• International conference on construction engineering and project management
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• 2011.02a
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• pp.534-541
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• 2011

Risk evaluation approaches for bidding on international construction projects are typically partitioned into three stages: country selection, project classification, and bid-cost evaluation. However, previous studies are frequently under attack in that they have several crucial limitations: 1) a dearth of studies about country selection risk tailored for the overseas construction market at a corporate level; 2) no consideration of uncertainties for input variable per se; 3) less probabilistic approaches in estimating a range of cost variance; and 4) less inclusion of covariance impacts. This study thus suggests a three-staged risk evaluation model to resolve these inherent problems. In the first stage, a country portfolio model that maximizes the expected construction market growth rate and profit rate while decreasing market uncertainty is formulated using multi-objective genetic analysis. Following this, probabilistic approaches for screening bad projects are suggested through applying various data mining methods such as discriminant logistic regression, neural network, C5.0, and support vector machine. For the last stage, the cost overrun prediction model is simulated for determining a reasonable bid cost, while considering non-parametric distribution, effects of systematic risks, and the firm's specific capability accrued in a given country. Through the three consecutive models, this study verifies that international construction risk can be allocated, reduced, and projected to some degree, thereby contributing to sustaining stable profits and revenues in both the short-term and the long-term perspective.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.58-65
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• 2011

Construction Productivity is one of the most important elements in construction management. It is used in construction process scheduling and cost management, which are significant sector in construction management. It is important to make appropriate schedule and monitor how works are done within schedule. But construction project contains uncertainty and inexactitude, modifying construction schedule is being an issue to manage construction works well. Even though prediction and monitoring of productivity can be principal activity, it is hard to predict productivity with manager's experience and a standard of estimate. A large number of factors influencing productivity, such as drawing, construction method, weather, labor, material, equipment, etc. But current calculation of productivity depends on empirical probability, not consider difference of each influencing factor. In this research, the aim is to present a productivity predicting regression model of form work, which includes effectiveness of influences factors. 5 variables existed inside form work are selected by interview and site research based on literature review of existed various productivity influencing factors. The effectiveness and correlation of productivity influencing factors are analyzed by statistical approach, and it is used to make productivity regression model. The finding of this research will improves monitoring and controlling of project schedule in construction phase.

• Korean Journal of Construction Engineering and Management
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• v.12 no.2
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• pp.81-88
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• 2011

Providers haven't recently had a flexible construction cost estimation system to meet various needs of consumers about public housing. So the subject of this study is to estimate construction cost reasonably in early project stage of public housing and then develop reliable means which is able to support construction cost management and establish a adequate funding investment plan as a provider. In this study, Regression analysis was performed by the case on 20 public apartment complex which were designed from the first half of 2007 to the first half of 2008. A total construction cost of construction, civil engineering, machinery, elevator, land scape, electricity and communication work was used as one sample for increasing explanation and representativeness of the case. In addition, The total construction cost which is devided into design, contract and completion cost was variously analysed for increasing relevance of model and actual utilization. The result of estimation model based on a total construction cost set up completion and design cost showed that error rate is within 2%, which is a excellent result. The estimation model of the construction cost developed by this study is expected to estimate approximate construction cost which is adjacent real construction cost in early stage of the project by using some data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.385-392
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• 2021

In general, earth retaining work in construction works enables the construction of structures, prevents the displacement of the surrounding ground to the maximum extent, and plays an important role in ensuring the safety of the surrounding structures and field workers. The earth retaining work and the construction method differ according to the various ground characteristics, surrounding topographical characteristics, repair environment, and design conditions. In particular, in the case of Seoul city, the environments and ground conditions differ according to the area. This study analyzed the earth retaining work cost mainly for the apartment construction project in Seoul and calculated the approximate earth retaining work cost at the project planning stage. A model was developed to predict the cost of earth retaining work that matches the characteristics of Seoul City and predict the construction cost for earth retaining work. This paper presents the predicted earth retaining work cost using a multiple regression model that applies 10 project outlines as independent variables. The error rate of the prediction result of the earth retaining work cost of the apartment construction project in Seoul using multiple regression models was 10.75%.

