• Journal of the Korea Institute of Building Construction
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• v.14 no.5
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• pp.477-485
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• 2014

Standard production unit system and historical cost data are the most typical data base for calculating budget price in construction. However, these construction cost estimation methods are difficult to calculate proper construction cost because definition, additional allowance or modification criteria is not clear in construction within one day. Therefore, this study identifies problems for standard production unit system and historical cost data and suggests the improvements for them. For the objectives, this study analyzes frequency after implementing survey for 44 specialty contractors in placing at kyeonggi-province. As the results of the study, labor costs in standard production unit system and equipment costs in historical cost data and in construction of pavement and maintenance by project type was exceeded at most high rate against construction cost estimation methods. Based on this result, standard production unit system and historical cost data need to be modified by three improvements such as classification by project scale. These will be baseline data for improvement of construction cost estimation methods for less than one day workload.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.34 no.2
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• pp.667-676
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• 2014

This study has conducted case studies in order to suggest alternatives to the historical data-based estimate system. Price fluctuation calculation methods based on historial cost indexes, standard estimate and construction cost indexes were applied to 9 road construction sites in Jeju for an analysis. As a result, in 5 construction sites (about 56% of 9 sites), the index control rate calculated based upon historical data-based estimate system was higher than that calculated based upon standard estimate and construction cost indexes. Thus the establishment of the requirements for the adjustment of contract price due to price fluctuation delays, which leads to a significant difference in price fluctuation amount. And, in an analysis of construction cost indexes, the indexes for road construction were used for calculating index control rate which ranges from 2.0 to 9.4 percent, indicating the time of construction amount and price fluctuation application has a significant influence on index control rate.

• 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.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.417-424
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• 2009

For a construction project to progress smoothly, effective cost estimation is vital, particularly in the conceptual and schematic design stages. In these early phases, despite the fact that initial estimates are highly sensitive to changes in project scope, owners require accurate forecasts which reflect their supplying information. Thus, cost estimators need effective estimation strategies. Practically, parametric cost estimates are the most commonly used method in these initial phases, which utilizes historical cost data (Karshenas 1984, Kirkham 2007). Hence, compilation of historical data regarding appropriate cost variance governing parameters is a prime requirement. However, precedent practice of data mining (data preprocessing) for denoising internal errors or abnormal values is needed before compilation. As an effort to deal with this issue, this research proposed a statistical methodology for data preprocessing and verified that data preprocessing has a positive impact on the enhancement of estimate accuracy and stability. Moreover, Statistically Preprocessed data Based Parametric (SPBP) cost models are developed based on multiple regression equations and verified their effectiveness compared with conventional cost models.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.488-493
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• 2013

Accurate initial cost estimates are essential to effective management of construction projects where many decisions are made in the course of project management by referencing the estimates. In practice, the initial estimates are frequently derived from historical actual cost data, for which standard distribution-based techniques are widely applied in the construction industry to account for risk associated with the estimates. This approach assumes the same probability distribution of estimate errors for any selected estimates. This assumption, however, is not always satisfied. In order to account for the probabilistic nature of estimate errors, an alternative method for measuring the risk associated with a selected initial estimate is developed by applying the Bayesian probability approach. An application example include demonstrates how the method is implemented. A hypothesis test is conducted to reveal the robustness of the Bayesian probability model. The method is envisioned to effectively complement cost estimating methods that are currently in use by providing benefits as follows: (1) it effectively accounts for the probabilistic nature of errors in estimates; (2) it is easy to implement by using historical estimates and actual costs that are readily available in most construction companies; and (3) it minimizes subjective judgment by using quantitative data only.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.3D
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• pp.371-381
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• 2008

The systematic methodology for estimating construction cost approximately at planning and pre-design phase of a river facility construction project has not yet been established because of its unique characteristics including its relatively small project size in terms of cost. This research suggests a 4-level cost information structure and identifies critical factors affecting construction cost as a result of thorough analysis of accumulated historical cost data of river facility construction projects. Also, this research presents the framework of the approximate cost estimating methodology for river facility construction project a planning stage.

• Journal of the Korea Institute of Building Construction
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• v.5 no.1 s.15
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• pp.111-121
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• 2005

This study purposed to derive a model to estimate appropriate construction costs for the application of the estimation system based on historical construction data. For this purpose, it analyzed data ($1999\sim2004$) on the construction costs of reinforced concrete apartments (Long-Lamina-type apartments), which have been highly standardized and have a lot of relevant objective data using statistical analysis techniques and developed an estimation model.

• Journal of the Korea Institute of Building Construction
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• v.13 no.1
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• pp.66-74
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• 2013

In the early stages of a construction project, the most important thing is to predict construction costs in a rational way. For this reason, many studies have been performed on the estimation of construction costs for apartment housing and office buildings at early stage using artificial intelligence, statistics, and the like. In this study, cost data held by a provincial Office of Education on elementary schools constructed from 2004 to 2007 were used to compare the multiple regression model with an artificial neural network model. A total of 96 historical data were classified into 76 historical data for constructing models and 20 historical data for comparing the constructed regression model with the artificial neural network model. The results of an analysis of predicted construction costs were that the error rate of the artificial neural network model is lower than that of the multiple regression model.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.444-447
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• 2006

Construction managers need to pay a close attention to the resource utilization in order to deliver the construction project successfully. Construction scheduling is crucial for resource control in that it provides information when and how much to bring down work force to sites. In scheduling, activity duration is projected based on the productivity of historical data or the intuition of scheduler. This paper studies the opportunity of applying cost-based productivity for estimating activity duration. For cost-based productivity, the cost of resource is used as an input and the work quantities as an output. Out of historical data, regression model has been developed to understand the validity of applying cost-based productivity in projecting activity duration. The result of study will work as a prerequisite for implementing the environment of database-based construction scheduling.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• autumn
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• pp.367-370
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• 2002

The Elements of common temporary work in Construction Project have ambiguous work scope between client and builder. Also problems of non-breakdown, non-standardization in common temporary work are obstructive when contract. Finally they will be reached claim factors on the construction project. Because the common temporary work information management system is not built, We have to develop common temporary work information management system based on historical cost database for Construction Management. This system will successively accomplish the project in pre-construction step to standardize work items and to forecast the cost of common temporary work. Therefore feasibility study will be possible with historical database in new project.