• Journal of the Korean Society for Railway
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• v.5 no.3
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• pp.148-157
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• 2002

A health monitoring system becomes a useful tool to obtain information on long term behavior of the important railway structures such as very long span and special type bridges. The health monitoring system not only gives the direct measurement data of the railway bridges but also provides the basic data on the maintenance of the structures. Therefore, periodic calibrations of the health monitoring system will be a necessary step toward precise and accurate assessment of the railway bridges. In this study, the calibration and gauge factor readjustment process made for the health monitoring system installed in the railroad bridges is reviewed and some findings are explained in detail: specifically, the calibrators made for this purpose are illustrated and the regression processes of the calibration on long-term displacement using water level sensor, longitudinal displacement using LVDT sensor, instantaneous displacement using LVDT sensors and accelerometer are described in full length. Based on the regression results, it was found that the gauge factors need to be readjusted according to the regression equation but, since the deviation or shift is not serious so far, long-term observation on each sensor is also recommended. Future work will be concentrated on the long-term analysis of each sensor and on the database creation so that the assessment of the structures is possible.

• Journal of the Korean Society for Railway
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• v.6 no.4
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• pp.232-238
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• 2003

A health monitoring system becomes a very useful tool to obtain information on long term behavior of the important railway structures such as very long span and special type bridges. It can be also used to give a warning signal to the maintenance engineer when the structure shows abnormal behavior. However, due to long term use and temperature changes, the health monitoring system needs to be calibrated periodically. In this study, calibration and gauge factor readjustment process made for the health monitoring system installed in the railroad bridges and tunnel are reviewed and a few findings are updated. Future work will be concentrated on the long-term analysis of the measurement data and on the database structures so that the assessment of the structure is possible

• Smart Structures and Systems
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• v.25 no.3
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• pp.285-299
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• 2020

Long term structural health monitoring has gained wide attention among civil engineers in recent years due to the scale and severity of infrastructure deterioration. Establishing effective damage indicators and proposing enhanced monitoring methods are of great interests to the engineering practices. In the case of bridge health monitoring, long term structural vibration measurement has been acknowledged to be quite useful and utilized in the planning of maintenance works. Previous researches are majorly concentrated on linear time series models for the measurement, whereas nonlinear dependences among the measurement are not carefully considered. In this paper, a new bridge health monitoring method is proposed based on the use of long term vibration measurement. A combination of the fundamental ARMA model and copula theory is investigated for the first time in detecting bridge structural damages. The concept is applied to a real engineering practice in Japan. The efficiency and accuracy of the copula based damage indicator is analyzed and compared in different window sizes. The performance of the copula based indicator is discussed based on the damage detection rate between the intact structural condition and the damaged structural condition.

• Smart Structures and Systems
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• v.21 no.2
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• pp.171-185
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• 2018

Maintenance of deteriorated bridge structures has always been one of the challenging issues in developing countries as it is directly related to daily life of people including trade and economy. An effective maintenance strategy is highly dependent on timely inspections on the bridge health condition. This study is intended to investigate an approach for detecting bridge damage for the long-term health monitoring by use of copula theory. Long-term measured data for the seven-span plate-Gerber bridge is investigated. Autoregressive time series models constructed for the observed accelerations taken from the bridge are utilized for the computation of damage indicator for the bridge. The copula model is used to analyze the statistical changes associated with the modal parameters. The changes in the modal parameters with the time are identified by the copula statistical properties. Applicability of the proposed method is also discussed based on a comparison study among other approaches.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.1
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• pp.114-126
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• 2016

