Abstract
The construction sector is one of the major global economies and is characterized by relatively low increases in productivity and high inefficiencies. The adoption of advanced technological solutions has the potential to increase productivity, safety, and quality and to reduce costs during the execution of construction processes. As the development of potential solutions for automating construction processes continues and the availability of such solutions is increasing, an evaluation of their potential benefits compared to those of the conventional processes that are currently employed on construction sites becomes relevant. However, the introduction of advanced technology on construction sites represents both an opportunity and a challenge and requires a careful assessment of the utility and feasibility of replacing traditional methods with automated approaches. The complexity, uniqueness, and uncertainty that are typical of construction projects are the main obstacles to resolving such decision problems. This research aims to define a novel approach for supporting decision-making regarding equipment selection under conditions of uncertainty in construction processes. To this end, Bayesian decision theory is applied to define an approach for representing and solving such decision-making problems; this application in turn establishes a basis for designing decision-theoretic expert systems that can be used by decision-makers. To facilitate the design of such systems, the axiomatic design (AD) theory of systems is applied to formulate design guidelines that support the collaborative design of such systems. The applicability of the approach and the design guideline is shown in two exemplary use case scenarios. The first aims at verifying the applicability of the defined approach for representing and solving uncertain construction equipment selection problems and concerns the selection of a scaffold in a construction project. The second use case scenario aims at verifying the applicability of the guideline and illustrates the collaborative design of a system for supporting decision-making concerning the adoption of robotic equipment in construction processes. The results show that the approach and the guideline can be applied for the collaborative design of decision-theoretic expert systems that (i) evaluate the utility of available alternatives based on evidence, (ii) account for uncertainty, and (iii) exploit expert knowledge and preferences of users in construction equipment selection problems.