• Computers and Concrete
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• 제30권5호
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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.

• The Korean Journal of Ceramics
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• 제6권3호
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• pp.304-308
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• 2000

WO$_3$/SnO$_2$ceramics has been suggested as an effective sensing material for monitoring offensive odor or pollutant gases. This work was focussed on improving long-term stability, which has been a principal problem generally taking place in SnO$_2$semiconductor gas sensor. Miniaturized thick film gas sensors were fabricated by screen printing technique. Two types of sensor materials, W doped SnO$_2$and WO$_3$mixed SnO$_2$, were comparatively investigated on those long-term stability and sensitivites to several gases. Small amount of W doping(0.1 mol%) into SnO$_2$largely improved the long-term stability. The W(0.1 mol%) doped SnO$_2$gas sensor had higher sensitivities to both acetone and alcohol compared with WO$_3$(5 wt%) mixed SnO$_2$gas sensor. On the contrary, WO$_3$(5 wt%) mixed SnO$_2$gas sensor showed more superior sensitivity to cigarette smoke due to larger W content.

• 한국지열·수열에너지학회논문집
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• 제6권1호
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• pp.29-35
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• 2010

Long-term temperature monitoring has been performed for ground heat exchanger at the Earthquake Research Center (ERC) building in Korea Institute of Geoscience and Mineral Resources (KIGAM). For the 3 years of monitoring, overall temperature increases are observed at various depths within a borehole heat exchanger. But monitoring of ground temperature variation at the monitoring well beforehand showed that geothermal utilization is not the only source for the temperature increase, Because various kinds of sources can cause the ground temperature change, more thorough investigation should be followed.

• Smart Structures and Systems
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• 제15권2호
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• pp.395-408
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• 2015

This study aims to propose a Bayesian approach to consider changes in temperature and vehicle weight as environmental and operational factors for vibration-based long-term bridge health monitoring. The Bayesian approach consists of three steps: step 1 is to identify damage-sensitive features from coefficients of the auto-regressive model utilizing bridge accelerations; step 2 is to perform a regression analysis of the damage-sensitive features to consider environmental and operational changes by means of the Bayesian regression; and step 3 is to make a decision on the bridge health condition based on residuals, differences between the observed and predicted damage-sensitive features, utilizing 95% confidence interval and the Bayesian hypothesis testing. Feasibility of the proposed approach is examined utilizing monitoring data on an in-service bridge recorded over a one-year period. Observations through the study demonstrated that the Bayesian regression considering environmental and operational changes led to more accurate results than that without considering environmental and operational changes. The Bayesian hypothesis testing utilizing data from the healthy bridge, the damage probability of the bridge was judged as no damage.

• International Journal of Concrete Structures and Materials
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• 제2권2호
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• pp.91-98
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• 2008

Curved bridges are important components of a highway transportation network for connecting local roads and highways, but very few data have been collected in terms of their field performance. This paper presents two-years monitoring and system identification results of a curved concrete box-girder bridge, the West St. On-Ramp, under ambient traffic excitations. The authors permanently installed accelerometers on the bridge from the beginning of the bridge life. From the ambient vibration data sets collected over the two years, the element stiffness correction factors for the columns, the girder, and boundary springs were identified using the back-propagation neural network. The results showed that the element stiffness values were nearly 10% different from the initial design values. It was also observed that the traffic conditions heavily influence the dynamic characteristics of this curved bridge. Furthermore, a probability distribution model of the element stiffness was established for long-term monitoring and analysis of the bridge stiffness change.

