• Journal of The Korean Society of Agricultural Engineers
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• v.51 no.3
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• pp.9-14
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• 2009

Flow and pollution load were monitored at 2 combined sewer outlets (C-1 and C-2) of urban watersheds during dry weather from September, 2004 to April, 2006 for 20 months. The objectives were to investigate the diurnal variation of flow and pollutant load and to find the proper sampling time that could measure representative flow and pollutant load. Pollution load closed to the average daily load at C-1 could be measured at 00:00 hour and by the mean of 15:00 and 18:00 hour measures, and 15:00 and 21:00 hour measures, respectively. In addition at C-2, it was 21:00 hour and the mean of 15:00 and 18:00 hour measures. This study concluded that arbitrary sampling of flow and water quality could cause large errors in the estimation of urban pollution load and recommended that urban combined sewers should be monitored when flow and water quality showed daily average and concentration.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.1
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• pp.1-11
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• 2014

In order to determine the optimal number of monitoring points in single-track tunnel lining, this thesis compares and evaluates the results of two cases: when the tunnel lining is modeled into a simple beam form and then is applied to 1) the tunnel lining inverse analysis program, and to 2) the commercially-used program. The displacement and stress of specific tunnel lining cross-sections are determined by entering the load conditions into the commercially-used program for tunnel interpretations. In doing so, two cases were assumed: where a symmetrically-distributed load was acting upon the tunnel lining of a single-track tunnel and where an asymmetrically-distributed load was in action. By comparing the computed displacement with the stress and displacement determined by entering side numbers 3, 5, and 7 into the tunnel lining inverse analysis program, the optimal number of monitoring points is determined. From the results of the research, it can be inferred that the number of monitoring points needs to be at least 5 points, considering the efficiency of monitoring in practice and the loss-and-damage rate of tunnel monitoring.

• Smart Structures and Systems
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• v.28 no.6
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• pp.799-810
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• 2021

When monitoring the structural integrity of a bridge using data collected through accelerometers, identifying the profile of the load exerted on the bridge from the vehicles passing over it becomes a crucial task. In this study, the speed and location of vehicles on the deck of a bridge is reconfigured using real-time video to implicitly associate the load applied to the bridge with the response from the bridge sensors to develop an image-based deep learning network model. Instead of directly measuring the load that a moving vehicle exerts on the bridge, the intention in the proposed method is to replace the correlation between the movement of vehicles from CCTV images and the corresponding response by the bridge with a neural network model. Given the framework of an input-output-based system identification, CCTV images secured from the bridge and the acceleration measurements from a cantilevered beam are combined during the process of training the neural network model. Since in reality, structural damage cannot be induced in a bridge, the focus of the study is on identifying local changes in parameters by adding mass to a cantilevered beam in the laboratory. The study successfully identified the change in the material parameters in the beam by using the deep-learning neural network model. Also, the method correctly predicted the acceleration response of the beam. The proposed approach can be extended to the structural health monitoring of actual bridges, and its sensitivity to damage can also be improved through optimization of the network training.

• Structural Engineering and Mechanics
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• v.50 no.1
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• pp.105-119
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• 2014

This study presents a steel container crane movement analysis and assessment based on structural health monitoring (SHM). The accelerometers are used to monitor the dynamic crane behavior and a 3-D finite element model (FEM) was designed to express the static displacement of the crane under the different load cases. The multi-input single-output nonlinear autoregressive neural network with external input (NNARX) model is used to identify the crane dynamic displacements. The FEM analysis and the identification model are used to investigate the safety and the vibration state of the crane in both time and frequency domains. Moreover, the SHM system is used based on the FEM analysis to assess the crane behavior. The analysis results indicate that: (1) the mean relative dynamic displacement can reveal the relative static movement of structures under environmental load; (2) the environmental load conditions clearly affect the crane deformations in different load cases; (3) the crane deformations are shown within the safe limits under different loads.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.5 s.248
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• pp.560-567
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• 2006

The exact load measurements for the mechanical parts of a wind turbine are important step both fur the evaluation of a specific wind turbine design and for a certification process. A common method for a mechanical load measurement is using a strain gauge sensing. Two main problems ought to be answered in order for this method to be applied to the wind turbine project. These are strain gauge calibration and non-contact signal transmission from the strain gauge output to a load monitoring system. This paper suggests reliable solutions fer these two problems. A Bluetooth, a short range wireless data communication technology, is used to solve the second problem. The first one, the strain gauge calibration methodology for a load measurement in a wind turbine application, is fully explained in this paper. Various mechanical loadings for a strain gauge calibration in a wind turbine load measurement are introduced and analyzed. Initial experimental results which are obtained from a 1 kW small size wind turbine are analyzed, and the uncertainty problem in estimating mechanical loads using a calibration matrix is fully covered in this paper.

