• The Journal of Bigdata
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• v.8 no.1
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• pp.83-97
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• 2023

Project evaluation is the process of evaluating the progress and results of a project. Smart city projects can be divided into system components (infrastructure, services, platforms), or projects can run simultaneously for multiple services. In addition, services are developed and expanded through additional projects. In order to ensure that the smart city, which is composed of various projects, proceeds in accordance with the goals and strategies, periodic project evaluation is required during the project implementation process. The smart city project evaluation system proposed in this paper is designed to provide comprehensive and objective indicators by reflecting various factors that must be considered for projects occurring in all stages of planning, design, construction, and operation of smart cities. The indicators derived from the evaluation system can be used by decision makers to determine the direction of smart city project development. In addition, it is designed so that the performance of the project can be evaluated interim before the end of the project and the feedback obtained from it can be reflected. To introduce the application method of the smart city project evaluation system proposed in this study, the evaluation system developed in this study was applied to the smart city project case of Incheon Free Economic Zone (IFEZ). Based on the evaluation results, items that can maximize the improvement effect of each smart city project item were presented, and the direction of smart city project implementation was suggested. By utilizing a smart city project evaluation system that reflects the characteristics of smart city projects that are composed of multiple projects, comprehensive planning and management of smart city projects will be possible, and this study will serve as a reference for identifying priority improvement factors for projects.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.707-717
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• 2006

As an alternative to conventional prestressed concrete (PSC) girders, various types of PSC girders are either under development or have already been applied in bridge structures. Incrementally prestressed concrete girder is one of these newly developed girders. According to the design concept, these new types of PSC girders have the advantages of requiring less self-weight while having the capability of longer spans. However, the dynamic interaction between bridge superstructures and passing trains is one of the critical issues concerning these railway bridges designed with more flexibility. Therefore, it is very important to evaluate modal parameters of newly designed bridges before doing dynamic analyses. In the present paper, a 25 meters long full scale PSC girder was fabricated as a test specimen and modal testing was carried out to evaluate modal parameters including natural frequencies and modal damping ratios at every prestressing stage. During the modal testing, a digitally controlled vibration exciter as well as an impact hammer is applied, in order to obtain precise frequency response functions and the modal parameters are evaluated varying with construction stages. Prestressed force effects on changes of modal parameters are analyzed at every incremental prestressing stage. With the application of reliable properties from modal experiments, estimation of dynamic performances of PSC girder railway bridges can be obtained from various parametric studies on dynamic behavior under the passage of moving train. Dynamic displacements, impact factor, acceleration of the slab, end rotation of the girder, and other important dynamic performance parameters are checked with various speeds of the train.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.5D
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• pp.635-644
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• 2009

Image processing technique in the outdoor environment is very sensitive, and it tends to lose a lot of accuracy when it rapidly changes by outdoor environment. Therefore, in order to calculate accurate traffic information using the traffic monitoring system, we must resolve removing shadow in transition time, Distortion by the vehicle headlights at night, noise of rain, snow, and fog, and occlusion. In the research, we developed a system to calibrate the amount of traffic, speed, and time occupancy by using image processing technique in a variety of outdoor environments change. This system were tested under outdoor environments at the Gonjiam test site, which is managed by Korea Institute of Construction Technology (www.kict.re.kr) for testing performance. We evaluated the performance of traffic information, volume counts, speed, and occupancy time, with 4 lanes (2 lanes are upstream and the rests are downstream) from the 16th to 18th December, 2008. The evaluation method performed as based on the standard data is a radar detection compared to calculated data using image processing technique. The System evaluation results showed that the amount of traffic, speed, and time occupancy in period (day, night, sunrise, sunset) are approximately 92-97% accuracy when these data compared to the standard data.

