• The Journal of Engineering Geology
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• v.20 no.3
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• pp.297-310
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• 2010

This study represents basic research into the utilization of mixed ash (fly ash and bottom ash) from the ash pond of the Taean Thermal Power Plant as a construction material. We conducted physical and mechanical experiments on the mixed ash and examined its engineering characteristics in terms of its use as a material for road landfill and structure backfill. We evaluated the physical and chemical characteristics of the ash by performing tests to determine specific gravity, maximum and minimum density, liquid limit and plastic limit, grain size distribution, composition (by X-ray diffraction), and loss on ignition. We also evaluated the mechanical characteristics by testing for permeability, compaction, CBR, and tri-axial compression. The experiments on the mixed ash yielded a specific gravity of 2.18-2.20, dry density of $9.38-13.32\;kN/m^3$, modified CBR of 16.5%-21%, permeability coefficient of 1.32 to $1.89-10^{-4}cm/sec$, and drained friction angle of $36.43^{\circ}-41.39^{\circ}$. The physical and mechanical properties of the mixed ash do not meet the quality standards stipulated for road landfill and structure backfill materials. Mixed ash with a high content of fly ash failed to meet some of the quality standards. Therefore, in order to utilize the mixed ash as a material for road landfill and structure backfill, it is necessary to improve its properties by mixing with bottom ash.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.43-57
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• 2012

To enhance the competitive advantage in a constantly changing business environment, an enterprise management must make the right decision in many business activities based on both internal and external information. Thus, providing accurate information plays a prominent role in management's decision making. Intuitively, historical data can provide a feasible estimate through the forecasting models. Therefore, if the service department can estimate the service quantity for the next period, the service department can then effectively control the inventory of service related resources such as human, parts, and other facilities. In addition, the production department can make load map for improving its product quality. Therefore, obtaining an accurate service forecast most likely appears to be critical to manufacturing companies. Numerous investigations addressing this problem have generally employed statistical methods, such as regression or autoregressive and moving average simulation. However, these methods are only efficient for data with are seasonal or cyclical. If the data are influenced by the special characteristics of product, they are not feasible. In our research, we propose a forecasting framework that predicts service demand of manufacturing organization by combining Case-based reasoning (CBR) and leveraging an unsupervised artificial neural network based clustering analysis (i.e., Self-Organizing Maps; SOM). We believe that this is one of the first attempts at applying unsupervised artificial neural network-based machine-learning techniques in the service forecasting domain. Our proposed approach has several appealing features : (1) We applied CBR and SOM in a new forecasting domain such as service demand forecasting. (2) We proposed our combined approach between CBR and SOM in order to overcome limitations of traditional statistical forecasting methods and We have developed a service forecasting tool based on the proposed approach using an unsupervised artificial neural network and Case-based reasoning. In this research, we conducted an empirical study on a real digital TV manufacturer (i.e., Company A). In addition, we have empirically evaluated the proposed approach and tool using real sales and service related data from digital TV manufacturer. In our empirical experiments, we intend to explore the performance of our proposed service forecasting framework when compared to the performances predicted by other two service forecasting methods; one is traditional CBR based forecasting model and the other is the existing service forecasting model used by Company A. We ran each service forecasting 144 times; each time, input data were randomly sampled for each service forecasting framework. To evaluate accuracy of forecasting results, we used Mean Absolute Percentage Error (MAPE) as primary performance measure in our experiments. We conducted one-way ANOVA test with the 144 measurements of MAPE for three different service forecasting approaches. For example, the F-ratio of MAPE for three different service forecasting approaches is 67.25 and the p-value is 0.000. This means that the difference between the MAPE of the three different service forecasting approaches is significant at the level of 0.000. Since there is a significant difference among the different service forecasting approaches, we conducted Tukey's HSD post hoc test to determine exactly which means of MAPE are significantly different from which other ones. In terms of MAPE, Tukey's HSD post hoc test grouped the three different service forecasting approaches into three different subsets in the following order: our proposed approach > traditional CBR-based service forecasting approach > the existing forecasting approach used by Company A. Consequently, our empirical experiments show that our proposed approach outperformed the traditional CBR based forecasting model and the existing service forecasting model used by Company A. The rest of this paper is organized as follows. Section 2 provides some research background information such as summary of CBR and SOM. Section 3 presents a hybrid service forecasting framework based on Case-based Reasoning and Self-Organizing Maps, while the empirical evaluation results are summarized in Section 4. Conclusion and future research directions are finally discussed in Section 5.

