• International conference on construction engineering and project management
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• 2011.02a
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• pp.1-6
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• 2011

As large-scale building projects have recently increased for the residential, commercial and office facilities, construction costs for these projects have become a matter of great concern, due to their significant construction cost implications, as well as unpredictable market conditions and fluctuations in the rate of inflation during the projects' long-term construction periods. In particular, recent volatile fluctuations of construction material prices fueled such problems as cost forecasting. This research develops a time series model using the Box-Jenkins approach and material price time series data in Korea in order to forecast trends in the unit prices of required materials. Building information modeling (BIM) approaches are also used to analyze injection times of construction resources and to conduct quantity take-off so that total material prices can be forecast. To determine an optimal time series model for forecasting price trends, comparative analysis of predictability of tentative autoregressive integrated moving average (ARIMA) models is conducted. The proposed BIM-based time series forecasting model can help to deal with sudden changes in economic conditions by estimating material prices that correspond to resource injection times.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.192-200
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• 2003

The object of this research is to develop a computerized algorithm of cost estimation method to forecast the total construction cost in the bidding stage by the historical and elemental work cost data. Traditional cost models to prepare Bill of Quantities in the korea construction industry since 1970 are not helpful to forecast the project total cost in the bidding stage because the BOQ is always constant data according to the design factors of a particular project. On the contrary, statistical models can provide cost quicker and more reliable than traditional ones if the collected cost data are sufficient enough to analyze the trends of the variables. The estimation system considers non-deterministic methods which referred to as the 'Monte Carlo simulation. The method interprets cost data to generate a probabilistic distribution for total costs from the deficient elemental experience cost distribution.

• Journal of the Korea Institute of Building Construction
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• v.12 no.3
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• pp.257-265
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• 2012

Many construction companies are simultaneously carrying out numerous projects in the housing construction industry. It is essential to accurately forecast the cash flow of a project through optimal process management and resource input in order to manage funds rationally and enhance the competitiveness of a company. Current cash flow forecasting methods offer lower accuracy due to a large gap between the revenue and expenditure element. Expenditure elements depends on the real-time changing actual cost for work performed. This research survey was conducted on the actual state of construction management of K company to investigate the problems of cash flow forecasting. To achieve this, the work process and construction management system were integrated to improve the cost management system of K company. To accurately forecast the cash flow of a project, revenue and expenditure elements were displayed in the total cash flow forecast window. This research is expected to assist in the implementation of a system of cash flow forecasting on housing construction by excluding negative elements of revenue and expenditure.

• Journal of the Korea Institute of Building Construction
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• v.7 no.3
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• pp.131-137
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• 2007

To predict deep excavation wall movements is important in the urban areas considering the cost and the safety in construction. Failing to estimate deep excavation wall movements in advance causes too many problems in the projects. The purpose of this study is to propose the forecast model of deep excavation wall movements using artificial neural networks. The data of the Deep Excavation Wall Movements which were done form Long research is used of Artificial neural networks training and apply the real construction work measured data to the Artificial neural networks model. Applying the artificial neural networks to forecast the deep excavation wall movements can significantly contribute to identifying and preventing the accident in the overall construction work.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2004.05a
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• pp.113-117
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• 2004

We need the contingency cost in order to deal with the uncertainty to be accompanied inevitably at the construction and an every kind risk not to forecast in advance. And also the contingency colt needed for the change order and we need it for reduction of the delay and reduce the trouble between owner and constructor. This study, through checking and analyzing the risk factor, in the step of domestic construction, suggests optimal management reserve to specific business about the contract type and the scale. The main results of this research are summarized as follow. First, I investigated the recognition about the contingency cost, grasped the risk to be happened at the construction step and found out the frequency occurrence, through making up question to engineer are carrying out their job in the domestic construction. Second, I computed optimal contingency cost rate by the statistics investigation, and proposed an improvement plan and problem when compute a contingency cost.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.383-386
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• 2001

The ability to make good cost overruns predictions is a very important aspect of in major construction project. The probabilistic cost models can provide more reliable than traditional cost models which have been used in korea to prepare Bill of Quantities, if the actual cost data are sufficient enough to analyze the trends of the variables. The paper considers non-deterministic methods in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The objectives of this research is to develop a method to forecast the probabilistic total construction cost and the elemental work cost

• International conference on construction engineering and project management
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• 2015.10a
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• Korean Journal of Construction Engineering and Management
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• v.2 no.2 s.6
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• pp.90-97
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• 2001

The paper considers non-deterministic methods of analysing the risk exposure in a cost estimate. The method(referred to as the 'Monte Carlo simulation' method) interprets cost data indirectly, to generate a probability distribution for total costs from the deficient elemental experience cost distribution. The Monte Carlo method is popular method for incorporating uncertainty relative to parameter values in risk assessment modelling. Non-deterministic methods, they are here presented as possibly effective foundation on which to risk management in cost estimating. The objectives of this research is to develop a computerized algorithms to forecast the probabilistic total construction cost and the elemental work cost at the planning stage.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.212-219
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• 2003

In construction project planning and control, a cost model performs a critical role such as cost determination on a contract stage and cost tracing. The model can maximize owner's profit and value within the project budget and optimize cost management works on overall construction implementation stages. A BoQ(Bill of Quantities) generally adopted in a unit price contract has been applied as an important tool for cost control and forecast. However a previous cost model based on the BoQ has shown limitations in that it requires too detailed information and heavy manpower on cost management and difficulty in keeping relationship with construction planning, scheduling and progress management. The each cost items and unit prices which constitute of construction works are individually very important management factors but the relative weight for each items and prices have a difference on the contents and conditions of each conditions of each construction works. In consideration of this structural mechanism of cost determination, this research is aimed at examining the critical factors affecting the construction cost determination and propose and verify a new cost forecasting model which is more simple and efficient and also keeps the accuracy of cost management.

• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.24-34
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• 2013

Since 2004, the government decided to gradually introduce Actual Cost Data into cost estimate for improving problems of below-cost tendering and to reflect fair market price through competition and carry contract efficiently. However, there are many concerns that Actual Cost Data has not reflected real market price, even that has contributed to reduce the government's budget. General construction firm's burden for labor cost is imputed to specialty contractors and eventually it becomes construction worker's burden. Therefore, realization of Actual Cost Data is very important factor to settle this system. To understand realization level and make short term forecast, this paper drew construction group of which labor cost constitutes more than 95% of direct cost, and compares their Actual Cost Data with relevant skilled workers's unit wage and predicts using time series analysis. The bid price which is not be reflected market price accelerates work environment changes and leads to directly affect such as late disbursement of wages, bankruptcy to workers. Therefore this paper is expected to be used to the preliminary data for solving the problem and establishing improvement of Actual Cost Data.