• Structural Engineering and Mechanics
• /
• v.29 no.2
• /
• pp.135-154
• /
• 2008

The moving vehicle loads on a bridge deck is one of the most important live loads of bridges. They should be understood, monitored and controlled before the bridge design as well as when the bridge is open for traffic. A MOM-based algorithm (MOMA) is proposed for identifying the timevarying moving vehicle loads from the responses of bridge deck in this paper. It aims at an acceptable solution to the ill-conditioning problem that often exists in the inverse problem of moving force identification. The moving vehicle loads are described as a combination of whole basis functions, such as orthogonal Legendre polynomials or Fourier series, and further estimated by solving the new system equations developed with the basis functions. A number of responses have been combined, some numerical simulations on single axle, two axle and multiple-axle loads, being either constant or timevarying, have been carried out and compared with the existing time domain method (TDM) in this paper. The illustrated results show that the MOMA has higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-conditioning cases to some extent when it is used to identify the moving force from bridge responses.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.45 no.5
• /
• pp.38-47
• /
• 2008

The most commonly used location recognition system is the GPS-based approach. However, the GPS is inefficient for an indoor or urban area where high buildings shield the satellite signals. To overcome this problem, this paper propose the indoor positioning method using wavelet and neural network. The basic idea of proposed method is estimated the location using the received signal strength from wireless APs installed in the indoor environment. Because of the received signal strength of wireless radio signal is fluctuated by the environment factors, a feature that is strength of signal noise and error and express the time and frequency domain is need. Therefore, this paper is used the wavelet coefficient as the feature. And the neural network is used for estimate the location. The experiment results indicate 94.6% an location recognition rate.

• Korean Journal of Geomatics
• /
• v.2 no.1
• /
• pp.37-46
• /
• 2002

Accurate classification of water area is an preliminary step to accurately analyze the flooded area and damages caused by flood. This step is especially useful for monitoring the region where annually repeating flood is a problem. The accurate estimation of flooded area can ultimately be utilized as a primary source of information for the policy decision. Although SAR (Synthetic Aperture Radar) imagery with its own energy source is sensitive to the water area, its shadow effect similar to the reflectance signature of the water area should be carefully checked before accurate classification. Especially when we want to identify small flood area with mountainous environment, the step for removing shadow effect turns out to be essential in order to accurately classify the water area from the SAR imagery. In this paper, the flood area was classified and monitored using multi-temporal RADARSAT SAR images of Ok-Chun and Bo-Eun located in Chung-Book Province taken in 12th (during the flood) and 19th (after the flood) of August, 1998. We applied several steps of geometric and radiometric calculations to the SAR imagery. First we reduced the speckle noise of two SAR images and then calculated the radar backscattering coefficient $(\sigma^0)$. After that we performed the ortho-rectification via satellite orbit modeling developed in this study using the ephemeris information of the satellite images and ground control points. We also corrected radiometric distortion caused by the terrain relief. Finally, the water area was identified from two images and the flood area is calculated accordingly. The identified flood area is analyzed by overlapping with the existing land use map.

• Smart Structures and Systems
• /
• v.19 no.1
• /
• pp.1-10
• /
• 2017

The operation of subway trains induces secondary structure-borne vibrations in the nearby underground spaces. The vibration, along with the associated noise, can cause annoyance and adverse physical, physiological, and psychological effects on humans in dense urban environments. Traditional tethered instruments restrict the rapid measurement and assessment on such vibration effect. This paper presents a novel approach for Wireless Smart Sensor (WSS)-based autonomous evaluation system for the subway train-induced vibrations. The system was implemented on a MEMSIC's Imote2 platform, using a SHM-H high-sensitivity accelerometer board stacked on top. A new embedded application VibrationLevelCalculation, which determines the International Organization for Standardization defined weighted acceleration level, was added into the Illinois Structural Health Monitoring Project Service Toolsuite. The system was verified in a large underground space, where a nearby subway station is a good source of ground excitation caused by the running subway trains. Using an on-board processor, each sensor calculated the distribution of vibration levels within the testing zone, and sent the distribution of vibration level by radio to display it on the central server. Also, the raw time-histories and frequency spectrum were retrieved from the WSS leaf nodes. Subsequently, spectral vibration levels in the one-third octave band, characterizing the vibrating influence of different frequency components on human bodies, was also calculated from each sensor node. Experimental validation demonstrates that the proposed system is efficient for autonomously evaluating the subway train-induced ambient vibration of underground spaces, and the system holds the potential of greatly reducing the laboring of dynamic field testing.

