• 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.

• Journal of the Korea Institute of Building Construction
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• v.4 no.1
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• pp.105-110
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• 2004

Resource procurement is an important management area because cost of resource covers 40％ of total construction project cost and resource delivery has direct relationship with project performance. Integration of cost provides various potentials for effective and efficient project control. This study investigates the usefulness of time in resource procurement management focused on materials. These days, construction projects have characterized manufacture because of industrialization and component. Therefore, application of systematic resource planning has been requested in the construction. There are many companies conducting procurement of resource on the web by applying MRP, ERP etc. in the construction. However, in applying them in the construction yet, there is obstruction. MRP has the character doing its function under accurate cost prediction of resource. But prediction of resource is difficult in industry mechanism of the construction. If accurate cost prediction of resource is possible in the construction, it will be expected to reduce cost of procurement of resource substantially by applying successful resource planning model in the manufacture. On the basis of recent current, the purpose of study is to present procurement of resource system that period observance of construction and minimization of stock is possible by reflecting accurate lead-time to apply proactive thought to be able to cope with alteration of construction schedule efficiently in analyzing resource planning of the construction site.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.653-659
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• 2018

The accurate prediction of construction cost is a key factor in a project's success. However, it is hard to predict the construction costs in the planning stages rapidly and precisely when drawings, specifications, construction cost calculation statements are incomplete, among other factors. Accurate construction-cost prediction in the planning stage of a project is also important for project feasibility studies and successful completion. Therefore, various techniques have been applied to accurately predict construction costs at an early stage when project information is limited. There are many factors that affect the construction cost prediction. This paper presents a construction-cost prediction method as multiple regression model with seven construction factors as independent variables. The method was used to predict the construction cost of a local housing union project, and the error rate was 4.87%. It is not possible to compare the cost of the project at the planning stage of the local housing union project, but it has high prediction accuracy compared to the unit price of an existing unit area. It is likely to be applied in construction-cost calculation work and to contribute to the establishment of the budget for the local housing union project.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.137-142
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• 2006

Cost and duration is important factors which directly affect profit therefore must be forecasted correctly to accomplish success of projects. So construction company uses EVMS(Earned Value Management System) to forecast final cost and duration. But previous forecasting model has low accuracy because of its linear forecasting method and can't reflect characteristic of company and project and changes as each progress. This paper presents cost and duration forecasting model using the slope prediction of cost and duration as each progress to reflect the various characteristics of construction industry. EVMS data of 23 road construction projects was used to make up regression analysis equation of slope forecasting model.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.419-422
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• 2002

The features of modem construction industry can be summarized as specialization, complexity, and large-scale. Therefore, increasing uncertainty of construction project can not be effectively dealt with traditional method used for construction cost management. Cost overrun affects on successful execution of managing construction project in a negative way. Therefore, accurate estimation is a priori for effective cost management. First, this work analyzes the previous cost estimation model for the effective cost management. Then, a standard structure required for developing the cost estimation model for site development was presented. In addition, the cost estimation model which can be used in planning and design phases was introduced by analyzing real site development projects.

• Journal of the Korea Institute of Building Construction
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• v.23 no.1
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• pp.103-111
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• 2023

Accurate cost estimation in the early stages of a construction project is critical to the successful execution of the project. In this study, an ANFIS model was presented to predict construction costs in the early stages of a construction project. To increase the usability of the model, open construction cost data was used, and a model using limited information in the early stage of the project was presented. We analyzed existing studies related to ANFIS to identify recent trends, and after reviewing the basic structure of ANFIS, presented an ANFIS model for predicting conceptual construction costs. The variation in prediction performance depending on the type and number of membership functions of the ANFIS model was analyzed, the model with the best performance was presented, and the prediction accuracy of representative machine learning models was compared and analyzed. Through comparing the ANFIS model with other machine learning models, it was found to show equal or better performance, and it is concluded that it can be applied to predicting construction costs in the early stage of a project.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2012.11a
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• pp.153-154
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• 2012

The accuracy of cost estimation at an early stage in school building project is one of the critical factors for successful completion. So many method and techniques have developed that can estimate construction cost using limited information available in the early stage. Among the techniques, Support Vector Machine(SVM) has received attention in various field due to its excellent capacity for self-learning and generalization performance. Therefore, the purpose of this study is to verify the applicability of cost prediction model based on SVM in school building project at the early stage. Data used in this study are 139 school building cost constructed from 2004 to 2007 in Gyeonggi-Do. And prediction error rate of 7.48% in support vector machine is obtained. So the results showed applicability of using SVM model for predicting construction cost of school building projects.

• Korean Journal of Construction Engineering and Management
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• v.10 no.3
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• pp.139-147
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• 2009

Accurate construction cost estimation in the initial stage of building project plays a key role for project success and for mitigation of disputes. Total construction cost(TCC) estimation of apartment projects in Vietnam has become more important because those projects increasingly rise in quantity with the urbanization and population growth. This paper presents the application of artificial neural networks(ANNs) in estimating TCC of apartment projects. Ninety-one questionnaires were collected to identify input variables. Fourteen data sets of completed apartment projects were obtained and processed for training and generalizing the neural network(NN). MATLAB software was used to train the NN. A program was constructed using Visual C++ in order to apply the neural network to realistic projects. The results suggest that this model is reasonable in predicting TCCs for apartment projects and reinforce the reliability of using neural networks to cost models. Although the proposed model is not validated in a rigorous way, the ANN-based model may be useful for both practitioners and researchers. It facilitates systematic predictions in early phases of construction projects. Practitioners are more proactive in estimating construction costs and making consistent decisions in initial phases of apartment projects. Researchers should benefit from exploring insights into its implementation in the real world. The findings are useful not only to researchers and practitioners in the Vietnam Construction Industry(VCI) but also to participants in other developing countries in South East Asia. Since Korea has emerged as the first largest foreign investor in Vietnam, the results of this study may be also useful to participants in Korea.

• International conference on construction engineering and project management
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• 2005.10a
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• pp.935-940
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• 2005

This paper presents the results of a statistical analysis using historical data of cost contingency. As a result, a model that predicts and estimates an accurate cost contingency value using the least squares estimation method was developed. Data such as original contract amounts, estimated contingency amounts set by maximum funding limits, and actual contingency amounts, were collected and used for model development. The more effective prediction model was selected from the two developed models based on its prediction capability. The model would help guide project managers making financial decisions when the determination of the cost contingency amounts for multiple projects is necessary.

• Journal of Construction Engineering and Project Management
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• v.3 no.2
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• pp.13-20
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• 2013

Construction industry has become a larger and highly competitive industry. A successful construction project cannot be achieved only by efficient and fast construction techniques but also reasonable material cost and adequate transferring time of materials to installation. The steel industry in East Asia has become the mainstream in overall steel industries in over the world during the middle of the 21st century. China, Japan and Korea has been the main exportation countries. However, even though the international economic failure, China has increased the exportation amount and became an only exporting country which must be considered a serious problem regarding competitiveness in the international steel exportation industry. Thus, this study analyses the factors affecting the supply and demand amount of reinforced bars in the domestic field and moreover suggesting a unit cost prediction model using the System Dynamics simulation methodology, one of powerful prediction tools using cause-effect relationships. It is expected that this study contributes to the domestic steel industry growth in competitiveness in the international industry. In addition, the methodology used in this paper presents the frameworks for appropriate tools for market trend analysis and prediction of other markets.