• Geomechanics and Engineering
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• v.35 no.2
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• pp.121-133
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• 2023

The demand for cement and limestone crushed materials has increased many folds due to the tremendous increase in construction activities in Pakistan during the past few decades. The number of cement production industries has increased correspondingly, and so the rock-blasting operations at the limestone quarry sites. However, the safety procedures warranted at these sites for the blast-induced ground vibrations (BIGV) have not been adequately developed and/or implemented. Proper prediction and monitoring of BIGV are necessary to ensure the safety of structures in the vicinity of these quarry sites. In this paper, an attempt has been made to predict BIGV using artificial neural network (ANN) at three selected limestone quarries of Pakistan. The ANN has been developed in Python using Keras with sequential model and dense layers. The hyper parameters and neurons in each of the activation layers has been optimized using randomized and grid search method. The input parameters for the model include distance, a maximum charge per delay (MCPD), depth of hole, burden, spacing, and number of blast holes, whereas, peak particle velocity (PPV) is taken as the only output parameter. A total of 110 blast vibrations datasets were recorded from three different limestone quarries. The dataset has been divided into 85% for neural network training, and 15% for testing of the network. A five-layer ANN is trained with Rectified Linear Unit (ReLU) activation function, Adam optimization algorithm with a learning rate of 0.001, and batch size of 32 with the topology of 6-32-32-256-1. The blast datasets were utilized to compare the performance of ANN, multivariate regression analysis (MVRA), and empirical predictors. The performance was evaluated using the coefficient of determination (R²), mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and root mean squared error (RMSE)for predicted and measured PPV. To determine the relative influence of each parameter on the PPV, sensitivity analyses were performed for all input parameters. The analyses reveal that ANN performs superior than MVRA and other empirical predictors, andthat83% PPV is affected by distance and MCPD while hole depth, number of blast holes, burden and spacing contribute for the remaining 17%. This research provides valuable insights into improving safety measures and ensuring the structural integrity of buildings near limestone quarry sites.

• Information Systems Review
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• v.25 no.1
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• pp.129-143
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• 2023

In the era of big data, the field of artificial intelligence is showing remarkable growth, and in particular, the image classification learning methods by deep learning are becoming an important area. Various studies have been actively conducted to further improve the performance of CNNs, which have been widely used in image classification, among which a representative method is the Convolutional Recurrent Neural Network (CRNN) algorithm. The CRNN algorithm consists of a combination of CNN for image classification and RNNs for recognizing time series elements. However, since the inputs used in the RNN area of CRNN are the flatten values extracted by applying the convolution and pooling technique to the image, pixel values in the same phase in the image appear in different order. And this makes it difficult to properly learn the sequence of arrangements in the image intended by the RNN. Therefore, this study aims to improve image classification performance by proposing a novel hybrid method of CNN and RNN applying the concepts of encoder and decoder. In this study, the effectiveness of the new hybrid method was verified through various experiments. This study has academic implications in that it broadens the applicability of encoder and decoder concepts, and the proposed method has advantages in terms of model learning time and infrastructure construction costs as it does not significantly increase complexity compared to conventional hybrid methods. In addition, this study has practical implications in that it presents the possibility of improving the quality of services provided in various fields that require accurate image classification.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.891-899
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• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• Korean Journal of Materials Research
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• v.34 no.6
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• pp.291-302
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• 2024

In this paper, iron ore tailings (IOT) were separated from the tailings field and used to prepare cement stabilized macadam (CSM) with porous basalt aggregate. First, the basic properties of the raw materials were studied. Porous basalt was replaced by IOT at ratios of 0, 20 %, 40 %, 60 %, 80 %, and 100 % as fine aggregate to prepare CSM, and the effects of different cement dosage (4 %, 5 %, 6 %) on CSM performance were also investigated. CSM's durability and mechanical performance with ages of 7 d, 28 d, and 90 d were studied with the unconfined compression strength test, splitting tensile strength test, compressive modulus test and freeze-thaw test, respectively. The changes in Ca²⁺ content in CSM of different ages and different IOT ratios were analyzed by the ethylene diamine tetraacetic acid (EDTA) titration method, and the micro-morphology of CSM with different ages and different IOT replaced ratio were observed by scanning electron microscopy (SEM). It was found that with the same cement dosage, the strengths of the IOT-replaced CSM were weaker than that of the porous basalt aggregate at early stage, and the strength was highest at the replaced ratio of 60 %. With a cement dosage of 4 %, the unconfined compressive strength of CSM without IOT was increased by 6.78 % at ages from 28 d to 90 d, while the splitting tensile strength increased by 7.89 %. However, once the IOT replaced ratio reached 100 %, the values increased by about 76.24 % and 17.78 %, which was better than 0 % IOT. The CSM-IOT performed better than the porous basalt CSM at 90 d age. This means IOT can replace porous basalt fine aggregate as a pavement base.

