# On the Design of a Decision Support System for Robotic Equipment Adoption in Construction Processes
## 1 Introduction
Analyses conducted in previous studies have shown that the key parameters considered when evaluating the utility of robotic systems compared to conventional processes are safety, quality, productivity, and cost [26]. Results of comparisons prove that construction robots have the capability to increase safety [12; 26; 28], productivity [12; 26; 28], and quality [12; 26; 29] on construction sites. Some studies prove that robots may have a positive impact on cost [5; 8], while others find that extra costs can occur for their deployment on site [12; 26]. These considerations show that the deployment of robotic equipment on construction sites can have both benefits and drawbacks. Although many potential benefits of construction robots are described in the above-mentioned works, other barriers can prevent their widespread adoption in practice, such as a low propensity towards changing existing work practices [5; 11]. This conservative attitude can be changed by involving domain experts in the definition of potential application areas and the expected impacts of construction robots. Previous studies show how experts can be engaged in the strategic definition of high-priority applications and the evaluation of the potential impacts of construction robots in these fields [5]. The aim of this research is to illustrate the design of a decision support system (DSS) for robotic equipment adoption that compares conventional and robotic processes by evaluating their utility. To achieve this aim we apply an axiomatic-design guideline [27] that supports the design of decision-theoretic expert systems based on Bayesian decision theory to aid equipment selection in construction. Axiomatic design is a system design methodology that is successfully applied in engineering, business, software, and product development [30]. Decision-theoretic expert systems allow one to assess the utility of the available options based on evidence, preference statements and expert knowledge. The approach is illustrated by means of exemplary use case scenarios of a research project that aims at developing configurable collaborative modular robotic platforms targeting use cases in construction. The use case scenarios are defined in collaboration with domain experts of the construction group participating in the project. We choose a use case scenario that considers the employment of collaborative robots for semiautonomous or teleoperated marking and spraying for the detailed description of the approach. As a result, this research shows how a DSS in the field of robotic equipment adoption in construction can be designed. In particular, Axiomatic design allows us to divide the complex design process of the decision theoretic expert system into small and manageable steps that can be easily replicated in additional use cases. The structured involvement of domain experts in every development step increases the reliability of the DSS, fills lack of data with expert knowledge, and can increase the acceptance of the system by the potential users. The remainder of this article is organised as follows: in Section 2 we introduce the preliminaries on the materials and methods considered; in Section 3 we describe the development of the prototype and the obtained results; in Section 4 we discuss the results; and in Section 5 we draw the conclusions.