Generating 3D data model for civil engineering using a generative AI

Abstract

The civil engineering industry faces low productivity issues; hence, it is adopting Building/Construction Information Modeling, Management (BIM/CIM) to improve productivity in this domain. BIM/CIM utilizes 3D data models to streamline whole construction projects, design construction, operation and maintenance (O&M), and demolition processes, leading to enhanced efficiency and quality of construction projects. However, creating these 3D models by hand is time-consuming and labor-intensive. This issue is particularly true for infrastructures that engineers have been designing based on 2D data for a long time. This study proposes an interactive method for generating 3D data models using generative artificial intelligence (AI) to address this issue. Using the proposed method, engineers provide instructions in natural language and generate 3D data models automatically. The proposed method reduces the burden of engineers operating 3D modeling software or inputting complex parameters, resulting in labor savings and time reduction. This study focused on Industry Foundation Classes (IFC) 4.3 and conducted experiments to generate data models of simple shapes (rectangular cuboid, cylinder, and sphere). The proposed method utilizes one-shot prompting to generate 3D models collectively because IFC is a particular professional file format. This study evaluated the accuracy of the proposed method based on the ratio of the number of collectively generated models to the total number of generated models. The results show an accuracy of 64% for rectangular cuboids, 31% for cylinders, and 44% for spheres. The proposed method is currently limited to low accuracy and generating simple shapes. However, future development aims to improve accuracy using fine-tuning and AI agents and support complex shapes used in the civil engineering industry, such as beams, columns, and footing, as well as attributes. The proposed generative AI-based 3D data model generation method is expected to accelerate BIM/CIM adoption in the civil engineering industry, significantly contributing to productivity improvement.