• Journal of the Korea Concrete Institute
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• v.26 no.2
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• pp.151-157
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• 2014

Since recently developed Ultra-High-Performance-Concrete (UHPC) provides very high strength, stiffness, and durability, many studies have been made on the application of the UHPC to bridge decks. Due to high strength and stiffness of UHPC bridge deck, the structural contribution of top flange of steel girder composite to UHPC deck would be much lower than that of conventional concrete deck. At this point of view, this study proposes a inverted-T shaped steel girder composite to UHPC deck. This girder requires a new type of shear connector because conventional shear connectors are welded on top flange. This study also proposes three different types of shear connectors, and evaluate their ultimate strength via push-out static test. The first one is a stud shear connector welded directly to the web of the girder in the transverse direction. The second one is a puzzle-strip type shear connector developed by the European Commission, and the last one is the combination of the stud and the puzzle-strip shear connectors. Experimental results showed that the ultimate strength of the transverse stud was 26% larger than that given in the AASHTO LRFD Bridge Design Specifications, but a splitting crack observed in the UHPC deck was so severe that another measure needs to be developed to prevent the splitting crack. The ultimate strength of the puzzle-strip specimen was 40% larger than that evaluated by the equation of European Commission. The specimens combined with stud and puzzle-strip shear connectors provided less strength than arithmetical sum of those. Based on the experimental observations, there appears to be no advantage of combining transverse stud and puzzle-strip shear connectors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.50-58
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• 2014

Due to the cost burden of new construction, the necessity of repair and retrofitting of aged structures is sharply increasing as the domain of repair and retrofitting construction is expanding. Because of the necessity, new technologies for repair and retrofitting are continuously studied in Korea and foreign countries. Steel adhesive method, fiber reinforced plastic (FRP) surface adhesive method, and external prestressing method are used to perform the repair and retrofitting works in Korea. In order to consider a repair method using steel mesh reinforced cement mortar (SMCM), 3-point flexural member test was conducted considering repair area and layer number of SMCM. Five types of specimens including ordinary reinforced concrete (RC) specimen with dimensions of $1400{\times}500{\times}200$ (mm) were cast for testing the deflection measurement, a LVDT was installed at the top center of the specimens. Also, a steel strain gauge and a concrete strain gauge were placed at the center of the specimens. A steel strain gauge was also installed on the shear reinforcement. The 3 point flexural member test results showed that the maximum load of SMCM reinforced specimen was higher than that of basic RC specimen in all of the load-displacement curves. Also, the results showed that, when the whole lower part of the basic RC specimen was reinforced, the maximum load and strain were 1.18 and 1.37 times higher than that of the basic RC specimen, respectively. Each specimen showed a slightly different failure behavior where the difference of the results was caused by the difference in the adhesive level between SMCM and RC. Particularly, in SM-B1 specimen, SMCM spalled off during the experiment. This failure behavior showed that the adhesive performance for RC must be improved in order to utilize SMCM as repair and retrofitting material.

• Journal of the Korea Institute of Building Construction
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• v.9 no.4
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• pp.63-73
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• 2009

Recently, the use of lightweight panels in building structures has been increasing. Of the various lightweight panel types, styrofoam sandwich panels are inexpensive and are excellent in terms of their insulation capacity and their constructability. However, sandwich panels that include organic material are quite vulnerable to fire, and thus can numerous casualties in the event of a fire due to the lack of time to vacate and their emission of poisonous gas. On the other hand, lightweight foamed concrete is excellent, both in terms of its insulation ability and its fire resistance, due to its Inner pores. The properties of lightweight concrete is influenced by foaming agent type. Accordingly, this study investigates the insulation properties by foaming agent type, to evaluate the possibility of using light-weight foamed concrete instead of styrene foam. Our research found thatnon-heating zone temperature of lightweight foamed concrete using AP (Aluminum Powder) and FP (animal protein foaming agent) are lower than that of light-weight foamed concrete using AES (alkyl ether lactic acid ester). Lightweight foamed concrete using AES and FP satisfied fire performance requirements of two hours at a foam ratio 50, 100. Lightweight foamed concrete using AP satisfied fire performance requirements of two hours at AP ratio 0.1, 0.15. The insulation properties were better in closed pore foamed concrete by made AP, FP than with open pore foamed concrete made using AES.