The construction industry has long been labor-intensive, with slow productivity growth and a significant environmental impact. In this regard, the ever-increasing practices of additive manufacturing (AM) in construction have presented a variety of advantages and are deemed one of the critical technologies for the concept of Construction 4.0. Building information modeling (BIM) as an enabler for the digital transformation in the architecture, engineering, and construction (AEC) domain provides a framework for considering novel AM methods during the early stages of architectural design. It is known that decisions during early design stages significantly impact the subsequent planning and construction phases, whereas missing AM knowledge by architects and engineers could in turn impede the adoption of AM technologies when the early determination of appropriate manufacturing methods needs to be made. Meanwhile, the early stages of architectural design are characterized by vagueness, uncertainty, and incompleteness, which have to be clarified iteratively by both architects and domain experts. To this end, this paper introduces a knowledge-driven design decision support that prospectively incorporates an adaptive feedback mechanism under the BIM methodology. As such, architects can be assisted in choosing appropriate construction methods during the early stages of architectural design.

Advanced construction techniques, such as additive manufacturing (AM) and modular construction, offer promising solutions to address labor shortages, reduce CO₂ emissions, and enhance material efficiency. Despite their potential, the adoption of these innovative methods is hindered by the construction industry’s fragmented expertise. Building Information Modeling (BIM) is frequently suggested to integrate this diverse knowledge, but existing BIM-based approaches lack a robust framework for systematically documenting and retrieving the cross-domain knowledge essential for construction projects. To bridge this gap, this paper presents an ontology-driven methodology for documenting and utilizing expert knowledge, with a focus on AM in construction. Based on a well-founded ontological framework, a set of modular ontologies is formalized for individual domains. Additionally, a prototypical documentation tool is developed to elevate recorded information and BIM models as a knowledge graph. This knowledge graph will interface with advanced large language models (LLMs), enabling effective question answering and knowledge retrieval.

Additive Manufacturing (AM) technologies have great potential to promote sustainable development in the architecture, engineering, and construction (AEC) domain. But the inherent complexity of AM and lack of domain knowledge hinder decisions about appropriate construction methods. With state-of-the-art Semantic Web technologies, a knowledge base regarding AM technologies can be formalized and integrated into the Building Information Modeling (BIM) methodology. To this end, this paper demonstrates how a Design Decision Support System (DDSS) utilizes formal knowledge to assist architects in choosing the appropriate AM method by assessing the manufacturability of individual building components. By following and refining the essential activities described, we aim to provide architects with informed decision support, thus facilitating the versatile use of AM technologies in the AEC domain.