• Proceedings of the KSR Conference
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• 2011.10a
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• pp.281-286
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• 2011

Station structures, one of important infrastructures, which have been being operated since the 1970s, are especially vulnerable to even the medium-level earthquake and they could be damaged by long-term internal or external vibrations such as ambient vibrations. Recently, much attention has been paid to real-time monitoring of the fatal defect or long-term deterioration of civil infrastructures to ensure their safety and adequate performance throughout their life span. In this study, a structural health monitoring methodology using acceleration responses is proposed to evaluate the health-state of the station structures and to detect initial damage-stage. A damage index is developed using the acceleration data and it is applied to outlier analysis, one of unsupervised learning based pattern recognition methods. A threshold value for the outlier analysis is determined based on confidence level of the probabilistic distribution of the acceleration data. The probabilistic distribution is selected according to the feature of the collected data.

• Computers and Concrete
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• v.30 no.5
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• pp.301-310
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• 2022

Cement-based sensors have been widely used as structural health monitoring systems, however, their long-term sensing performance have not actively investigated. In this study, a deep learning-based methodology is adopted to predict the long-term piezoresistive properties of cement-based sensors. Samples with different multi-walled carbon nanotube contents (0.1, 0.3, and 0.5 wt.%) are fabricated, and piezoresistive tests are conducted over 10,000 loading cycles to obtain the training data. Time-dependent degradation is predicted using a modified long short-term memory (LSTM) model. The effects of different model variables including the amount of training data, number of epochs, and dropout ratio on the accuracy of predictions are analyzed. Finally, the effectiveness of the proposed approach is evaluated by comparing the predictions for long-term piezoresistive sensing performance with untrained experimental data. A sensitivity of 6% is experimentally examined in the sample containing 0.1 wt.% of MWCNTs, and predictions with accuracy up to 98% are found using the proposed LSTM model. Based on the experimental results, the proposed model is expected to be applied in the structural health monitoring systems to predict their long-term piezoresistice sensing performances during their service life.

• Journal of Home Health Care Nursing
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• v.23 no.2
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• pp.155-165
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• 2016

Purpose: The purpose of this study was to investigate factors influencing health workers' infection management behavior in long-term care facilities. Methods: A descriptive cross-sectional survey was conducted with 180 health workers who are employed in long-term care facilities. The data were collected from April, 25 until July, in 2016. Results: Infection management behavior positively correlated with the perceived importance of infection management (r=.77, p<.001), but role conflict negatively correlated with infection management behavior (r=.28, p<.001). The hierarchical regression model with general characteristics (first step) and perceived importance of infection management, work environment, and role conflict (second step) against infection management behavior was statistically significant (F=31.93, p<.001). This model could explain 62.8% of infection management behavior ($R^2=.62$, ${\Delta}R^2=.39$). Particularly, perceived importance of infection management was identified as factors influencing infection management behavior(${\beta}=.70$, p<.001). Conclusion: The findings of this study imply that systemic education about infection control and monitoring should be considered, so as to encourage proper infection management behaviors among health workers in long-term care facilities.

• Smart Structures and Systems
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• v.17 no.6
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• pp.995-1015
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• 2016

In the design and condition assessment of bridges, it is usually necessary to take into consideration the extreme conditions which are not expected to occur within a short time period and thus require an extrapolation from observations of limited duration. Long-term structural health monitoring (SHM) provides a rich database to evaluate the extreme conditions. This paper focuses on the extrapolation of extreme traffic load effects on bridges using long-term monitoring data of structural strain. The suspension Tsing Ma Bridge (TMB), which carries both highway and railway traffic and is instrumented with a long-term SHM system, is taken as a testbed for the present study. Two popular extreme value extrapolation methods: the block maxima approach and the peaks-over-threshold approach, are employed to extrapolate the extreme stresses induced by highway traffic and railway traffic, respectively. Characteristic values of the extreme stresses with a return period of 120 years (the design life of the bridge) obtained by the two methods are compared. It is found that the extrapolated extreme stresses are robust to the extrapolation technique. It may owe to the richness and good quality of the long-term strain data acquired. These characteristic extremes are also compared with the design values and found to be much smaller than the design values, indicating conservative design values of traffic loading and a safe traffic-loading condition of the bridge. The results of this study can be used as a reference for the design and condition assessment of similar bridges carrying heavy traffic, analogous to the TMB.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.51-65
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• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Journal of KIBIM
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• v.11 no.3
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• pp.75-90
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• 2021

