A bibliometric review of the statistical modelling techniques for cost estimation of infrastructure projects

IF 3.5 Q3 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY Smart and Sustainable Built Environment Pub Date : 2023-10-11 DOI:10.1108/sasbe-01-2023-0005
Chinthaka Niroshan Atapattu, Niluka Domingo, Monty Sutrisna
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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.
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基础设施项目成本估算的统计建模技术的文献计量学综述
基础设施项目的成本超支一直是一个令人担忧的问题,需要一个适当的解决方案。当前的评估实践需要改进以减少成本超支。这项研究旨在寻找可能的统计建模技术,可用于开发成本模型,以产生更可靠的成本估算。设计/方法/方法采用两阶段选择法对Scopus的相关出版物进行文献综述。然后,使用相似度可视化(VOS)-Viewer来开发研究主题共现关键词分析和年度趋势的可视化地图。研究发现,建设项目成本模型主要采用回归分析(RA)、人工神经网络(ANN)、基于案例的推理(CBR)、模糊逻辑、蒙特卡罗模拟(MCS)、支持向量机(SVM)和参考类预测(RCF)等7种技术。RA、ANN和CBR是研究最多的技术。此外,观察到将两种或多种技术结合到一个模型中可以提高模型的性能。研究局限/启示本研究仅限于文献计量学文献综述的发现。研究结果提供了统计技术的评估,该行业可以采用这些技术来改进基础设施项目的传统评估实践。原创性/价值本研究对成本建模技术的研究进行了映射,并分析了趋势。它还审查了为基础设施项目开发的模型的性能。研究结果可用于进一步研究,利用性能更好的统计建模技术开发更可靠的成本模型。
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来源期刊
Smart and Sustainable Built Environment
Smart and Sustainable Built Environment GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
9.20
自引率
8.30%
发文量
53
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