• 제어로봇시스템학회논문지
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• 제18권8호
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• pp.738-743
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• 2012

This paper describes analysis of long-term experiments on a monitoring system to assess the daily activities of the elderly who live alone. The developed system is composed of an in-house sensing system and a server system. The in-house sensing system installed in their own houses is a typical wireless sensor network system including three kinds of wireless sensors. The server system has a database server and an assessment server. We have installed the system into an elderly house, collected data during over two years continuously, then analyze the data. From the analysis, we could measure the energy consumption profile of three kinds of sensor nodes. The experiment shows all kinds of nodes can operate over one year with two AA-size alkaline batteries. Using a measure of reliability of the monitoring system called 'deadzone', the system has showed the failure operation for 842 hours (4.66 %) during over 18,000 hours total operation period.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 가을 학술발표회 논문집
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• pp.717-723
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• 2002

Recently, the interest in safety assessment of civil infrastructures is increasing in Korea. Especially, as bridge structures become large-scale, it is necessary to monitor and maintain the safety state of bridges, which requires the monitoring system that can make a long-term measurement during the service time of bridge. In this paper, advanced fiber optic sensors for long-term measurement, setup techniques of bridge monitoring system and the assessment of measured data are introduced. Attached or embedded optical fiber sensors to structural members of small and big structures including Sung San Bridge are surveyed. For the Sung San Bridge, the responses of the fiber optic sensors by 30 ton weigh truck loads with various speeds ate measured. Monitoring system is also applied to the mock-up of bridges. The monitoring capability of the advanced fiber optic sensor system was confirmed.

• 한국농공학회논문집
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• 제49권1호
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• pp.67-76
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• 2007

Bioventing is commonly used for petroleum hydrocarbon (PHC) spills. This process provides better subsurface oxygenation, thus stimulating degradation by indigenous microorganisms. Therefore soil vapor monitoring points (VMPs) are extremely important in determining the potential effectiveness of bioventing and in long-term monitoring of bioventing progress. In this study in-situ soil gas monitoring well (GMW) was developed and presented the pilot test results which recover the contaminated site by bioventing method. The result of application was successful and it was expected that GMW developed could be applied to the evaluation procedure of bioventing effectiveness and long-term remediation potential.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2000년도 가을 학술발표회 논문집(II)
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• pp.1297-1302
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• 2000

A literature review has been carried out to investigate why bridges have collapsed without warning. The reasons behind the collapses have been categorized into short and long term risks. It is thought that permanent monitoring systems which assess structural adequacy are more appropriate to long term risks. From the knowledge of the Korean bridge stock, its current problems and its likely future problems, it was considered that generally the most useful application for a permanent monitoring system is to monitor where chloride-induced corrosion either of the reinforcement or prestressing tendons is possible. A number of permanent monitoring systems currently in use on existing bridges which include some aspect of corrosion detection have been reviewed. The reasons as to why they are being used, what is being measured, what techniques are being used, and if they are deemed successful has been investigated. Based on these findings, and experimental programme has been constructed to investigate the accuracy, reliability and usefulness of various suitable techniques which could be included in a permanent monitoring system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권10호
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• pp.3554-3570
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• 2021

With the rapid development of the Chinese water project, the safety monitoring of dams is urgently needed. Many drawbacks exist in dams, such as high monitoring costs, a limited equipment service life, long-term monitoring difficulties. MEMS sensors have the advantages of low cost, high precision, easy installation, and simplicity, so they have broad application prospects in engineering measurements. This paper designs intelligent monitoring based on the collaborative measurement of dual MEMS sensors. The system first determines the endpoint coordinates of the sensor array by the coordinate transformation relationship in the monitoring system and then obtains the dam settlement according to the endpoint coordinates. Next, this paper proposes a dual-MEMS sensor collaborative measurement algorithm that builds a mathematical model of the dual-sensor measurement. The monitoring system realizes mutual compensation between sensor measurement data by calculating the motion constraint matrix between the two sensors. Compared with the single-sensor measurement, the dual-sensor measurement algorithm is more accurate and can improve the reliability of long-term monitoring data. Finally, the experimental results show that the dam subsidence monitoring system proposed in this paper fully meets the engineering monitoring accuracy needs, and the dual-sensor collaborative measurement system is more stable than the single-sensor monitoring system.