Automation is generally assumed to improve project productivity. However, not enough research is done in the area of quantitative methods to evaluate productivity improvements through automation in construction. The aim of this study is to develop a methodology for analyzing productivity of any given automation system for construction. A case study of an automation system developed inhouse is used for illustration and validation. This system involves automated connections of column modules and coordinated lifting of the column assembly. A laboratory experiment has been done using this system for constructing column structures using modular blocks. The experimental results are compared with their equivalent manual processes. These studies are conducted using EZStrobe simulations which are calibrated using experimental data. Of the various project performance parameters, only time has been included in this study. The results would throw light on the impact of automation on construction activities on-site.

No abstract

PurposeThe 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/approachA 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.FindingsThe 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/implicationsThis study is based on a review of 169 papers from a database of peer-reviewed journals, within a time span of 50 years.Originality/valueGap 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.

No abstract