A smart home system improves the elderly's quality of life by monitoring and analyzing their movements and health conditions with better health-care and social support services. Therefore, there has been an effort to adopt a smart home system for the independently living elderly. However, to the best of our knowledge, no study has investigated the usability of a smart home system on actual independently living elderly housing in long-term settings. Thus, this study aims to demonstrate the usability of a smart home system on independently living elders in living lab conditions. The BLE smart band and the BLE receiver were chosen for the smart home system to monitor the movement of the participants in their homes as well as to monitor the heart rates, step counts, sleep index. Nine independent living elderly from the senior welfare center in Kimjae participated in this living lab demonstration experiment for ten months. This demonstration experiment confirmed the effectiveness of low-cost and easily adoptable IoT-based BLE sensor sets on independent living elders and discussed the troubles and limitations of the experiment. By grasping the pros and cons of IoT-based BLE sensor sets, this study seeks to improve the accessibility and usability of smart home systems for the elderly population in independent living arrangements.

• Health Policy and Management
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• v.21 no.4
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• pp.617-642
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• 2011

The purpose of our study is to analyze the determinants of the benefits of the long-term care insurance in Korea using 2008 and 2009 cross-sectional data. Per capita long-term care insurance benefits can be divided into home care services utilization rate, institutional care services utilization rate, per capita home care services benefits, and per capita institutional care services benefits, which are used as the dependent variables in our regression analysis. Admission rate and the ratio of the admitted to the applicant also used as the dependent variables. The results of our analysis show that the explanatory variables such as income level, needs for care, family type, access to the services, and regional characteristics are statistically significant to explain the dependent variables, the long-term care insurance benefits. The higher is the regional income and the more of the female residents, the more are the long-term care insurance benefits. The easier is the access to the services, the more are the insurance benefits. In the rural area, the level of the insurance benefits is relatively high. We propose that copayment rates of the long-term care insurance should be examined and monitoring on the over-use of the services should be done. Also preventive services and care by the family member should be expanded.

• Proceedings of the KSR Conference
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• 2008.06a
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• pp.224-231
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• 2008

A subway or an underground railway is one of the representative public transportations which lots of people take everyday. Then, subway station, which is also one of the very important public civil infrastructures, generally services for a long period of time. During the service time of stations, they are easily damaged from environmental corrosion, material aging, fatigue, and the coupling effects with long-term loads and extreme loads. Recently, civil construction work on the places near station often creates lots of damages to the station. As these damages accumulate, the performance of station degenerates due to the above factors. They would inevitably reduce the resisting capacity of station against the disaster; even they bring into the collapse of stations with the structural failure under long-term loads and extreme loads. And, if disaster such as earthquake, fire, etc. happens, it causes huge property damage and threatens the human lives. Because of these above reasons, the structural health monitoring system need to be developed for ensuring the safety of station. In this paper, the development directions of the structural health monitoring system with fiber optic sensor and USN for subway station are briefly described.

• Smart Structures and Systems
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• v.18 no.2
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• pp.317-334
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• 2016

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.

• Smart Structures and Systems
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• v.22 no.5
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• pp.509-525
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• 2018

Sensors and systems in Civionics technology play an important role for continuously facilitating real-time structure monitoring systems by detecting and locating damage to or degradation of structures. An advanced materials, design processes, long-term sensing ability of sensors, electromagnetic interference, sensor placement techniques, data acquisition and computation, temperature, harsh environments, and energy consumption are important issues related to sensors for structural health monitoring (SHM). This paper provides a comprehensive survey of various sensor technologies, sensor classes and sensor networks in Civionics research for existing SHM systems. The detailed classification of sensor categories, applications, networking features, ranges, sizes and energy consumptions are investigated, summarized, and tabulated along with corresponding key references. The current challenges facing typical sensors in Civionics research are illustrated with a brief discussion on the progress of SHM in future applications. The purpose of this review is to discuss all the types of sensors and systems used in SHM research to provide a sufficient background on the challenges and problems in optimizing design techniques and understanding infrastructure performance, behavior and current condition. It is observed that the most important factors determining the quality of sensors and systems and their reliability are the long-term sensing ability, data rate, types of processors, size, power consumption, operation frequency, etc. This review will hopefully lead to increased efforts toward the development of low-powered, highly efficient, high data rate, reliable sensors and systems for SHM.