# On the Design of a Decision Support System for Robotic Equipment Adoption in Construction Processes
## 1 Introduction  
The broad adoption of automation and robotics is changing operations in many business sectors [1]. Even though the construction sector is one of the major economies, it suffers from inefficiencies and a low increase in productivity [2, 3] and could, therefore, highly benefit from the introduction of automation and robotics. The adoption of robotic systems has the potential to increase safety, quality, productivity, and to reduce cost in construction processes [4, 5, 6]. During the last decades, the interest towards the development of robotic solutions for applications in the construction sector is constantly growing and their potential deployment is addressed in several works [5, 6, 7, 8]. However, compared to other sectors, the construction industry can be considered as a traditional industry that is characterised by a lack of interest in innovation [9, 10] and a clear opposition to changes [11]. Even though the awareness of potential benefits of automation and robotics in the construction industry is increasing, the adoption of such technologies can be judged as slow [5]. The purpose of this study is to facilitate the comparison of advanced technological solutions with conventional manual construction processes. For this purpose, we illustrate how a system for decision support in the field of robotic equipment adoption can be designed and developed. This can be achieved by performing a structured evaluation of the impact that advanced technologies may have on safety, quality, productivity, and cost, and by assessing the utility of their adoption compared to conventional manual processes in an unbiased way. Current research shows that one way to support decision making in this field is the assessment of the actual performance of robots compared to traditional work practices on the construction site [12] and to provide adequate tools for supporting decision makers in the choice of replacing traditional processes with automated systems [13]. However, uncertainty and interdependencies, typical of construction operations, often hinder the definition of standardised approaches for decision-making [14]. Furthermore, when facing the decision of whether to replace conventional work methods with automated counterparts, the evaluation of alternatives should be able to reflect both preferences and knowledge of the users [15]. Equipment selection problems in construction can also be supported by different approaches and methods [16], as follows. The selection of cranes can be supported by multi-criteria decision-making methods that are able to evaluate project specific requirements, the characteristics of the equipment and economic factors [17; 18]. Multicriteria decision-making methods that consider both qualitative and quantitative criteria are also employed for the selection of excavation machinery [19; 20]. Construction machinery selection for infrastructure projects can be performed by using a decision support framework that evaluates risks and costs of the available alternatives [21]. In addition, the use of Building Information Modelling (BIM) is playing an important role in the field of automation and robotics in construction [22]. BIM is a methodology supporting the management of information within the construction sector. The result of the BIM methodology is a BIM model, a model that contains both geometric and semantic data of a building that can be employed along its whole lifecycle [23]. Current research shows how such BIM models can be integrated into robotic control systems to support the deployment of robotics in building construction and operation [24; 25]. Nowadays, only few construction robots are actually used on real construction sites and comparisons to traditional or conventional processes are rarely available [26]. The performance of automated systems and traditional work methods for a given project can be assessed by showing how they perform on the level of single construction specific criteria or variables [12; 15; 27].