• International conference on construction engineering and project management
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• 2013.01a
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• pp.517-520
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• 2013

In construction operation, the temporary structure is used to support designed facilities or to provide work spaces for construction activities. Since the structure is used only during the construction operation, the operation may be given insufficient attention. The contractor is likely to try to save cost on the material and labor cost. This contractor's behavior frequently leads to construction accidents. In order to prevent accidents from the failure, the operation should be carefully monitored for identifying the effect of dynamics in the surrounding site area. Otherwise, any unexpected adversary effect could result in a very costly construction failure. This study presents the feasibility of the ubiquitous sensor network (USN) technology in collecting construction data during the construction operation of earth retaining walls. The study is based on the result at the Construction System Integration Laboratory (CSIL) at the Pusan National University. A USN-based system has been developed for monitoring the behavior of the temporary structure of earth retaining walls. The data collected from the sensors were used to understand the behavior of the temporary structure. The result of this study will be used in increasing the safety during the construction operation of retaining walls.

• Journal of Welding and Joining
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• v.21 no.4
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• pp.80-86
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• 2003

Arc-spot welding is generally used in joining of precise parts such as case and core in electric compressor. It is important to control joining deformation in electric compressor because clearance control of micrometer order is needed for excellent airtightness and anti-nose. The countermeasures for this deformation in field have mainly been dependent on rule of try and error by operator's experience because of productivities. For control this deformation problem without influence on productivities, development of exact simulation model should be needed. In this study, on the basis of previous study, the analysis model io predict deformation of precise order in arc-spot welded structure with non-uniform stiffness is brought up through feedback and tuning between monitoring data and analysis results. For this, deformation monitoring system was built and boundary condition considering mechanical melting temperature was applied.

• Smart Structures and Systems
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• v.20 no.2
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• pp.181-195
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• 2017

In this study, the severity of damage in tendon anchorage caused by the loss of tendon forces is quantitatively identified by using the PZT interface-based impedance monitoring technique. Firstly, a 2-DOF impedance model is newly designed to represent coupled dynamic responses of PZT interface-host structure. Secondly, the 2-DOF impedance model is adopted for the tendon anchorage system. A prototype of PZT interface is designed for the impedance monitoring. Then impedance signatures are experimentally measured from a laboratory-scale tendon anchorage structure with various tendon forces. Finally, damage severities of the tendon anchorage induced by the variation of tendon forces are quantitatively identified from the phase-by-phase model updating process, from which the change in impedance signatures is correlated to the change in structural properties.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.21-28
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• 2006

Structural health monitoring (SHM) is a new technology that will be increasingly applied at the industrial field as a potential approach to improve cost and convenience of structural inspection. Recently, the development of smart sensor is very active for real application. This study has focused on preparation and application study of SAL sensor. In order to detect elastic wave, smart piezoelectric sensor, SAL, is fabricated by using a piezoelectric element, shielding layer and protection layer. This protection layer plays an important role in a patched network of distributed piezoelectric sensor and shielding treatment. Four types of SAL sensor are designed/prepared/tested, and these details will be discussed in the paper. In this study, SAL sensor can be feasibly applied to perform structural health monitoring and to detect damage sources which result in elastic waves.

• 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

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.15-16
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• 2015

Measuring sensor, equipment, ICT facilities and their software have relatively short life time comparing to constructional structure so that we should exchange or fix them continuously in the process of maintenance and management. In this paper, we propose a novel design of integrated maintenance, management, and measuring monitoring system applying the concept of mobile cloud. For the sake of disaster prevention for constructional structures such as bridge, tunnel, and other traditional buildings in the village of local heritage, we analyze status of these structures in the long term or short term period as well as disaster situations. Collecting data based on mobile cloud and analyzing future expectations based on probabilistic and statistical techniques, we implement our integrated monitoring system for constructional structures to solve these existing problems. Final results of this design and implementation are basically applied to the monitoring system for more than 10,000 structures spread over national land in Korea. In addition, we can specifically apply the monitoring system presented here to a bridge of timber structure in Asan Oeam Village and a traditional house in Andong Hahoe Village to watch them from possible disasters. Total procedure of system design and implementation as well as development of the platform LinkSaaS and application services of monitoring functions implemented on the platform. We prove a good performance of our system by fulfilling TTA authentication test, web accommodation test, and operation test using real measuring data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2708-2716
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• 2013

This paper presents implementation of embedded remote monitoring diagnosis system which has effective wireless channel structure and communication protocols with user-friendly UI for intelligent digital vessel. Developed system contains integrated vessel monitoring system, server, exclusive mobile terminal and smart phone. We designed an effective dual structure communication channel and simple but effective communication protocol on the monitoring system. Failures of the wireless communication are minimized and the wrong wireless communication channel is immediately replaced. In addition, we developed an effective embedded Linux UI for LCD. The implemented wireless monitoring system was tested and verified on digital vessel.

• Smart Structures and Systems
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• v.23 no.3
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• Smart Structures and Systems
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• v.31 no.5
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• pp.469-483
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• 2023

The dynamic characteristics of wind turbine blades are usually monitored by contact sensors with the disadvantages of high cost, difficult installation, easy damage to the structure, and difficult signal transmission. In view of the above problems, based on computer vision technology and the improved YOLOv5 (You Only Look Once v5) deep learning model, a non-contact dynamic characteristic monitoring method for wind turbine blade is proposed. First, the original YOLOv5l model of the CSP (Cross Stage Partial) structure is improved by introducing the CSP2_2 structure, which reduce the number of residual components to better the network training speed. On this basis, combined with the Deep sort algorithm, the accuracy of structural displacement monitoring is mended. Secondly, for the disadvantage that the deep learning sample dataset is difficult to collect, the blender software is used to model the wind turbine structure with conditions, illuminations and other practical engineering similar environments changed. In addition, incorporated with the image expansion technology, a modeling-based dataset augmentation method is proposed. Finally, the feasibility of the proposed algorithm is verified by experiments followed by the analytical procedure about the influence of YOLOv5 models, lighting conditions and angles on the recognition results. The results show that the improved YOLOv5 deep learning model not only perform well compared with many other YOLOv5 models, but also has high accuracy in vibration monitoring in different environments. The method can accurately identify the dynamic characteristics of wind turbine blades, and therefore can provide a reference for evaluating the condition of wind turbine blades.

• Earthquakes and Structures
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• v.12 no.6
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• pp.673-687
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• 2017

A three-dimensional; structural health monitoring; vertical; planar; cross-sample entropy; multiscaleA three-dimensional structural health monitoring (SHM) system based on multiscale entropy (MSE) and multiscale cross-sample entropy (MSCE) is proposed in this paper. The damage condition of a structure is rapidly screened through MSE analysis by measuring the ambient vibration signal on the roof of the structure. Subsequently, the vertical damage location is evaluated by analyzing individual signals on different floors through vertical MSCE analysis. The results are quantified using the vertical damage index (DI). Planar MSCE analysis is applied to detect the damage orientation of damaged floors by analyzing the biaxial signals in four directions on each damaged floor. The results are physically quantified using the planar DI. With progressive vertical and planar analysis methods, the damaged floors and damage locations can be accurately and efficiently diagnosed. To demonstrate the performance of the proposed system, performance evaluation was conducted on a three-dimensional seven-story steel structure. According to the results, the damage condition and elevation were reliably detected. Moreover, the damage location was efficiently quantified by the DI. Average accuracy rates of 93% (vertical) and 91% (planar) were achieved through the proposed DI method. A reference measurement of the current stage can initially launch the SHM system; therefore, structural damage can be reliably detected after major earthquakes.