• Explosives and Blasting
• /
• v.39 no.2
• /
• pp.15-26
• /
• 2021

Domestic electronic detonators are used widely in many quarry and construction sites since its launch at 2013. In the case of SOC projects conducted in the city, most of them are designed in high-depth to reduce complaints. The high-depth excavation needs a long construction period and huge cost for building shaft and ventilation hole. Mechanical excavation method is applied when safety things are located nearby the site. Solidity of rock and machine's performance affect on the method's efficiency. So as the efficiency is getting lower, the construction period is extended, and the cost is increases as well. This case study is about changing the machine excavation method to the blasting method which is electronic detonator applied at the shaft construction site in the city. This is an example of using electronic detonators on the construction site in reducing blast-noise and vibration while meeting environmental regulatory standards.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.53-60
• /
• 2021

High voltage CV cables have been widely installed underground due to their convenience and urban aesthetics. However, cable accidents have occurred frequently owing to poor construction and natural degradations. This paper proposes the method to measure the multi parameter measurement for optimum diagnostics of high voltage cable connection parts and verifies its technical usefulness. This measurement is intended to diagnose degradations of cable connection parts by using simultaneous vibration and thermography as well as partial discharge(PD). The experiment in a shielded laboratory was carried out to verify the usefulness of the multi parameter measurement. The experiment defined the degradation of the cable connection part as 12 types, and produced each degradation sample. As a result of experiment, it was possible to check the correlation of vibration signals with regard to progress in some defects. In the case of thermography, the coherence with regard to the progress of some defects was found. We figure that the proposed method would be useful also in the noise environment.

• Explosives and Blasting
• /
• v.40 no.1
• /
• pp.29-38
• /
• 2022

Electronic detonators are now widely used in various construction sites and quarry mines. Including the sites where safety-thing is located nearby, Cases of using electronic detonators are increasing to maximize operational efficiency by improving blast fragmentation or reducing the cost of secondary blasting. This case study is about applying for electronic detonators on zone 00 construction site, which is the part of urban area metropolitan express rail A line project. Although the project was initially planned to utilize non-electric detonators, Electronic detonators are considered as the solution not only for safe and fast excavation, but also to minimize civil complaint and the damage of safety-thing. By applying electronic detonators, we were able to satisfy environmental regulations standards and prevent nearby safety-thing from getting damaged.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.25 no.3
• /
• pp.221-243
• /
• 2023

The use of roadheaders has been increasing to mitigate the problems of noise and vibration during tunneling operations in urban area. Since lack of experience of roadheader for hard rock, the selection of appropriate machines and the evaluation of cutting rates have been challenging. Currently, empirical models developed overseas are commonly used to evaluate cutting rates, but their effectiveness has not been verified for domestic rocks. In this paper, a comprehensive literature review was conducted to assess the rock cutting force, cutterhead capacity, and cutting rate to select the appropriate machine and evaluate its performance. The cutterhead capacity was reviewed based on the literature results for the site. Furthermore, a new empirical model and simplified method for predicting cutting rates were proposed through data analysis in relation to operation time and rock strength, and compared with those of the conventional model from the manufacturer. The results show good agreement for high strength range upper 80 MPa of uniaxial compressive strength.

• The Journal of Bigdata
• /
• v.6 no.2
• /
• pp.39-49
• /
• 2021

Recently, large and diverse weather data are being collected by sensors from various sources. Efforts to predict the concentration of fine dust through machine learning are being made everywhere, and this study intends to compare PM10 and PM2.5 prediction models using data from 840 outdoor air meters installed throughout the city. Information can be provided in real time by predicting the concentration of fine dust after 5 minutes, and can be the basis for model development after 10 minutes, 30 minutes, and 1 hour. Data preprocessing was performed, such as noise removal and missing value replacement, and a derived variable that considers temporal and spatial variables was created. The parameters of the model were selected through the response surface method. XGBoost, Random Forest, and Deep Learning (Multilayer Perceptron) are used as predictive models to check the difference between fine dust concentration and predicted values, and to compare the performance between models.

• Smart Structures and Systems
• /
• v.31 no.1
• /
• pp.57-68
• /
• 2023

Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.