Early-stage cost estimation model for power generation project with limited historical data

Abstract

Purpose

Reliable conceptual cost estimation of large-scale construction projects is critical for successful project planning and execution. For addressing the limited data availability in conceptual cost estimation, this study proposes an enhanced ANN-based cost estimating model that incorporates artificial neural networks, ensemble modeling and a factor analysis approach.

Design/methodology/approach

In the ANN-based conceptual cost estimating model, the ensemble modeling component enhances training, and thus, improves its predictive accuracy and stability when project data quantity is low; and the factor analysis component finds the optimal input for an estimating model, rendering explanations of project data more descriptive.

Findings

On the basis of the results of experiments, it can be concluded that ensemble modeling and FAMD (Factor Analysis of Mixed Data) are both conjointly capable of improving the accuracy of conceptual cost estimates. The ANN model version combining bootstrap aggregation and FAMD improved estimation accuracy and reliability despite these very low project sample sizes.

Research limitations/implications

The generalizability of the findings is hard to justify since it is difficult to collect cost data of construction projects comprehensively. But this difficulty means that our proposed approaches and findings can provide more accurate and stable conceptual cost forecasting in the early stages of project development.

Originality/value

From the perspective of this research, previous uses of past-project data can be deemed to have underutilized that information, and this study has highlighted that — even when limited in quantity — past-project data can and should be utilized effectively in the generation of conceptual cost estimates.

Citation

Lee, J.G., Lee, H.-S., Park, M. and Seo, J. (2022), "Early-stage cost estimation model for power generation project with limited historical data", Engineering, Construction and Architectural Management, Vol. 29 No. 7, pp. 2599-2614. https://doi.org/10.1108/ECAM-04-2020-0261