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In recent decades, the construction industry has undergone a technological shift incorporating innovative technologies, such as robotics. However, information requirements must be met to integrate robotics further. Currently, building information models (BIM) contain substantial project information that can be leveraged for robots to create construction tasks, but for some building systems, the level of development (LOD) is inadequate to support these new requirements. Therefore, this study proposes a framework to increase the LOD of building systems by considering location information (X, Y, Z), orientation, material type, and component I.D. The computational modeler, Dynamo, is leveraged to increase the model’s LOD, extract information, and facilitate robotic task execution in the future. A case study is presented for multiple masonry room configurations developed in Autodesk Revit, where masonry units are generated and placed into design locations based on the geometry of the wall system. The case study used concrete masonry units (CMU) and standard brick. The number of partial-sized and full-sized blocks for each configuration was recorded, along with the computational time required to generate the units. It was observed that room configurations with more openings had longer computational times when compared to rooms constructed from the same material. After running the script, the model is reviewed to ensure accuracy and prevent overlaps or gaps in the model. The workflow provides insight into the methods used to interpret model geometry and extract information.

 

Building information modeling (BIM) technology in construction has become increasingly prevalent in recent years, and integrating robotics is seen as a natural step to improve efficiency. To increase the level of development (LOD) of a BIM model to support a construction robot, parametric modeling can be used to create highly detailed models by supplementing and defining the geometric and physical properties of the construction elements, such as the components’ size, shape, and material parameters, which are used as inputs for designing robotic tasks. Component information and data are stored as extractable parameters within the BIM model, allowing a robot to perform highly precise and repeatable tasks. This study develops a framework for implementing computational parametric modeling for masonry wall systems with Dynamo. This study tested six wall configurations constructed of 8″ × 8″ × 16″ concrete masonry units (CMUs). Dynamo successfully interpreted most wall geometries placing full-sized CMUs into the correct design locations. Errors occurred when placing partial-sized CMUs, typically at wall intersections, revealing a need for future refinement. The study shows the careful planning and considerations needed to implement computational modeling to generate model content for creating robotic tasks. 

This paper presents a construction robot schema (CRS) for construction planners to facilitate decision-making and project planning in operating robotics. CRS is a database schema structure that was developed in our previous study, which can facilitate collecting and exchanging data of various construction robots based on the data requirements of the construction domain. We validated the applicability of the schema by the simulation of robotic construction operations. In addition, we conducted interviews with experts from the construction industry to validate the information in CRS. As a result, the schema was validated with minor revisions to some parameters. The characteristics of CRS compared to other types of robot schema are that its development and application are based on the perspective of the construction domain and are designed to cover different construction robots broadly. The conclusions highlight the contributions of the data schema use and applicability for the construction industry.