• Steel and Composite Structures
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• v.46 no.6
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• pp.731-744
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• 2023

Vehicle load information is an important role in operating and ensuring the structural health of cable-stayed bridges. In this regard, an efficient and economic method is proposed for vehicle load detection based on the observed cable tension and vehicle position using a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), a robust program for modeling and considering both geometric and material nonlinearities of bridge structures subjected to vehicle load with low computational costs. With the superiority of GNN, the proposed model is demonstrated to precisely capture complex nonlinear correlations between the input features and vehicle load in the output. Four popular machine learning methods including artificial neural network (ANN), decision tree (DT), random forest (RF), and support vector machines (SVM) are refereed in a comparison. A case study of a cable-stayed bridge with the typical truck is considered to evaluate the model's performance. The results demonstrate that the GNN-based model provides high accuracy and efficiency in prediction with satisfactory correlation coefficients, efficient determination values, and very small errors; and is a novel approach for vehicle load detection with the input data of the existing monitoring system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.9
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• pp.1530-1539
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• 2010

Recent technology advancement related to computer & communication and measuring devices allows system operators to adopt more intelligent monitoring and control systems to their power systems in order to prevent massive system blackout. Among them, wide-area monitoring and control(WAMAC) system based on synchro-phasor technology has been widely applied to power systems for their own purposes. In this paper, the study on the development of load shedding scheme to improve voltage stability in KEPCO system is introduced. Summary of WAMAC technology being developed and applied in the world through extensive literature survey is proposed. And methodology to develop voltage stability index and multi-step load shedding scheme based on synchro-phasor data is also presented.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1221-1226
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• 2015

This paper proposes a multiple switch mode power supply (SMPS) system based on the wireless network which controls variable load. The system enables power supply of up to 600W using 200W SMPS as a unit module and provides a controlling function of output power based on variable load and a monitoring function based on wireless network. The controlling function for output power measures the variation of output power and facilitates efficient power supply by controlling output power based on the measured variation value. The monitoring function guarantees a stable power supply by observing the multiple SMPS system in real time via wireless network. The performance of the proposed system was examined by various experiments. In addition, it was verified through standardized test of Korea Testing Certification. The results were given and discussed.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.447-451
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• 2010

Monitoring and analyzing the status of smart objects, the ubiquitous smart monitoring system provides several information such as user-state index, service states and system operation among the services in real time. It also provides self-optimization and self-management for enhancing the performance of services. In order to expand the application scope of this real-time monitoring system, it is indispensible to process huge amount of stream data. In this paper, we propose a load balancing scheme to solve the overload of the monitoring agents. Our proposed scheme reduces deadline miss ratio of entire data by more than 80%.

• The Journal of Engineering Geology
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• v.14 no.4 s.41
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• pp.429-442
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• 2004

Landslide of the Whanpyeong Mountain which was occurred at Busan Metropolitan City in 1999 belongs to the category of plane failure. Automatic monitoring system to measure horizontal displacement, pore pressure change and load change has operating from reconstruction stage for evaluating rock slope stability (August, 2000$\~$Feburuary, 2002). As a result of the analysis on the monitoring performance data, it is suggested that infiltrated rain water from pound surface discharges rapidly through cut-slope because pressure head of water decreases rapidly after rainfall while rise of pore pressure is proportional to the amount of rain water. As a result of data analyses for inclinometers and load cells, it seems that slope is stablized be cause ground deformation is rarely detected. The areas especially similar to the study site where landslide is induced by heavy rain fall, change of pore pressure is rapidly analyzed using automatic monitoring system. Therefore, it is considered that automatic monitoring system is very effect for slope stability analysis on important cut-slopes.