• Structural Monitoring and Maintenance
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• v.7 no.4
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• pp.345-365
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• 2020

At present, many machine leaning and data mining methods are used for analyzing and predicting structural response characteristics. However, the platform that combines big data analysis methods with online and offline analysis modules has not been used in actual projects. This work is dedicated to developing a multifunctional Hadoop-Spark big data platform for bridges to monitor and evaluate the serviceability based on structural health monitoring system. It realizes rapid processing, analysis and storage of collected health monitoring data. The platform contains offline computing and online analysis modules, using Hadoop-Spark environment. Hadoop provides the overall framework and storage subsystem for big data platform, while Spark is used for online computing. Finally, the big data Hadoop-Spark platform computational performance is verified through several actual analysis tasks. Experiments show the Hadoop-Spark big data platform has good fault tolerance, scalability and online analysis performance. It can meet the daily analysis requirements of 5s/time for one bridge and 40s/time for 100 bridges.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.669-674
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• 2007

To develop a promising hybrid structural health monitoring (SHM) system, a combined use of structural vibration and electro-mechanical (EM) impedance is proposed. The hybrid SHM system is designed to use vibration characteristics as global index and EM impedance as local index. The proposed health monitoring scheme is implemented into prestressed concrete (PSC) girder bridges for which a series of damage scenarios are designed to simulate various prestress-loss situations at which the target bridges car experience during their service life. The measured experimental results, modal parameters and electro-magnetic impedance signatures, are carefully analyzed to recognize the occurrence of damage and furthermore to indicate its location.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.147-156
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• 2023

Because the behavior of cable bridges is dominated by dynamic response and is relatively complex, short- and long-term field monitoring are often required to evaluate the bridge condition. If a permanent SHMS (Structural Health Monitoring System) is not installed, a portable monitoring system is needed for the checking of bridge condition. In this case, it can be difficult to operate the portable monitoring system due to limited conditions such as power and communication according to the location and type of the bridge. In this study, the portable-based smart structural response monitoring system is developed that can be effectively used for short- and long-term monitoring of cable bridges in Korea and Southeast Asia. The developed system is a multi-channel portable data acquisition and analyzer that can be operated for a long time in the field using its own power supply system, and is included with the automated analysis algorithm for the dynamic characteristics of cable bridges using real-time data. In order to evaluate the field applicability of the developed system, field demonstration was conducted on cable bridges in Korea and Vietnam. Through the demonstration, the reliability and efficiency of field operation of the developed system were confirmed, and additionally, the possibility of application to overseas markets was confirmed in cable bridge monitoring field.

• Smart Structures and Systems
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• v.4 no.6
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• pp.711-726
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• 2008

A number of efforts had been sought to instrument bridges for the purpose of structural monitoring and assessment. The outcome of these efforts, as gauged by advances in the understanding of the definition of structural damage and their role in sensor selection as well as in the design of cost and data-effective monitoring systems, has itself been difficult to assess. The authors' experience with the design, calibration, and operation of a monitoring system for the Kishwaukee Bridge in Illinois has provided several lessons that bear upon these concerns. The systems have performed well in providing a continuous, low-cost monitoring platform for bridge engineers with immediate relevant information.

• Journal of the Korea institute for structural maintenance and inspection
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• v.11 no.2
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• pp.134-144
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• 2007

In this study, a smart sensor unit is developed by using the smart sensor technology that is being rapidly developed in recent years for structural health monitoring system, and its performance is evaluated through various experiments, and also, damage detection experiment is performed on a model structure. This paper as the first half of this study contains the development and performance evaluation of the smart sensor. In the latter half of this study, structure damage detection experiment is performed for the application of verified smart sensor unit into structural health monitoring, and it is compared with a wire measurement system. The smart sensor is developed by using high-power wireless modem, MEMS Sensor and AVR microcontroller, and an embedded program is also developed for the control and operation of the sensor unit. To verify the performance of the smart sensor, many experiments are performed for sensitivity and resolution analysis tests, data acquisition by using cantilever beam and shaker, and on-site application using actual bridge. As a result, the smart sensor proves to be satisfactory in its performance.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.1
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• pp.99-109
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• 2012

This study aimed to develop a Structural Health Monitoring System for steel beams in the manner of suggesting and verifying a theoretical formula for displacement estimation using strain gauges, and estimating the loading points and magnitude. According to the results of this study, it was found that when a load of 160kN (56% of the yield load) was applied, the error rate of the deflection obtained with a strain gauge at the point of maximum deflection compared to the deflection measured with a displacement meter was within 2%, and that the estimates of the magnitude and points of load application also showed the error rate of not more than 1%. This suggests that the displacement and load of steel beams can be measured with strain gauges and further, it will enable more cost-effective sensor designing without displacement meter or load cell. The Structural Health Monitoring System program implemented in Lab VIEW gave graded warnings whenever the measured data exceeds the specified range (strength limit state, serviceability limit state, yield strain), and both the serviceability limit state and strength limit state could be simultaneously monitored with strain gauge alone.

• Journal of Communications and Networks
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• v.11 no.4
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• pp.350-358
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• 2009

In a wireless sensor network (WSN) setting, this paper presents a distributed decision-making framework and illustrates its application in an online structural health monitoring (SHM) system. The objective is to recover a damage severity vector, which identifies, localizes, and quantifies damages in a structure, via distributive and collaborative decision-making among wireless sensors. Observing the fact that damages are generally scarce in a structure, this paper develops a nonlinear 0-norm minimization formulation to recover the sparse damage severity vector, then relaxes it to a linear and distributively tractable one. An optimal algorithm based on the alternating direction method of multipliers (ADMM) and a heuristic distributed linear programming (DLP) algorithm are proposed to estimate the damage severity vector distributively. By limiting sensors to exchange information among neighboring sensors, the distributed decision-making algorithms reduce communication costs, thus alleviate the channel interference and prolong the network lifetime. Simulation results in monitoring a steel frame structure prove the effectiveness of the proposed algorithms.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.06a
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• pp.1095-1096
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• 2009

• Magazine of the Korea Institute for Structural Maintenance and Inspection
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• v.20 no.2
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• pp.14-17
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• 2016

• Smart Structures and Systems
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• v.21 no.5
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• pp.695-703
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• 2018

Based on monitoring data collected from the Nanjing Dashengguan Bridge over the last five years, this paper systematically investigates the effects of temperature field and train loadings on the structural responses of this long-span high-speed railway bridge, and establishes the early warning thresholds for various structural responses. Then, some lessons drawn from the structural health monitoring system of this bridge are summarized. The main context includes: (1) Polynomial regression models are established for monitoring temperature effects on modal frequencies of the main girder and hangers, longitudinal displacements of the bearings, and static strains of the truss members; (2) The correlation between structural vibration accelerations and train speeds is investigated, focusing on the resonance characteristics of the bridge at the specific train speeds; (3) With regard to various static and dynamic responses of the bridge, early warning thresholds are established by using mean control chart analysis and probabilistic analysis; (4) Two lessons are drawn from the experiences in the bridge operation, which involves the lacks of the health monitoring for telescopic devices on the beam-end and bolt fractures in key members of the main truss.