The aim of this paper is to synthesize knowledge related to performance evaluation of automated construction processes during the planning and execution phases through a theme-based literature classification. The primary research question that is addressed is “How to quantify the performance improvement in automated construction processes?”
A systematic literature review of papers on automated construction was conducted involving three stages-planning, conducting and reporting. In the planning stage, the purpose of the review is established through key research questions. Then, a four-step process is employed consisting of identification, screening, shortlisting and inclusion of papers. For reporting, observations were critically analysed and categorized according to themes.
The primary conclusion from this study is that the effectiveness of construction processes can only be benchmarked using realistic simulations. Simulations help to pinpoint the root causes of success or failure of projects that are either already completed or under execution. In automated construction, there are many complex interactions between humans and machines; therefore, detailed simulation models are needed for accurate predictions. One key requirement for simulation is the calibration of the models using real data from construction sites.
This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.
Gap in research in the area of performance evaluation of automated construction is brought out. The importance of simulation models calibrated with on-site data within a methodology for performance evaluation is highlighted.
The authors wish to thank the Department of Science and Technology (DST) and Science and Engineering Research Board (SERB), India, who have funded this project through the grants DST/TSG/AMT/2015/234 and IMP/2018/000224.
Krishnamoorthi, S. and Raphael, B. (2022), "A review of methodologies for performance evaluation of automated construction processes", Built Environment Project and Asset Management, Vol. 12 No. 5, pp. 719-737. https://doi.org/10.1108/BEPAM-03-2021-0059
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