Off-site construction has been a crucial part of industrializing the industry to realize higher productivity, better quality, and a more sustainable approach for constructing buildings. Off-site construction requires decomposing a floor plan into modules that can be in the form of either panelized walls or volumetric modules. However, the previous modularization models and approaches are limited due to their inability to consider the topological constraints of the modules, the flexible modularization of varying floor plans, and the mixed use of panelized walls and volumetric modules. As such, this paper proposes a graph-based optimization methodology for the hybrid modularization of building floor plans. The methodology was implemented using a multiobjective genetic algorithm that encodes and decodes the floor plan using novel graph modeling and operations. A visual programming script was developed to extract the wall properties, their adjacencies, and junction information from the building information model (BIM) of the floor plan. Time and cost estimation functions were developed to evaluate the hybrid strategies of panelized-volumetric modularization. The deployment of the methodology was demonstrated using an example floor plan design, which resulted in a spectrum of hybrid modularization plans ranging between fully volumetric and fully panelized solutions. For this specific example, the fully volumetric solution was 23% faster than the fully panelized solution but was 22% more expensive. The main contributions of this study are the topological modeling of module types, their floor plan postdesign flexible utilization, and the ability to explore hybrid modularization strategies. The findings of this study can prove useful for modular and off-site building manufacturers to improve their agility and increase their market share.

Graph Neural Networks (GNNs) have become a popular toolkit for generative floor plan design. Although design variation has improved greatly, few studies consider non- geometrical characteristics, such as building energy performance, in the generative design process. This paper presents a GNN-based approach to predict the energy performance for floor plan customization (energy-aware design). The approach lays the foundation for a performance-aware generative design using GNN. The results show that the GNN can achieve high accuracy in energy performance prediction.