Quantifying Variations in Project‐Cost Estimates

Bradley, Robert M.; Powell, Michael G.; Soulsby, Michael R.

doi:10.1061/(asce)9742-597x(1990)6:1(99)

Cited by 10 publications

(2 citation statements)

References 2 publications

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“…Cost-related challenges within sewerage projects constitute a distinct area of focus in the research literature. In one study [8], sewerage project cost estimates are examined using two alternative approaches. The first approach integrates component cost ranges and probability values established by a panel of estimators.…”

Section: Sewerage Construction Projects Managementmentioning

confidence: 99%

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Juszczyk,

Hanák,

Výskala

et al. 2023

Applied Sciences

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This paper presents research results on the development of an original cost prediction model for construction costs in sewerage projects. The focus is placed on fast cost estimates applicable in the early stages of a project, based on fundamental information available during the initial design phase of sanitary sewers prior to the detailed design. The originality and novelty of this research lie in the application of artificial neural network ensembles, which include a combination of several individual neural networks and the use of simple averaging and generalized averaging approaches. The research resulted in the development of two ensemble-based models, including five neural networks that were trained and tested using data collected from 125 sewerage projects completed in the Czech Republic between 2018 and 2022. The data included information relevant to various aspects of projects and contract costs, updated to account for changes in costs over time. The developed models present satisfactory predictive performance, especially the ensemble model based on simple averaging, which offers prediction accuracy within the range of ±30% (in terms of percentage errors) for over 90% of the training and testing samples. The developed models, based on the ensembles of neural networks, outperformed the benchmark model based on the classical approach and the use of multiple linear regression.

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Section: Sewerage Construction Projects Managementmentioning

confidence: 99%

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Juszczyk,

Hanák,

Výskala

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition, the advanced applications including probability and statistics, neural networks (NN), and relational database management systems (RDBMS) have been employed for the purpose of improving the accuracy. Findings of previous studies [1] [2][3] [4] indicate that the accuracy of the estimate using these advanced applications can be increased up to 20%.…”

Section: Intorductionmentioning

confidence: 99%

Approximate Cost Estimating System for Construction of Steel Mill

Woo¹,

Jung²,

Lee³

et al. 2001

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Enhancing the accuracy of an approximate cost estimate is a critical issue since the estimate ordinarily predicts the total cost of a project with insufficient project information. In order to facilitate the approximate cost estimate process, this paper proposes a computer-based integrated system that enables the flexibility to varying amount and accuracy of available information, higher accuracy by using several different adjustment factors at the same time, and decision supporting feature by fast manipulation of the profits and contingencies in determining the final bid price. Proposed methodology has been implemented for a steel mill construction company in Korea, and its viability was validated. Findings and practical implications are outlined.Keywords: approximate estimate, historical database, adjustment factors, information system 1. INTORDUCTION Preliminary estimating, especially for the designbuild contracts, is a critical process predicting the total cost of a project with insufficient project information. In order to enhance the accuracy of a preliminary or an approximate estimate, several methodologies have been developed and utilized.Approximate cost estimating methods ordinarily utilize parameters, factors, and indices. In addition, the advanced applications including probability and statistics, neural networks (NN), and relational database management systems (RDBMS) have been employed for the purpose of improving the accuracy. Findings of previous studies [1][2][3][4]indicate that the accuracy of the estimate using these advanced applications can be increased up to 20%.The approxi mate cost estimating method based on historical cost records is a frequently used one as well. When using this method, the quality of the historical cost data from the previously performed projects is a crucial factor determining the quality of the estimate result.The purpose of this research was to develop an estimating methodology and a computerized system for an engineering and construction firm in Korea, that has abundant experience in planning, designing, procurement, construction, inspection, and test operation of the Pohang and Kwangyang steelworks of the world's second largest steel company. The estimating system developed as a result of this research is integrated with the project management information system (PMIS) of the company in order to obtain quality information in a timely manner. SYSTEM DEVELOPEMENTThe system developed in this research, named CEDA (Capital cost Estimate Data Analysis ), uses relational database management systems (RDBMS) that manipulate all relevant estimate data and the historical database as well.CEDA consists of two subsystems; one for the estimate of construction cost and the other for the estimate of equipment cost. In estimating the cost of construction, the system mainly utilizes the shared historical construction performance database focused on actual labor productivity. For the cost estimate of major equipment to be installed in the steel mill, this system provides a...

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Using a risk register to integrate risk management in project definition

Willams

1994

International Journal of Project Management

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Quantifying Variations in Project‐Cost Estimates

Cited by 10 publications

References 2 publications

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Early Fast Cost Estimates of Sewerage Projects Construction Costs Based on Ensembles of Neural Networks

Approximate Cost Estimating System for Construction of Steel Mill

Using a risk register to integrate risk management in project definition

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