A bibliometric review of the statistical modelling techniques for cost estimation of infrastructure projects
Abstract
Purpose
Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.
Design/methodology/approach
A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.
Findings
The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.
Research limitations/implications
The research was limited to the findings from the bibliometric literature review.
Practical implications
The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.
Originality/value
This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.
Keywords
Acknowledgements
The authors would like to acknowledge Massey University, New Zealand for providing the doctoral scholarship for funding and express gratitude to Professor Bob Giddings for extensive support, advice and feedback on this paper during CIB World Congress 2022.
Citation
Atapattu, C.N., Domingo, N. and Sutrisna, M. (2023), "A bibliometric review of the statistical modelling techniques for cost estimation of infrastructure projects", Smart and Sustainable Built Environment, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/SASBE-01-2023-0005
Publisher
:Emerald Publishing Limited
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