Construction cost estimation: A parametric approach for better estimates of expected cost and variation

Swei, Omar; Gregory, Jeremy; Kirchain, Randolph

doi:10.1016/j.trb.2017.04.013

Cited by 43 publications

(24 citation statements)

References 25 publications

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“…Swei et al [15] presented an alternative approach for preliminary cost estimates through the combination of a maximum likelihood estimator to search for an optimal data transformation combined with least angle regression for input variable selection/dimensional reduction. Gardner et al [23] quantified the effort expended to carry out conceptual estimates using highway agency data and concluded that input variables require a low amount of effort and that have a high influence on the final predicted cost are desired in data-driven conceptual cost-estimating models for the highway agency studied.…”

Section: Literature Reviewmentioning

confidence: 99%

“…erefore, it is required to have a systematic way of exploring the cost-estimate accuracy factors which can be used for preliminary cost estimation of construction projects. In this regard, there are several studies that explored the techniques for identification of input cost factors for the PCE model in highway construction industry [7,[11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

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Accurate cost estimates are vital to the effective realisation of construction projects. Extended knowledge, wide-ranging information, substantial expertise, and continuous improvement are required to attain accurate cost estimation. Cost estimation at the preliminary phase of the project is always a challenge as only limited information is available. Hence, rational selection of input variables for preliminary cost estimation could be imperative. A systematic input variable selection approach for preliminary estimating using an integrated methodology of factor analysis and fuzzy AHP is presented in this paper. First, the factor analysis is used to classify and reduce the input variables and their variable coefficients are determined. Second, fuzzy AHP based on the geometric mean method is employed to determine the weights of input variables in a fuzzy environment where the subjectivity and vagueness are handled with natural language expressions parameterized by triangular fuzzy numbers. Then, the input variables are suggested to be selected starting with those having high coefficient and high importance weight. A set of three variables, one from each group, can be added to the estimating model at a time so that the problem of collinearity can vanish and good accuracy of the estimate can be ensured. The proposed approach enables cost estimators to better understand the complete input variable selection process at the early stage of project development and provide a more accurate, rational, and systematic decision support tool.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Meharie

Gariy

Mutuku

et al. 2019

Advances in Civil Engineering

View full text Add to dashboard Cite

show abstract

“…In recent years, researchers in the field of construction [7], [11]- [14] have investigated and developed new models in an attempt to improve cost estimation and forecasting, mainly using case-based reasoning, reference class forecasting, and artificial neural networks.…”

Section: Cost Estimatesmentioning

confidence: 99%

Construction Cost Estimation of Brazilian Highways Using Artificial Neural Networks

Barros¹,

Marcy²,

Carvalho³

2018

IJSCER

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Estimating costs of construction projects more accurately at the project development stage is crucial for feasibility studies and it is a key factor for their success. Construction costs are often underestimated and recent statistical studies show that errors in cost estimation have not diminished. This paper focuses on the development of a more accurate estimation technique for construction highway projects using Artificial Neural Networks. Different architectures of the network with 10, 15, and 20 neurons were trained and tested with the backpropagation algorithm. Based on this, data from fourteen highway projects in Brazil were collected and analyzed. Eleven parameters that contribute the most to the construction final budget were found after trials and errors. For the best scenario, an average cost estimation accuracy of 99% was achieved. This preliminary study showed the feasibility of the tool applied to projects in Brazil and may be used by public agencies in the future. 

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“…An estimation model for culverts material quantities has been carried out by Fragkakis, et al (2015) using linear regression. A parametric approach for a better pavement cost estimate by enhancing the earlier cost estimation approaches was conducted by Swei, et al (2017). An early bill-of-quantities (BoQ) estimation of concrete road bridges was developed by Dimitriou, et al (2018) using feed-forward artificial neural networks (FFANNs).…”

Section: Introductionmentioning

confidence: 99%

Estimation Modeling of Abutment Volume with Variations of Bridge Span, Abutment Height, and Seismic Zone

Mahendra

Triwiyono

2020

Journal of the Civil Engineering Forum

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The initial cost estimate for a bridge project using an estimation model can be done based on the dimensions, type, and material of the bridge. Research that has included bridge location as a determinant variable for initial bridge cost estimation has not been carried out much. The different locations of the bridge project will have different seismic accelerations and seismic load analysis. This study aims to create a model used to calculate the quantity needed for the construction of abutment in various locations with a PCI-Girder superstructure. The data used for the quantity estimation model was derived from the abutment design results. The data obtained from the calculation of concrete and reinforcing steel quantities from the abutment design with variations in bridge span i.e. 20 m; 25 m; 30 m; 35 m; and 40 m, abutment height i.e. 4 m; 6 m; and 8 m, and seismic zone 1, 2, 3, and 4. Volume estimation models are obtained by multiple linear regression analysis. The results show a very strong correlation between the span of the bridge and the height of abutment with the dependent variables. While the seismic zone has a strong correlation with the dependent variables. However, the seismic zone in this study did not meet the linear regression assumptions. This study developed 8 models to estimate abutment volumes. The R2 values of these models are 0.983 – 0.997. These showed that the models are properly made to be used to estimate abutment volumes with a PCI-Girder superstructure.

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Construction cost estimation: A parametric approach for better estimates of expected cost and variation

Cited by 43 publications

References 25 publications

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

An Effective Approach to Input Variable Selection for Preliminary Cost Estimation of Construction Projects

Construction Cost Estimation of Brazilian Highways Using Artificial Neural Networks

Estimation Modeling of Abutment Volume with Variations of Bridge Span, Abutment Height, and Seismic Zone

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