IFC-based system and methods to support analysis of robot-assisted offsite construction

The growing shortage of workers experienced in the labor-driven architecture, engineering, and construction (AEC) industry in the last decades has negatively impacted the industry, especially in the productivity. In the search of alternatives to alleviate this concerning situation, the AEC industry has readopted the concept of prefabrication (offsite construction). Compared to stick-built construction, offsite construction provides many advantages, such as construction in a controlled environment, the ability to perform parallel activities, quality improvement, less construction waste, safety improvement, and overall cost reduction. Despite the numerous advantages, there are challenges that have hindered the efficacy of offsite construction in practice. One of such challenges is the lack of interoperability in the design, planning, and construction workflows. Another challenge is that fabrication and assembly operations still rely on manual efforts which are time-consuming, costly, and error prone. With the advancement in digital and automation technologies, such as building information modeling (BIM) and robotics, there is an increasing interest in integrating these technologies to improve productivity in offsite construction. However, this has not been realized yet due to 1) the lack of BIM capability to incorporate automation technology in the design workflow, and 2) the lack of considerations of robotic technology to support AEC processes. Therefore, to address these gaps, in this research, the author proposed methods to 1) analyze building design information to infer construction-ready information and 2) generate construction operations simulations/animations using off-the-shelf robotic systems. The proposed methods consist of algorithms that enable: 1) inference of geometric and physical properties of building elements from industry foundation classes (IFC) models, and 2) generation of simulations for analyzing robot-assisted construction operations. These methods were tested on different test cases. Compared with manual efforts, the developed systems were more time efficient in the automated extraction of geometric and physical properties from IFC models as well as in the generation of the sub-module packages for constructability analysis using robotic automation. Experimental results showed that: (1) the developed method can be utilized in inferring the geometric and physical properties of building elements from IFC data models in an automated fashion, achieving 60.61% to 100% precision and 90.30% to 99.59% recall; and (2) the developed algorithms successfully generated the robot-related information from IFC-based BIM and successfully generated the simulation components automatically. Such automation reduces the needs of manual efforts in the extraction and generation of robotic simulation components. This research opens a new door for practitioners to analyze a building design related to the use of robotics for construction.