A review of methodologies for performance evaluation of automated construction processes

Purpose 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? Design/methodology/approach 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. Findings 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. Research limitations/implications This study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years. Originality/value 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.

Automated summary

This paper synthesizes knowledge related to performance evaluation of automated construction processes through theme-based literature classification. The study highlights the need for detailed simulation models to accurately predict complex interactions between humans and machines in automated construction. Calibration of simulation models using real data from construction sites is emphasized as a key requirement for accurate predictions.