• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.478-481
• /
• 2002

In these days, Cost and Scheduling was managed effectively because of introduction of EVMS to construction project. However the EVMS is appropriate methods to advanced country, so it is difficult to apply into domestic construction project. in this paper weighted value n, m was used of compositive index(CI) to forecast Estimate At Completion (EAC) using statistical analysis in credible interval the objective of this paper is to verify compositive index(CI) and to forecast Estimate At Completion (EAC).

• Journal of KIBIM
• /
• v.12 no.2
• /
• pp.40-48
• /
• 2022

It is very important to accurately predict construction costs in the early stages of the construction project. However, it is difficult to accurately predict construction costs with limited information from the initial stage. In recent years, with the development of machine learning technology, it has become possible to predict construction costs more accurately than before only with schematic construction characteristics. Based on machine learning technology, this study aims to analyze plans to more accurately predict construction costs by using only the factors influencing construction costs. To the end of this study, the effect of the error rate according to the activation function and the node configuration of the hidden layer was analyzed.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.11
• /
• pp.1503-1510
• /
• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

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

• Korean Journal of Construction Engineering and Management
• /
• v.22 no.6
• /
• pp.76-86
• /
• 2021

Construction projects have high potentials of claims and disputes due to inherent risks where a variety of stakeholders are involved. Since disputes could cause losses in terms of cost and time, it is a critical issue for contractors to forecast and pro-actively manage disputes in advance in order to secure project efficiency and higher profits. The objective of the study is to develop a decision tree-based predictive analytics model for forecasting dispute types and their probabilities according to construction project conditions. It can be a useful tool to forecast potential disputes and thus provide opportunities for proactive management.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.353-358
• /
• 2001

Since the financial crisis broke out, liquidity has become the critical issue in housing construction industry. In order to secure liquidity, it is prerequisite to precisely forecast cash flow. However, construction companies have failed to come up with a systematic process to manage and forecast cash flow. Until now, companies have solely relied on the prediction of profits and losses, which is carried out as they review business feasibility. To obtain more accurate cash flow forecast model, practical pattern of payments should be taken into account. In this theory, basic model that analyzes practical housing payment collection pattern resulting from prepayments and arrears is described. This model is to complement conventional cash flow forecast scheme in the phase of business feasibility review. Analysis result on final losses in cash that occur as a result of prepayment and arrears is considered in this model. Additionally, in the estimation of construction cost in the phase of business feasibility review, real construction prices instead of official prices are applied to enhance accuracy of cash outflow forecast. The proportion of payment made by a bill and changes in payment date caused by rescheduling of a bill are also factored in to estimate cash outflow. This model would contribute to achieving accurate cash flow forecast that better reflect real situation and to enhancing efficiency in capital management by giving a clear picture with regard to the demand and supply timing of capital.

• Journal of the Korea Institute of Building Construction
• /
• v.18 no.6
• /
• pp.621-631
• /
• 2018

The repair and maintenance cost of domestic public rental housing is an issue of considerable interest and growing financial concern. This paper suggests a quantity-based model as an alternative method for predicting costs, instead of the conventional model which is based on actual cost data. Furthermore, this paper provides a forecast of the repair costs incurred each year during the multi family house's maintenance phase (40 years). The recently changed the long-term repair plan and quality-improved interior materials were considered into the research. In order to estimate the cost of maintenance work, 5 sample apartments were selected and analyzed. The repair and maintenance cost from the case studies was converted to cost per household and per floor area for general use. On the other hand, the net present value method was applied to reflect the effect of time. We expect that the results will help to establish expenditure plans that are more effective for public rental housing in the maintenance stage.

• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.219-225
• /
• 2013

State transportation agencies utilize fleets of heavy equipment to construct and maintain roadways. Equipment cost models can be developed to forecast economic life, which is the point at which the average unit cost to date reaches a minimum. A calculated economic life and cost models can be used to quantify the impacts of management strategies applied to a fleet. The purpose of this research was to develop an accurate method of quantifying the results of management strategies applied to a fleet of heavy construction equipment. The strategies evaluated are related to the annual usage of the fleet and the size of the fleet. More specifically the methodology is used to adjust the economic model to consider a limit to the annual decline in machine usage and a reduction in the number of machines in the fleet. When limiting annual machine usage, a specified rate is applied to the usage of the fleet, while total usage is held constant. This causes aging at a modified rate. A reduction in fleet size also causes a change to the usage of a fleet as the fleet must use fewer machines to produce the same total usage.

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

• Korean Journal of Construction Engineering and Management
• /
• v.7 no.4 s.32
• /
• pp.91-99
• /
• 2006

The purpose of this study is propose an Artificial Neural Network(ANN) model for the construction estimate of the public educational facility at conceptual stage. The current method for the preliminary cost estimate of the public educational facility uses a single-parameter which is based on basic criteria such as a gross floor area. However, its accuracy is low due to the nature of the method. When the difference between the conceptual estimate and detailed estimate is huge, the project has to be modified to meet the established budget. Thus, the ANN model is developed by using multi-parameters in order to estimate the project budget cost more accurately. The result of the research shows 6.82% of the testing error rates when the developed model was tested. The error rates and the error range of the developed model are smaller than those of the general preliminary estimating model at conceptual stage. Since the proposed ANN model was trained using the detailed estimate information of the past 5 years' school construction data, it is expected to forecast the school project cost accurately.