With rapid urbanization and population growth, there is a huge housing demand coupled with
labour productivity challenges. Automation is considered as a solution to this crisis. However, not much
research has been carried out to quantify the benefits of automation in construction and to identify the
most appropriate level of automation in a given project.
Predicting the performance of new automated construction methods is useful for planning
purposes. Evaluating the potential for improving process efficiency by adoption of automation at various
levels of construction processes helps in taking critical planning decisions. In this context, it is worth taking
inspiration from the “search and optimization approach” that has been extensively used in engineering
design tasks.
This research presents a methodology to identify optimal automated construction processes
through model composition and stochastic search. This is applicable for both on-site and off-site
construction. Through model composition, millions of potential solutions are generated. These are
automatically evaluated through discrete event simulation; and performance parameters such as cost and
time are computed. Through a multi-criteria decision making, the best automated solution is identified
taking into account trade-offs among conflicting objectives that are acceptable to the user.
The methodology is demonstrated using a case study which shows its potential application at the
stage of planning, pre-construction and during construction of the project. Through multi-criteria decision
making, appropriate automated construction systems can be identified, and overall project performance
can be increased.
This research contributes to the application of automation and robotics in construction for
improving by developing a methodology for evaluating the benefits. It helps in the adoption of appropriate
construction tools, equipment and machinery through early decision making related to appropriate
automation systems. Significant impacts are expected in both on-site and off-site construction.
Keywords: Construction automation; Optimization; Automated fabrication; Project performance
improvement; Case based-reasoning; Compositional modeling; Discrete event simulation; Multi-criteria
decision making