• 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.

• Smart Structures and Systems
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• v.29 no.4
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• pp.561-576
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• 2022

Heritage timber structures represent the history and culture of a nation. These structures have been inherited from previous generations; however, they inevitably exhibit deterioration over time, potentially leading to structural deficiencies. Structural Health Monitoring (SHM) offers the potential to assess operational anomalies, deterioration, and damage through processing and analysis of data collected from transducers and sensors mounted on the structure. This paper reports on the design and implementation of a long-term SHM system on the Feiyun Wooden Pavilion in China, a three-story timber building built more than 500 years ago. The principles and features of the design and implementation of SHM systems for heritage timber buildings are systematically discussed. In total, 104 sensors of 6 different types are deployed on the structure to monitor the environmental effects and structural responses, including air temperature and humidity, wind speed and direction, structural temperatures, strain, inclination, and acceleration. In addition, integrated data acquisition and transmission subsystem using a newly developed software platform are implemented. Selected preliminary statistical and correlation analysis using one year of monitoring data are presented to demonstrate the condition assessment capability of the system based on the monitoring data.

• Journal of Korean Society for Quality Management
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• v.39 no.1
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• pp.90-97
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• 2011

The purpose of this study is to analyze various factors comprising a job satisfaction; determine possible factors that affects job satisfaction. Job satisfaction model is designed to evaluate major factors, such as job stress and strength, and to assess relationship between these factors. Partial least squares algorithm is used to develop a job satisfaction measurement model. To evaluate validity of developed model, survey data of health insurance review and assessment service is to applied.

• Structural Engineering and Mechanics
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• v.32 no.4
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• pp.489-500
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• 2009

The aim of this paper is to compare wavelet, kurtosis and pseudofractal based techniques for structural health monitoring in the presence of measurement noise. A detailed comparison and assessment of these techniques have been carried out in this paper through numerical experiments for the calibration of damage extent of a simply supported beam with an open crack serving as an illustrative example. The numerical experiments are deemed critical due to limited amount of experimental data available in the field of singularity based detection of damage. A continuous detectibility map has been proposed for comparing various techniques qualitatively. Efficiency surfaces have been constructed for wavelet, kurtosis and pseudofractal based calibration of damage extent as a function of damage location and measurement noise level. Levels of noise have been identified for each technique where a sudden drop of calibration efficiency is observed marking the onset of damage masking regime by measurement noise.

• Health Policy and Management
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• v.28 no.3
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• pp.197-201
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• 2018

There is a general consensus that many health care problems are attributable to the structural defects of the health care delivery system in Korea. The basic policy aimed to address these problems is to reform the delivery system so as that it incorporates two core principles: (1) stratification of medical care institutions into primary, secondary, and tertiary care providers according to the capability to perform specialized and complex services; (2) patients seeking care starting from the primary care provider and, if necessary, to be referred to the other provider step by step. This policy has been consistently pursued for about 30 years, but the achievement is far from success. Thus it is believed that the feasibility of the policy should be questioned. Starting from this question, based upon the observation of the current structure of the delivery system and its expected changes, the reform policy was discussed focusing on the assessment of its feasibility from both practical and theoretical viewpoints. The discussion leads to cast doubt on the policy for its possibility of making planned changes and producing expected desirable effects. Therefore it is advisable to investigate a wide range of alternative strategies and models for improving health care delivery.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.603-606
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• 2006

We present a study on the development of a practical and quantitative technique for the assessment of the structural health condition with using piezoceramic(PZT) sensors. The electro-impedance-based technique with the PZT patches is very sensitive for evaluation of the incipient and small damage in a high frequency range, and however the commonly traditional modal analysis method is effective only for considerably larger damages in low frequency range. The paper presents the technique in detecting and characterizing real-time damage on the specimen that is an aluminum plate fastened with bolts and nuts by different torques and as well a plate with a crack. By using the special arrangement of the PZT sensors, the required longitudinal wave is generated through the specimen. A large number of experiments are conducted and the different conditions of the specimens, i.e. the location and extent of loosening bolts, and the plate with a crack are simulated. respectively. Since fixing and loosening the loosened bolt is controlled by a torque wrench, we can control exactly the experiment of the different torques. Compared with the simulated healthy condition, we can find whether or not there is a damage in the specimen with using an impedance analyzer with the PZT sensors. Several indices are discussed and used for assessing the different simulated damages. As for the location of bolt loosening, the RMSD is found to be the most appropriate index for numerical assessment and as well the RMSD shows strongly linear relationship for assessing the extent of the bolt loosening, and the frequency peak shift ${\Delta}F$ is used to assess the cracked plate. The possibility of repeatability of the pristine condition signatures is also presented and the appropriate frequency range and interval are uniquely selected through large numbers of experiments.

• Smart Structures and Systems
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• v.5 no.4
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• pp.345-355
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• 2009

This article describes the development of a fiber optic accelerometer based on Fiber Bragg Gratings (FBG). The accelerometer utilizes the stiffness of the optical fiber and a lumped mass in the design. Acceleration is measured by the FBG in response to the vibration of the fiber optic mass system. The wavelength shift of FBG is proportional to the change in acceleration, and the gauge factor pertains to the shift in wavelength as a function of acceleration. Low frequency version of the accelerometer was developed for applications in monitoring bridges. The accelerometer was first evaluated in laboratory settings and then employed in a demonstration project for condition assessment of a bridge. Laboratory experiments involved evaluation of the sensitivity and resolution of measurements under a series of low frequency low amplitude conditions. The main feature of this accelerometer is single channel multiplexing capability rendering the system highly practical for application in condition assessment of bridges. This feature of the accelerometer was evaluated by using the system during ambient vibration tests of a bridge. The Frequency Domain Decomposition method was employed to identify the mode shapes and natural frequencies of the bridge. Results were compared with the data acquired from the conventional accelerometers.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1329-1344
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• 2015

The stiffness of a structure is one of several structural signals that are useful indicators of the amount of damage that has been done to the structure. To accurately estimate the stiffness, an equation of motion containing a stiffness parameter must first be established by expansion as a linear series model, a Taylor series model, or a power series model. The model is then used in multivariate autoregressive modeling to estimate the structural stiffness and compare it to the theoretical value. Stiffness assessment for modeling purposes typically involves the use of one of three statistical model refinement approaches, one of which is the efficient Akaike information criterion (AIC) proposed in this paper. If a newly added component of the model results in a decrease in the AIC value, compared to the value obtained with the previously added component(s), it is statistically justifiable to retain this new component; otherwise, it should be removed. This model refinement process is repeated until all of the components of the model are shown to be statistically justifiable. In this study, this model refinement approach was compared with the two other commonly used refinement approaches: principal component analysis (PCA) and principal component regression (PCR) combined with the AIC. The results indicate that the proposed AIC approach produces more accurate structural stiffness estimates than the other two approaches.

• Journal of the Korean Society of Marine Environment & Safety
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• v.7 no.3
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• pp.61-74
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• 2001

The prevention of marine accidents has been a major topic in marine society and various policies and countermeasures has been developed, applied to the industries. Formal Safety Assessment is a structured and systematic methodology, aimed at enhancing maritime safety, including protection of life, health, the marine environment and property, by using risk and cost-benefit assessments. In addition, it provides a means of being proactive, enabling potential hazards to be considered before a serious accident occurs. In this paper, we has been screening and ranking of hazards using fuzzy structural modeling method and quantitative risk assessment for the ship's collision in the last 10 years marine accidents.

• Smart Structures and Systems
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• v.30 no.6
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.