Many cohort studies have reported associations of individual-level long-term exposures to $PM_{10}$ and health outcomes. Individual exposures were often estimated by using exposure prediction models relying on $PM_{10}$ data measured at national regulatory monitoring sites. This study explored spatial and temporal characteristics of regulatory $PM_{10}$ measurement data in South Korea and suggested $PM_{10}$ concentration metrics as long-term exposures for assessing health effects in cohort studies. We obtained hourly $PM_{10}$ data from the National Institute of Environmental Research for 2001~2012 in South Korea. We investigated spatial distribution of monitoring sites using the density and proximity in each of the 16 metropolitan cities and provinces. The temporal characteristics of $PM_{10}$ measurement data were examined by annual/seasonal/diurnal patterns across urban background monitoring sites after excluding Asian dust days. For spatial characteristics of $PM_{10}$ measurement data, we computed coefficient of variation (CV) and coefficient of divergence (COD). Based on temporal and spatial investigation, we suggested preferred long-term metrics for cohort studies. In 2010, 294 urban background monitoring sites were located in South Korea with a site over an area of $415.0km^2$ and distant from another site by 31.0 km on average. Annual average $PM_{10}$ concentrations decreased by 19.8% from 2001 to 2012, and seasonal $PM_{10}$ patterns were consistent over study years with higher concentrations in spring and winter. Spatial variability was relatively small with 6~19% of CV and 21~46% of COD across 16 metropolitan cities and provinces in 2010. To maximize spatial coverage and reflect temporal and spatial distributions, our suggestion for $PM_{10}$ metrics representing long-term exposures was the average for one or multiple years after 2009. This study provides the knowledge of all available $PM_{10}$ data measured at national regulatory monitoring sites in South Korea and the insight of the plausible longterm exposure metric for cohort studies.

• Corrosion Science and Technology
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• v.3 no.2
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• pp.39-46
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• 2004

Development and application of structural health monitoring system in Korea have become active since the early 1990's. In earlier applications, health monitoring systems were installed in several existing bridges in order to collect initial field data by full scale load capacity test for design verification and subsequently monitor long-term performance and durability of the bridge as part of an inspection and maintenance program. Recently, modem and integrated monitoring systems have been introduced in most of the newly constructed long-span bridges since the design stage. This paper outlines the progresses and applications of monitoring systems in Korea for both existing and newly constructed bridges and describes their aims and characteristics.

• Journal of Ocean Engineering and Technology
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• v.25 no.4
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• pp.59-65
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• 2011

In structural health monitoring (SHM) using electro-mechanical impedance signatures, it is a critical issue for extremely large structures to extract the best damage diagnosis results, while minimizing unknown environmental effects, including temperature, humidity, and acoustic vibration. If the impedance signatures fluctuate because of these factors, these fluctuations should be eliminated because they might hide the characteristics of the host structural damages. This paper presents a long-term SHM technique under an unknown noisy environment for tidal current power plant structures. The obtained impedance signatures contained significant variations during the measurements, especially in the audio frequency range. To eliminate these variations, a continuous principal component analysis was applied, and the results were compared with the conventional approach using the RMSD (Root Mean Square Deviation) and CC (Cross-correlation Coefficient) damage indices. Finally, it was found that this approach could be effectively used for long-term SHM in noisy environments.

• Smart Structures and Systems
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• v.10 no.3
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• pp.271-298
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• 2012

Researchers have made significant progress in recent years towards realizing effective structural health monitoring (SHM) utilizing wireless smart sensor networks (WSSNs). These efforts have focused on improving the performance and robustness of such networks to achieve high quality data acquisition and distributed, in-network processing. One of the primary challenges still facing the use of smart sensors for long-term monitoring deployments is their limited power resources. Periodically accessing the sensor nodes to change batteries is not feasible or economical in many deployment cases. While energy harvesting techniques show promise for prolonging unattended network life, low power design and operation are still critically important. This research presents the WiSeMote: a new, fully integrated ultra-low power wireless smart sensor node and a flexible base station, both designed for long-term SHM deployments. The power consumption of the sensor nodes and base station has been minimized through careful hardware selection and the implementation of power-aware network software, without sacrificing flexibility and functionality.

• Smart Structures and Systems
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• v.31 no.1
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• pp.45-56
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• 2023

Vibration-based structural health monitoring is used to ensure the safety of structures by installing sensors in structures. The peak picking method, one of the applications of vibration-based structural health monitoring, is a method that analyze the dynamic characteristics of a structure using the peaks of the frequency response function. However, the results may vary depending on the person predicting the peak point; further, the method does not predict the exact peak point in the presence of noise. To overcome the limitations of the existing peak picking methods, this study proposes a new method to automate the modal analysis process by utilizing long short-term memory, a type of recurrent neural network. The method proposed in this study uses the time series data of the frequency response function directly as the input of the LSTM network. In addition, the proposed method improved the accuracy by using the phase as well as amplitude information of the frequency response function. Simulation experiments and lab-scale model experiments are performed to verify the performance of the LSTM network developed in this study. The result reported a modal assurance criterion of 0.8107, and it is expected that the dynamic characteristics of a civil structure can be predicted with high accuracy using data without experts.

• Smart Structures and Systems
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• v.15 no.2
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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.