• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.3
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• pp.244-256
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• 2010

Radio frequency identification(RFID) combined with wireless technology has good potential for structural health monitoring(SHM). We describe several advantages of RFID and wireless technologies for SHM, and review SHM examples with working principles, design and technical details for damage detection, heat exposure monitoring, force/strain sensing, and corrosion detection in concrete, steel, carbon fiber reinforced polymer(CFRP), and other materials. Various sensors combined with wireless communication are also discussed. These methodologies can be readily developed, implemented, and customized. There are some technical difficulties, but solutions are being addressed. Lastly, a surface acoustic wave-based RFID system is presented, and possible future trends of SHM based on RFID and wireless technology are presented.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.1545-1561
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• 2008

Among all transportations, railway transports have been promisingly offering excellent energy conservation and travelling time. Inevitably, they become a main role in not only transport goods but also passengers. With leap in development of technology, trains have tremendously enhanced their services in terms of speed, accessibility and comfort. However, the safety and ride quality have become a main issue as the train speed increased. The higher speeds have led the structural dynamics and health must be monitored from time to time to ensure that they are in good condition to provide reliable ride. Among all monitoring systems, the structural health monitoring (SHM) systems are imperative important due to its capability of in-situ monitoring and inherently reduce the maintenance frequencies and the huge associated cost. In this paper, SHM systems and the related non-destructive test and evaluation methods were discussed. The types of damages related to train vehicles as well as the damage hot spots are also included in this paper.

• Structural Monitoring and Maintenance
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• v.10 no.2
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• pp.175-190
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• 2023

This article aims to quantitatively predict the snowmelt in extreme cold regions, considering a combination of grayscale and neural models. The traditional non-equidistant GM(1,1) prediction model is optimized by adjusting the time-distance weight matrix, optimizing the background value of the differential equation and optimizing the initial value of the model, and using the BP neural network for the first. The adjusted ice forecast model has an accuracy of 0.984 and posterior variance and the average forecast error value is 1.46%. Compared with the GM(1,1) and BP network models, the accuracy of the prediction results has been significantly improved, and the quantitative prediction of the ice sheet is more accurate. The monitoring and maintenance of the structure by quantitative prediction model by gray models was clearly demonstrated in the model.

• Smart Structures and Systems
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• v.9 no.5
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• pp.393-410
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• 2012

Conventional geotechnical instrumentation techniques available for monitoring of slopes, especially soil-nailed slopes have limitations such as electromagnetic interference, low accuracy, poor longterm reliability and difficulty in mounting a series of strain sensors on a soil nail bar with a small-diameter. This paper presents a slope monitoring system based on fibre Bragg grating (FBG) sensing technology. This monitoring system is designed to perform long-term monitoring of slope movements, strains along soil nails, and other slope reinforcement elements. All these FBG sensors are fabricated and calibrated in laboratory and a trial of this monitoring system has been successfully conducted on a roadside slope in Hong Kong. As part of the slope stability improvement works, soil nails and a toe support soldier-pile wall were constructed. During the slope works, more than 100 FBG sensors were installed on a soil nail, a soldier pile, and an in- place inclinometer. The paper presents the layout and arrangement of the instruments as well as the installation procedures adopted. Monitoring data have been collected since March 2008. This trial has demonstrated the great potential of the optical fibre monitoring system for long-term monitoring of slope performance. The advantages of the slope monitoring system and experience gained in the field implementation are also discussed in the paper.

• Structural Engineering and Mechanics
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• v.17 no.3_4
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• pp.393-408
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• 2004

There exists a clear need to monitor the performance of civil structures over their operational lives. Current commercial monitoring systems suffer from various technological and economic limitations that prevent their widespread adoption. The wires used to route measurements from system sensors to the centralized data server represent one of the greatest limitations since they are physically vulnerable and expensive from an installation and maintenance standpoint. In lieu of cables, the introduction of low-cost wireless communications is proposed. The result is the design of a prototype wireless sensing unit that can serve as the fundamental building block of wireless modular monitoring systems (WiMMS). An additional feature of the wireless sensing unit is the incorporation of computational power in the form of state-of-art microcontrollers. The prototype unit is validated with a series of laboratory and field tests. The Alamosa Canyon Bridge is employed to serve as a full-scale benchmark structure to validate the performance of the wireless sensing unit in the field. A traditional cable-based monitoring system is installed in parallel with the wireless sensing units for performance comparison.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.1-8
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• 2009

The proposed real-time structural health monitoring(SHM) system in past transferred and received data, central server gathered data from sensors, through coaxial cable. an immense sum of money is required to structure sensor network using coaxial cable. This paper proposes USN-based structural health monitoring(SHM). AIso, this paper designs and realizes prototypes according to proposed SHM. The value of sensing data obtained through HSDPA transfer to the BMS(Bridge Monitoring Server) passing through the TCP / IP socket by building two-way communication system, We have implemented a complete graph converting full system.

• Computational Structural Engineering
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• v.18 no.4 s.70
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• pp.38-46
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• 2005

• Smart Structures and Systems
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• v.13 no.6
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• pp.927-942
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• 2014

Structural displacement is an important indicator for assessing structural safety. For structural displacement monitoring, vision-based displacement measurement systems have been widely developed; however, most systems estimate only 1 or 2-DOF translational displacement. To monitor the 6-DOF structural displacement with high accuracy, a vision-based displacement measurement system with a uniquely designed marker is proposed in this paper. The system is composed of a uniquely designed marker and a camera with a zooming capability, and relative translational and rotational displacement between the marker and the camera is estimated by finding a homography transformation. The novel marker is designed to make the system robust to measurement noise based on a sensitivity analysis of the conventional marker and it has been verified through Monte Carlo simulation results. The performance of the displacement estimation has been verified through two kinds of experimental tests; using a shaking table and a motorized stage. The results show that the system estimates the structural 6-DOF displacement, especially the translational displacement in Z-axis, with high accuracy in real time and is robust to measurement noise.

• Smart Structures and Systems
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• v.19 no.5
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• pp.499-512
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• 2017

Traditional structural dynamic analysis and Structural Health Monitoring (SHM) of large scale concrete civil structures rely on manufactured embedding transducers to obtain structural dynamic properties. However, the embedding of manufactured transducers is very expensive and low efficiency for signal acquisition. In dynamic structural analysis and SHM areas, piezoelectric transducers are more and more popular due to the advantages like quick response, low cost and adaptability to different sizes. In this paper, the applicable feasibility assessment of the designed "artificial" piezoelectric transducers called Concrete Piezoelectric Smart Module (CPSM) in dynamic structural analysis is performed via three major experiments. Experimental Modal Analysis (EMA) based on Ibrahim Time Domain (ITD) Method is applied to experimentally extract modal parameters. Numerical modal analysis by finite element method (FEM) modeling is also performed for comparison. First ten order modal parameters are identified by EMA using CPSMs, PCBs and FEM modeling. Comparisons are made between CPSMs and PCBs, between FEM and CPSMs extracted modal parameters. Results show that Power Spectral Density by CPSMs and PCBs are similar, CPSMs acquired signal amplitudes can be used to predict concrete compressive strength. Modal parameter (natural frequencies) identified from CPSMs acquired signal and PCBs acquired signal are different in a very small range (~3%), and extracted natural frequencies from CPSMs acquired signal and FEM results are in an allowable small range (~5%) as well. Therefore, CPSMs are applicable for signal acquisition of dynamic responses and can be used in dynamic modal analysis, structural health monitoring and related areas.