• Journal of Korean Society of Water and Wastewater
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• v.26 no.6
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• pp.797-805
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• 2012

Contribution of immobilized media with bacteria to the odor removal was evaluated in a lab scale bio-filter compared to that with sponge or ceramic media without the immobilized bacteria. Candida tropicalis for volatile organic compounds and ammonium oxidizing bacteria (AOB) for inorganic compounds were used as seeds in lab-scale bio-reactors. Three different type of media in the bio-reactors that immobilized bioreactor (IBR), sponge bioreactor (SBR), and ceramic bioreactor (CBR) were examined, respectively. An empty bed contact time (EBCT) of the bio-filters was fixed as 60 seconds, and the inlet concentration of toluene was changed from 20 ppm to 200 ppm to observe the removal efficiency depending on the concentrations. As a result, the maximum elimination capacities of IBR, SBR, and CBR were 166 $g/m^3/hr$, 138 $g/m^3/hr$, and 138 $g/m^3/hr$, respectively. In addition, toluene as an organic compound and ammonia as an inorganic compound were applied together with different inlet concentrations varied from 80 ppm to 250 ppm of toluene and from 2.5 ppm to 40 ppm of ammonia. The toluene maximum elimination capacities in IBR, SBR, and CBR were 97.4 $g/m^3/hr$, 59.5 $g/m^3/hr$, and 81.9 $g/m^3/hr$, respectively. The ammonia maximum elimination capacities were reached as 7.2 $g/m^3/hr$ in IBR, 6.6 $g/m^3/hr$ in SBR, and 7.0 $g/m^3/hr$ in CBR.

• Journal of the Korean GEO-environmental Society
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• v.4 no.1
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• pp.29-39
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• 2003

The major objectives of this study were to investigate the physical characteristics of selected refuse landfill waste soils which are excepted general waste materials and assessed the possibility of recycling for road construction or embankment materials. The old landfill site which is selected for this study is located at Youngyang in Kyungsangpukdo and it had been dumped and closed for 16-25 years. Therefore, the selected landfill waste soil became to geotechnical engineering characteristics when the closed landfill site is reused for road embankment materials. It was found that it would be better to use the selected waste soil mixed with the ordinary soil.

• Journal of the Korean Geotechnical Society
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• v.20 no.3
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• pp.75-83
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• 2004

The Indiana Department of Transportation (INDOT) has permitted the use of Lime Kiln Dust (LKD) as a low-cost construction material in creating a workable platform for soil modification (not for soil stabilization) since the early 1990s on selected projects. However, the enhanced strength of soils with LKD has not been accounted for in the subgrade stability calculations in the design process. This study was initiated to evaluate how the lime kiln dust is a comparable material to hydrated lime. A series of laboratory tests were performed to assess the mechanical benefits of lime kiln dust in combination with various predominant fine grained soils encountered in the State of Indiana, such as A-4, A-6 and A-7-6. In the course of this study, several tests such as the Atterberg limits, standard Proctor, unconfined compression, CBR, volume stability, and resilient modulus were performed. As a result, mixtures of fine grained soils with 5% lime or 5% LKD substantially improve unconfined compressive strength up to 60% - 400%. CBR values for treated soils are in the range of 25 to 70 while those for untreated soils range from 3 to 18. In general, significant increase in resilient moduli of the soils treated with lime and LKD was observed. This indicates that lime kiln dust may be a viable, cost effective alternative to hydrated lime in enhancing the strength of fine grained soils.

