• Journal of Digital Convergence
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• v.12 no.4
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• pp.399-407
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• 2014

The purpose of this study was to provide basic data necessary for improving the oral health of the elderly and quality of their lives by analyzing the relationship among the geriatric denture-related characteristics, denture satisfaction, and geriatric oral health assessment index. For this study, the elderly aged 65 or higher who resided in Euseong-gun, Yeongju-si, Andong-si, Gyeongsangbuk-do were surveyed from March 25, 2013 to May 9 of the same year. The results of this study showed that the denture satisfaction had high correlation with the 'satisfaction with denture attachment', 'satisfaction with aesthetic function of pronunciation', and satisfaction with masticatory function. Moreover, the causative relation was found to exist among the geriatric denture-related characteristics, denture satisfaction, and geriatric oral health assessment index. Thus, it is considered necessary to establish the institutional system and take measures that can improve the awareness towards the geriatric oral health education and geriatric oral health state with respect to effective use and management of denture.

• Computers and Concrete
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• v.20 no.5
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• pp.555-562
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• 2017

The accuracy and integrity of stress data acquired by bridge heath monitoring system is of significant importance for bridge safety assessment. However, the missing and abnormal data are inevitably existed in a realistic monitoring system. This paper presents a data reconstruction approach for bridge heath monitoring based on the wavelet multi-resolution analysis and support vector machine (SVM). The proposed method has been applied for data imputation based on the recorded data by the structural health monitoring (SHM) system instrumented on a prestressed concrete cable-stayed bridge. The effectiveness and accuracy of the proposed wavelet-based SVM prediction method is examined by comparing with the traditional autoregression moving average (ARMA) method and SVM prediction method without wavelet multi-resolution analysis in accordance with the prediction errors. The data reconstruction analysis based on 5-day and 1-day continuous stress history data with obvious preternatural signals is performed to examine the effect of sample size on the accuracy of data reconstruction. The results indicate that the proposed data reconstruction approach based on wavelet multi-resolution analysis and SVM is an effective tool for missing data imputation or preternatural signal replacement, which can serve as a solid foundation for the purpose of accurately evaluating the safety of bridge structures.

• Smart Structures and Systems
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• v.24 no.5
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• pp.617-630
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• 2019

Currently most of the vision-based structural identification research focus either on structural input (vehicle location) estimation or on structural output (structural displacement and strain responses) estimation. The structural condition assessment at global level just with the vision-based structural output cannot give a normalized response irrespective of the type and/or load configurations of the vehicles. Combining the vision-based structural input and the structural output from non-contact sensors overcomes the disadvantage given above, while reducing cost, time, labor force including cable wiring work. In conventional traffic monitoring, sometimes traffic closure is essential for bridge structures, which may cause other severe problems such as traffic jams and accidents. In this study, a completely non-contact structural identification system is proposed, and the system mainly targets the identification of bridge unit influence line (UIL) under operational traffic. Both the structural input (vehicle location information) and output (displacement responses) are obtained by only using cameras and computer vision techniques. Multiple cameras are synchronized by audio signal pattern recognition. The proposed system is verified with a laboratory experiment on a scaled bridge model under a small moving truck load and a field application on a footbridge on campus under a moving golf cart load. The UILs are successfully identified in both bridge cases. The pedestrian loads are also estimated with the extracted UIL and the predicted weights of pedestrians are observed to be in acceptable ranges.

• Safety and Health at Work
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• v.6 no.3
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• pp.200-205
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• 2015

Background: Construction activity has made considerable breakthroughs in the past two decades on the back of increases in development activities, government policies, and public demand. At the same time, occupational health and safety issues have become a major concern to construction organizations. The unsatisfactory safety performance of the construction industry has always been highlighted since the safety management system is neglected area and not implemented systematically in Indian construction organizations. Due to a lack of enforcement of the applicable legislation, most of the construction organizations are forced to opt for the implementation of Occupational Health Safety Assessment Series (OHSAS) 18001 to improve safety performance. Methods: In order to better understand factors influencing the implementation of OHSAS 18001, an interpretive structural modeling approach has been applied and the factors have been classified using matrice d'impacts croises-multiplication $appliqu{\acute{e}}$ a un classement (MICMAC) analysis. The study proposes the underlying theoretical framework to identify factors and to help management of Indian construction organizations to understand the interaction among factors influencing in implementation of OHSAS 18001. Results: Safety culture, continual improvement, morale of employees, and safety training have been identified as dependent variables. Safety performance, sustainable construction, and conducive working environment have been identified as linkage variables. Management commitment and safety policy have been identified as the driver variables. Conclusion: Management commitment has the maximum driving power and the most influential factor is safety policy, which states clearly the commitment of top management towards occupational safety and health.

