Choongwan Koo

Economic and Environmental Evaluation Model for Selecting the Optimum Design of Green Roof Systems in Elementary Schools

Green-roof systems offer various benefits to man and nature, such as establishing ecological environments, improving landscape and air quality, and offering pleasant living environments. This study aimed to develop an optimal-scenario selection model that considers both the economic and the environmental effect in applying GRSs to educational facilities. The following process was carried out: (i) 15 GRSs scenarios were established by combining three soil and five plant types and (ii) the results of the life cycle CO(2) analyses with the GRSs scenarios were converted to an economic value using certified emission reductions (CERs) carbon credits. Life cycle cost (LCC) analyses were performed based on these results. The results showed that when considering only the currently realized economic value, the conventional roof system is superior to the GRSs. However, the LCC analysis that included the environmental value, revealed that compared to the conventional roof system, the following six GRSs scenarios are superior (cost reduction; reduction ratio; in descending order): scenarios 13 (

Estimation of the monthly average daily solar radiation using geographic information system and advanced case-based reasoning.

195,229; 11.0%), 3 (

The development of a construction cost prediction model with improved prediction capacity using the advanced CBR approach

188,178; 10.6%), 8 (

A decision support model for improving a multi-family housing complex based on CO2 emission from electricity consumption

181,558; 10.3%), 12 (

An integrated multi-objective optimization model for solving the construction time-cost trade-off problem

130,464; 7.4%), 2 (

A decision support system for determining the optimal size of a new expressway service area

124,566; 7.0%), and 7 (

Estimating the Loss Ratio of Solar Photovoltaic Electricity Generation through Stochastic Analysis

113,931; 6.4%). Although the effect is relatively small in terms of cost reduction, environmental value attributes cannot be ignored in terms of the reduction ratio.

Framework for the validation of simulation-based productivity analysis: focused on curtain wall construction process

The photovoltaic (PV) system is considered an unlimited source of clean energy, whose amount of electricity generation changes according to the monthly average daily solar radiation (MADSR). It is revealed that the MADSR distribution in South Korea has very diverse patterns due to the countrys climatic and geographical characteristics. This study aimed to develop a MADSR estimation model for the location without the measured MADSR data, using an advanced case based reasoning (CBR) model, which is a hybrid methodology combining CBR with artificial neural network, multiregression analysis, and genetic algorithm. The average prediction accuracy of the advanced CBR model was very high at 95.69%, and the standard deviation of the prediction accuracy was 3.67%, showing a significant improvement in prediction accuracy and consistency. A case study was conducted to verify the proposed model. The proposed model could be useful for owner or construction manager in charge of determining whether or not to introduce the PV system and where to install it. Also, it would benefit contractors in a competitive bidding process to accurately estimate the electricity generation of the PV system in advance and to conduct an economic and environmental feasibility study from the life cycle perspective.

Prediction Model for Gas-Energy Consumption using Ontology-based Breakdown Structure of Multi-Family Housing Complex

Decision-making in the early stages of a construction project will have a significant impact on the project. Limited and uncertain information, however, makes it difficult to accurately predict constriction costs. To solve this problem, this study developed the advanced case-based reasoning (CBR) model with 101 cases of multi-family housing projects. The advanced CBR model was developed to integrate the advantages of prediction methodologies such as CBR, multiple regression analysis (MRA), and artificial neural networks (ANN), and the optimization process using a genetic algorithm. This study defined four optimization parameters, as follows: (i) the minimum criterion for scoring the attribute similarity, (ii) the range of attribute weight, (iii) the range of case selection and (iv) the tolerance range of cross range between MRA and ANN. Since the system was developed using the Microsoft-Excel-based Visual Basic Application (VBA) for ease of use, it is expected that the model supports the stakeholders in charge of predicting and managing a construction cost in the early stages of a construction project to get more accurate result from historical cases as a reference.

Dynamic analysis of the urban-based low-carbon policy using system dynamics: Focused on housing and green space

The number of deteriorated multi-family housing complexes in South Korea continues to rise, and consequently their electricity consumption is also increasing. This needs to be addressed as part of the nations efforts to reduce energy consumption. The objective of this research was to develop a decision support model for determining the need to improve multi-family housing complexes. In this research, 1664 cases located in Seoul were selected for model development. The research team collected the characteristics and electricity energy consumption data of these projects in 2009-2010. The following were carried out in this research: (i) using the Decision Tree, multi-family housing complexes were clustered based on their electricity energy consumption; (ii) using Case-Based Reasoning, similar cases were retrieved from the same cluster; and (iii) using a combination of Multiple Regression Analysis, Artificial Neural Network, and Genetic Algorithm, the prediction performance of the developed model was improved. The results of this research can be used as follows: (i) as basic research data for continuously managing several energy consumption data of multi-family housing complexes; (ii) as advanced research data for predicting energy consumption based on the project characteristics; (iii) as practical research data for selecting the most optimal multi-family housing complex with the most potential in terms of energy savings; and (iv) as consistent and objective criteria for incentives and penalties.