• Structural Monitoring and Maintenance
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• v.7 no.2
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• pp.109-124
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• 2020

Benefiting from the massive monitoring data collected by the Structural health monitoring (SHM) system, scholars can grasp the complex environmental effects and structural state during structure operation. However, the monitoring data is often missing due to sensor faults and other reasons. It is necessary to study the recovery method of missing monitoring data. Taking the structural temperature monitoring data of Nanjing Dashengguan Yangtze River Bridge as an example, the long short-term memory (LSTM) network-based recovery method for missing structural temperature data is proposed in this paper. Firstly, the prediction results of temperature data using LSTM network, support vector machine (SVM), and wavelet neural network (WNN) are compared to verify the accuracy advantage of LSTM network in predicting time series data (such as structural temperature). Secondly, the application of LSTM network in the recovery of missing structural temperature data is discussed in detail. The results show that: the LSTM network can effectively recover the missing structural temperature data; incorporating more intact sensor data as input will further improve the recovery effect of missing data; selecting the sensor data which has a higher correlation coefficient with the data we want to recover as the input can achieve higher accuracy.

• Structural Engineering and Mechanics
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• v.19 no.2
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• pp.123-139
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• 2005

An automatic modal identification program is developed for continuous extraction of modal parameters of three cable-supported bridges in Hong Kong which are instrumented with a long-term monitoring system. The program employs the Complex Modal Indication Function (CMIF) algorithm for identifying modal properties from continuous ambient vibration measurements in an on-line manner. By using the LabVIEW graphical programming language, the software realizes the algorithm in Virtual Instrument (VI) style. The applicability and implementation issues of the developed software are demonstrated by using one-year measurement data acquired from 67 channels of accelerometers permanently installed on the cable-stayed Ting Kau Bridge. With the continuously identified results, variability in modal vectors due to varying environmental conditions and measurement errors is observed. Such an observation is very helpful for selection of appropriate measured modal vectors for structural health monitoring use.

• Journal of Sensor Science and Technology
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• v.19 no.1
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• pp.8-16
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• 2010

This research proposes measuring of BCG(ballistocardiogram) to monitor heart activities in a non-constrained environment, at home or work. Unlike with ECG, measuring BCG does not require the attachment of leads on the subject's body and allows signal measuring in a non-constrained state. It enables effective long-term monitoring of cardiac conditions. In this study a chair type BCG measurement system to continuous monitor the activity of the heart is implemented. The instrument consists of upper petal and ready for press of chair load cell sensor is attached to measure the change of the object's weight. In order to extract the output ballistic signal from the weight and force sensor signals. Beside the signal processing circuit for the digital conversion, the ballistic signal is detected using DAQ equipment. Signal processing algorithm including wavelet transforms for noise cancellation, template matching for normalization and peak detection in BCG is developed. ECG and BCG were concurrently measured to evaluate the performance of the system, and comparing the characteristics of the two signals verified the possibility of the system in non-constrained and nonconscious health monitoring.

• Journal of Sensor Science and Technology
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• v.23 no.1
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• pp.7-14
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• 2014

Noncontact electrocardiogram (ECG) measurement using capacitive-coupled technique is a very reliable long-term noninvasive health-care remote monitoring system. It can be used continuously without interrupting the daily activities of the user and is one of the most promising developments in health-care technology. However, ECG signal is a very small electric signal. A robust system is needed to separate the clean ECG signal from noise in the measurement environment. Noise may come from many sources around the system, for example, bad contact between the sensor and body, common-mode electrical noise, movement artifacts, and triboelectric effect. Thus, in this paper, the extended Kalman filter (EKF) is applied to denoise a real-time ECG signal in capacitive-coupled sensors. The ECG signal becomes highly stable and noise-free by combining the common analog signal processing and the digital EKF in the processing step. Furthermore, to achieve ubiquitous monitoring, android-based application is developed to process the heart rate in a realtime ECG measurement.