• Structural Engineering and Mechanics
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• v.54 no.2
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• pp.199-219
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• 2015

This paper presents the non-destructive evaluation of a high-speed railway bridge using train-induced strain responses. Based on the train-track-bridge interaction analysis, the strain responses of a high-speed railway bridge under moving trains with different operation status could be calculated. The train induced strain responses could be divided into two parts: the force vibration stage and the free vibration stage. The strain-displacement relationship is analysed and used for deriving critical displacements from theoretical stain measurements at a forced vibration stage. The derived displacements would be suitable for the condition assessment of the bridge through design specifications defined indexes and would show certain limits to the practical application. Thus, the damage identification of high-speed railways, such as the stiffness degradation location, needs to be done by comparing the measured strain response under moving trains in different states because the vehicle types of high-speed railway are relatively clear and definite. The monitored strain responses at the free vibration stage, after trains pass through the bridge, would be used for identifying the strain modes. The relationship between and the degradation degree and the strain mode shapes shows certain rules for the widely used simply supported beam bridges. The numerical simulation proves simple and effective for the proposed method to locate and quantify the stiffness degradation.

• Smart Structures and Systems
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• v.29 no.3
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• pp.475-484
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• 2022

The loss of cable tension for civil infrastructure reduces structural bearing capacity and causes harmful deformation of structures. Currently, most of the structural health monitoring (SHM) approaches for cables rely on contact transducers. This paper proposes a cable tension identification technology using percussion sound, which provides a fast determination of steel cable tension without physical contact between cables and sensors. Notably, inspired by the concept of tensioning strings for piano tuning, this proposed technology predicts cable tension value by deep learning assisted classification of "percussion" sound from tapping a steel cable. To simulate the non-linear mapping of human ears to sound and to better quantify the minor changes in the high-frequency bands of the sound spectrum generated by percussions, Mel-frequency cepstral coefficients (MFCCs) were extracted as acoustic features to train the deep learning network. A convolutional neural network (CNN) with four convolutional layers and two global pooling layers was employed to identify the cable tension in a certain designed range. Moreover, theoretical and finite element methods (FEM) were conducted to prove the feasibility of the proposed technology. Finally, the identification performance of the proposed technology was experimentally investigated. Overall, results show that the proposed percussion-based technology has great potentials for estimating cable tension for in-situ structural safety assessment.

• Smart Structures and Systems
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• v.31 no.2
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• pp.183-197
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• 2023

For long-span bridges with a structural health monitoring (SHM) system, environmental temperature-driven responses are proved to be a main component in measurements. However, anomalous structural behavior may be hidden incomplicated recorded data. In order to receive reliable assessment of structural performance, it is important to study therelationship between temperature and monitoring data. This paper presents an application of the cointegration based methodology to detect anomalies that may be masked by temperature effects and then forecast the temperature-induced deflection (TID) of long-span suspension bridges. Firstly, temperature effects on girder deflection are analyzed with fieldmeasured data of a suspension bridge. Subsequently, the cointegration testing procedure is conducted. A threshold-based anomaly detection framework that eliminates the influence of environmental temperature is also proposed. The cointegrated residual series is extracted as the index to monitor anomaly events in bridges. Then, wavelet separation method is used to obtain TIDs from recorded data. Combining cointegration theory with autoregressive moving average (ARMA) model, TIDs for longspan bridges are modeled and forecasted. Finally, in-situ measurements of Xihoumen Bridge are adopted as an example to demonstrate the effectiveness of the cointegration based approach. In conclusion, the proposed method is practical for actual structures which ensures the efficient management and maintenance based on monitoring data.

• Structural Engineering and Mechanics
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• v.44 no.4
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• pp.417-429
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• 2012

Taking the superficial temperature increment as the major fatigue damage indicator, the infrared thermography was used to predict fatigue parameters (fatigue strength and S-N curve) of welded joints subjected to fatigue loading with a high mean stress, showing good predictions. The fatigue damage status, related to safety evaluation, was tightly correlated with the temperature field evolution of the hot-spot zone on the specimen surface. An energetic damage model, based on the energy accumulation, was developed to evaluate the residual fatigue life of the welded specimens undergoing cyclic loading, and a good agreement was presented. It is concluded that the infrared thermography can not only well predict the fatigue behavior of welded joints, but also can play an important role in health detection of structures subjected to mechanical loading.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.273-276
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• 2006

In this paper the recent research and application activities on smart structure technologies for civil infra structures in Korea are briefly introduced. The developments of structural health monitoring systems and effective retrofit/maintenance methodologies for infra structures have become active in Korea since the middle of 1990's, as the number of the deteriorated infra structures, mostly built on the rapidly industrialized period of 1970's, has increased very rapidly. Discussions are made on smart sensors, non destructive technologies, monitoring and assessment methods and systems for civil infra structures.

• Smart Structures and Systems
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• v.12 no.3_4
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• pp.399-414
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• 2013

The smart sensor technology has opened new horizons for assessing and monitoring structural health of civil infrastructure. Smart sensor's unique features such as onboard computation, wireless communication, and cost effectiveness can enable a dense network of sensors that is essential for accurate assessment of structural health in large-scale civil structures. While most research efforts to date have been focused on realizing wireless smart sensor networks (WSSN) on bridge structures, relatively less attention is paid to applying this technology to buildings. This paper presents a decentralized damage detection using the WSSN for building structures. An existing flexibility-based damage detection method is extended to be used in the decentralized computing environment offered by the WSSN and implemented on MEMSIC's Imote2 smart sensor platform. Numerical simulation and laboratory experiment are conducted to validate the WSSN for decentralized damage detection of building structures.