• Journal of the Korea Institute of Building Construction
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• v.13 no.5
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• pp.506-512
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• 2013

Since the amendment of the law on the private sector investment in social infrastructure in January of 2005, the government has been actively promoting Build-Transfer-Lease (BTL) projects. Notably, most new educational facilities have been built as BTL projects. For these facilities, the unit cost per unit area has been applied to predict construction costs. However, since construction costs are mostly managed after the detailed design phase, the costs can be estimated incorrectly. For this reason, cost management is needed in the planning phase, with a sound approximate estimate to prevent the wasteful use of funds. To address this shortcoming, this study aims to develop a quantity prediction model for education facilities using regression analysis in the planning phase. The developed model is focused on the required quantities of reinforced concrete and bricks. In order to achieve the objective, the data of 44 educational facility projects collected from Gyeonggi-do was used in the regression model. This study can be utilized by major stakeholders to accurately predict construction costs by estimating the appropriate quantities of reinforced concrete and bricks in the planning design phase.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.497-501
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• 2013

An important decision-making element for the success of housing redevelopment projects is a prediction of the profitability of redevelopment. Risk factors influencing profitability were deduced through a review of the literature about profitability and a risk analysis developed by a survey of maintenance projects. In addition, a profitability prediction depending on the analysis of risk factors is necessary to judge the business feasibility of a project in the planning stages. A profitability prediction model of management and disposal method, which is calculated by proportional rate and which helps estimate contributions to profitability, is proposed to prevent difficulties in business development. The proposed model has the potential to prevent interruptions, reduce the length of projects, generate cost savings, and enable rational decision-making during the project period by allowing a judgment of profitability at the planning stage.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.48-49
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• 2019

The number of disputes over defects after completion of construction work in apartment buildings is increasing every year. In this situation, the prediction of reasonable defect repair costs is very important. In this paper, we are going to collect basic data for predicting defect repair costs through the correlation analysis of the construction period and defect repair cost of apartment houses. For this purpose, first of all, the construction period and defect repair cost of apartment houses were analyzed to analyze the construction period for each type of work, the construction period for each project type, and the construction period for each standard calculation. Next, the correlation between defect repair cost and the independent variables of the candidate was conducted. According to the analysis, the ratio of framing air, the ratio of finishing air, and the number of delay days showed strong correlation.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2013.11a
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• pp.114-115
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• 2013

The maintenance and repair period consists the largest part of a construction project life cycle cost. However, it has been analyzed that the repairing plan relies on regulations and the officers' experience mostly that sometimes lead to performing unnecessary work. Moreover, the data occurred during repairing have not been stored in a system that can be used in future plans. Therefore, the purpose of this study is to suggest a repairing cost and time predicting model by applying the properties of the building.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.888-895
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• 2009

Compaction is a process of increasing soil density using physical energy. It is intended to improve the strength and stiffness of soil. In embankment, degree of compaction affects the construction time, money, also method of soil improvement. In large scale embankment project, difficulties of embankment should change due to uncertainty of settlement. So it is very important to predict the final settlement and factor of safety induced by embankment. In many construction site, there are primarily design of high embankment using in-situ soil. Therefore numerical analyses are necessary for valid evaluation of the settlement prediction. But due to the construction cost and schedule, there were lacking in properties of soil and also limited number of in-situ test were performed. So we proposed the method that can easily estimate the proper soil parameters and suggest the proper method of numerical analysis. From this, two-dimensional finite-difference numerical analysis was conducted to investigate the settlement and factor of safety induced by embankment with various case of compaction rate and embankment height.

• Korean Journal of Construction Engineering and Management
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• v.8 no.5
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• pp.191-200
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• 2007

This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.