Modularization aims to decompose a building or system into modules of components with controlled interdependencies to allow for the parallelization of their design tasks and the transfer of the work from the construction site to an efficient offsite manufacturing environment. However, these design and construction benefits of modular building decomposition may result in increased complexities that amplify the impact of design changes. This study proposes a quantitative methodology for assessing the complexity of hybrid building modularization strategies that combine the use of volumetric and panelized modules. The methodology integrates novel graph-based modeling schema, graph algorithms, a hybrid modularization modeling approach, and a structural complexity metric. The proposed methodology was assessed using an illustrative project case. The main contributions of this study is the development of a graph-based modeling approach for hybrid modularization and a quantitative approach for assessing the complexity of modularized buildings.@en

Mobile factories promise an increased project efficiency with on-demand production and Just-in-Time delivery of prefabricated elements. However, traditional scheduling methods predominantly focus on either factory or site and neglect the factory mobility, often leading to suboptimal synchronization. To address this gap, this paper introduces a novel reinforcement learning (RL)-based model for optimizing the operational policy of mobile factories in infrastructure projects. The developed model simultaneously schedules on-site and off-site operations, effectively integrating the performance metrics at the project level. Utilizing RL, the factory's production management system continuously learns and adjusts in response to real-time project developments, ensuring optimal decision-making regarding scheduling and resource allocation.@en

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.@en

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.@en

The use of prefabricated modules can benefit the construction industry with the economy of scales and production efficiency. However, the existing approach to develop module libraries is project-based, lacking the potential to reuse and manage in future projects. By taking the repeatability and manufacturability into account, this paper proposes a graph-based framework to identify possible modules automatically from multiple projects by frequent pattern mining. The results show that the repeated patterns share a degree of standardization and can be considered as module candidates. Finally, the framework is implemented as add-ons in the BIM environment to support module lifecycle management.@en

One emerging strategy in industrialized construction is the use of mass customization to increase product efficiency without sacrificing design flexibility. Effective implementation of mass customization can be done through a product platform, i.e. a configurator. However, existing configurators often lack integration of knowledge from the downstream supply chain such as manufacturing. This paper proposes a conceptual framework for a configurator unified through a manufacturing kit-of-parts. Kit-of-parts are pre-engineered and pre-designed digital models representing fabrication-ready components. Using production rules and restraints, accurate planning and design representations can be derived and utilized in the configurator. This research develops a configurator prototype using three-tier architecture. The prototype supports a low-to-high level of detail of kit-of-parts. It integrates product platforms and project development across multiple building phases including site planning, floor plan layout, and 3D model generation. Finally, the implementation of a modular building configurator illustrates the benefits of the proposed configurator-based workflow.@en