• Smart Structures and Systems
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• v.25 no.1
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• pp.111-122
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• 2020

Researchers up to date have introduced several Structural Health Monitoring (SHM) techniques with varying advantages and drawbacks for each. Satellite positioning systems (GPS, GLONASS and GALILEO) based techniques proved to be promising, especially for high natural period structures. Particularly, the GPS has proved sufficient performance and reasonable accuracy in tracking real time dynamic displacements of flexible structures independent of atmospheric conditions, temperature variations and visibility of the monitored object. Tall structures are particularly sensitive to oscillations produced by different sources of dynamic actions; such as typhoons. Wind forces induce in the structure both longitudinal and perpendicular displacements with respect to the wind direction, resulting in torsional effects, which are usually more complex to be detected. To efficiently track the horizontal rotations of the in-plane sections of such flexible structures, two main issues have to be considered: a suitable sensor topology (i.e., location, installation, and combination of sensors), and the methodology used to process the data recorded by sensors. This paper reports the contributions of the measurements recorded from dual frequency GPS receivers and uni-axial accelerometers in a full-scale experimental campaign. The Canton tower in Guangzhou-China is the case study of this research, which is instrumented with a long-term structural health monitoring system deploying both accelerometers and GPS receivers. The elaboration of combining the obtained rather long records provided by these two types of sensors in detecting the torsional behavior of the tower under ambient vibration condition and during strong wind events is discussed in this paper. Results confirmed the reliability of GPS receivers in obtaining the dynamic characteristics of the system, and its ability to capture the torsional response of the tower when used alone or when they are combined with accelerometers integrated data.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.32 no.1
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• pp.78-88
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• 2022

Objectives: To develop the smart sensor to protect worker's health from chemical exposure by adopting ICT (Information and Communications Technology) technologies. Methods: To develope real-time chemical exposure monitoring system, IoT (Internet of Things) sensor technology and regulations were reviewed. We developed and produced smart sensor. A smart sensor is a system consisting of a sensor unit, a communication unit, and a platform. To verify the performance of smart sensors, each sensor has been certified by the Korea Laboratory Accreditation Scheme (KOLAS). Results: Chemicals (TVOC; Total Volatile Organic Compounds, Cl₂: Chlorine, HF: Hydrogen fluoride and HCN: Hydrogen cyanide) were selected according to a priority logic (KOSHA Alert, acute poisoning statistics, literature review). Notifications were set according to OEL (occupational exposure limit). Sensors were selected based on OEL and the capabilities of the sensors. Communication is designed to use LTE (Long Term Evolution) and Wi-Fi at the same time for convenience. Electronic platform were applied to build this monitoring system. Conclusions: Real-time monitoring system for OEL of hazardous chemicals in workplace was developed. Smart sensor can detect chemicals to complement monitoring of traditional workplace environmental monitoring such as short term and peak exposure. Further research is needed to expand the scope of application, improve reliability, and systematically application.

• Smart Structures and Systems
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• v.24 no.5
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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.

• Computers and Concrete
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• v.8 no.3
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• pp.293-309
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• 2011

A long-term structural health monitoring (SHM) system comprising over 700 sensors of sixteen types has been implemented on the Guangzhou Television and Sightseeing Tower (GTST) of 610 m high for real-time monitoring of the structure at both construction and service stages. As part of this sophisticated SHM system, 48 temperature sensors have been deployed at 12 cross-sections of the reinforced concrete inner structure of the GTST to provide on-line monitoring via a wireless data transmission system. In this paper, the differential temperature profiles in the reinforced concrete inner structure of the GTST, which are mainly caused by solar radiation, are recognized from the monitoring data with the purpose of understanding the temperature-induced structural internal forces and deformations. After a careful examination of the pre-classified temperature measurement data obtained under sunny days and non-sunny days, common characteristic of the daily temperature variation is observed from the data acquired in sunny days. Making use of 60-day temperature measurement data obtained in sunny days, statistical patterns of the daily rising temperature and daily descending temperature are synthesized, and temperature distribution models of the reinforced concrete inner structure of the GTST are formulated using linear regression analysis. The developed monitoring-based temperature distribution models will serve as a reliable input for numerical prediction of the temperature-induced deformations and provide a robust basis to facilitate the design and construction of similar structures in consideration of thermal effects.

• Proceedings of the KSR Conference
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• 2002.10b
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• pp.1074-1079
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• 2002

Long term measurement data on the bridge response caused by moving loads are fundamental ingredient to the development or improvement of the new bridge design. In addition, proper establishment of the systematic analysis and diagnosis together with the maintenance system become the essential procedure to the effective repair/reinforcement/retrofit of not only the high speed but also the conventional railway bridges. Therefore, the real time health monitoring system on the important railway bridges should be enhancing the proper maintenance of the structures. The main objective of this study is, therefore, to develop a monitoring device including Weigh-In-Motion (WIM) function and the emphasis is place on the easy and economic installation of the developed system in the field condition.

• Smart Structures and Systems
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• v.1 no.4
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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.