• Proceedings of the Materials Research Society of Korea Conference
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• 2011.10a
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• pp.28.1-28.1
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• 2011

We deposited the carbon films on Ni substrates by thermal atomic layer deposition (th-ALD), for the first time, using carbon tetrabromide ($CBr_4$) precursors and H2 reactants at two different temperatures (573 K and 673 K). Morphology of carbon films was characterized by scanning electron microscopy (SEM). The carbon films having amorphous carbon structures were analyzed by X-ray photoemission spectroscopy (XPS) and Raman spectroscopy. As the working temperature was increased from 573 K to 673 K, the intensity of C1s spectra was increased while that of O1s core spectra was reduced. That is, the purity of carbon films containing bromine (Br) atoms was increased. Also, the thin amorphous carbon films (ALD 3 cycle) were transformed to multilayer graphene segregated on Ni layer, through the post-annealing and cooling process.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.29-37
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• 2024

In this study, recycled concrete aggregates (RCA) were treated in a 5-kg-scale prototype reactor with carbon dioxide (CO₂) to enhance their material quality and geotechnical performance. The aggregate crushing value (ACV) and California bearing ratio (CBR) were measured on untreated RCAs and CO₂-treated RCAs. After CO₂ treatment, the ACV decreased from 35.6% to 33.2%, and the CBR increased from 97.5% to 102.4%. The CO₂ treatment caused a reduction of fine particle generation and an increase in bearing capacity through carbonation. When CO₂ treatment was performed with mechanical agitation, which provided additional enhancement in mechanical quality, the ACV was reduced further to 30.3%, and the CBR increased to 137.7%. If upscaled effectively, the proposed CO₂ treatment technique would be an effective method to reduce carbon emissions in construction industries.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.42-52
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• 2013

The aim of this study is to present a prediction model of construction cost for a bridge that has a high reliability using historical data from the planning phase based on a CBR (Case-Based Reasoning) method in order to overcome limitations of existing construction cost prediction methods, which is linearly estimated. To do this, a reasoning model of bridge construction cost by a spreadsheet template was suggested using complexly both CBR and GA (Genetic Algorithm). Besides, this study performed a case study to verify the suggested cost reasoning model for bridge construction projects. Measuring efficiency for a result of the case study was 8.69% on average. Since accuracy of the suggested prediction cost is relatively high compared to the other analysis methods for a prediction of construction cost, reliability of the suggested model was secured. In the case that information for detailed specifications of each bridge type in an initial design phase is difficult to be collected, the suggested model is able to predict the bridge construction cost within the minimized measuring efficiency with only the representative specifications for bridges as an improved correction method. Therefore, it is expected that the model will be used to estimate a reasonable construction cost for a bridge project.

• Geotechnical Engineering
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• v.6 no.1
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• pp.35-42
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• 1990

Over the years, most pavement designs based on soil strength and permanent strain are almost independent of soil elasticity. However, it was found that plasticity and elasticity of soil have both effected on the failure of pavement structures. The elasticity of soil, hence, using the resilient modulus is reflected for recent pavement design. Although the current AASHTO specifications(1986) for pavement design had changed the soil support value to the resilient modulus, triaxial devices conducting the resilient modulus test have not been fully equipped in a great majority of laboratories. Thus, in the present work, such a resilient modulus is usually derived(from CBR, K values, etc.) by estimating equations. The purpose of this study is to evaluate the resilient modulus of weathered granite soils sampled from 4 points of the central region of Korea by means of AASHTO T 274-82. According to this, some empirical equations for predicting that of the weathered granite soil are proposed and then, the relationship to convert CBR into the resilient modulus is developed.

• Asia pacific journal of information systems
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• v.16 no.4
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• pp.217-238
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• 2006

In this study, we propose a virtual community recommendation model based on user behavioral models. It is designed to recommend optimal virtual communities for an active user by applying case-based reasoning (CBR) using behavioral factors suggested in the technology acceptance model (TAM) and its extensions. Also, it is designed to filter its case-base by considering the user's needs type before applying CBR. To test the usefulness of our model, we conduct two-step validation - experimental validation for the collected data, and survey validation for investigating the actual satisfaction level. Experimental results show that our model presents effective recommendation results in an efficient way. In addition, they also show that the information on the user's needs type may generate opportunities for cross-selling other commercial items.