental changes by removing principal components sensitive to them. Experimental results showed that the proposed approach is an effective tool for long-term SHM under significant environmental changes.

• Structural Monitoring and Maintenance
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• 제1권3호
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• pp.293-308
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• 2014

In the aeronautical environment, numerous regulatory and communication protocols exist that cover interconnection of on-board equipment inside the aircraft. Developed and implemented by the airlines since the 1960s, these communication systems are reliable, strong, certified and able to contact different sensors distributed throughout the aircraft. However, the scenario is slightly different in the structural health monitoring (SHM) field as the requirements and specifications that a global SHM communication system must fulfill are distinct. The number of SHM sensors installed in the aircraft rises into the thousands, and it is impossible to maintain all of the SHM sensors in operation simultaneously because the overall power consumption would be of thousands of Watts. This design of a new communication system must consider aspects as management of the electrical power supply, topology of the network for thousands of nodes, sampling frequency for SHM analysis, data rates, selected real-time considerations, and total cable weight. The goal of the research presented in this paper is to describe and present a possible integration scheme for the large number of SHM sensors installed on-board an aircraft with low power consumption. This paper presents a new communications system for SHM sensors known as the Bi-Instruction Link Bi-Operator (BILBO).