The early stages of building design involve the consideration of different design variants and their assessment regarding various performance criteria including energy consumption and costs. During the design process, the involved experts from different disciplines frequently exchange building information to develop a design that satisfies the project’s requirements and objectives. In the course of this iterative process, the building design evolves throughout multiple refinement stages. At the same time, different variants are developed. In BIM-based projects, the maturity of the design information provided by the model is expressed by the notion of Level of development (LOD). So far, however, there is no method to formally define the information requirements of a LOD. In particular, there are no means for expressing the uncertainty involved with the provided information. By contrast, despite the insufficient information available in early design stages, a BIM model appears precise and certain. This situation leads to false assumptions and model evaluations, for example, in the case of energy efficiency calculations or structural analysis. Hence, this paper presents an overview of a set of approaches that were developed to alleviate and preserve the consistency of the designed solutions. The approach includes the development of a multi-LOD meta-model, which allows one to explicitly describe the LOD requirements of each building component type incorporating the possible uncertainties, e.g. concerning the building dimensions. On the basis of this multi-LOD model, methods for evaluating a building design’s performance regarding the building’s structure and life cycle energy performance are proposed that take the defined uncertainties into account. To support the management of design variants in one consistent model, a graph-based approach is introduced. Finally, a minimized communication protocol is described to facilitate the workflow and communicate the evaluation results for supporting the decision-making process.

The process of designing a building involves producing design concepts while fulfilling various requirements and regulations. Furthermore, during the project's life-cycle, multiple experts from multiple domains collaborate in developing the different partial models, including architectural, structural, and HVAC among others. Accordingly, clearly communicating the rationale behind design decisions is crucial for developing regulatory compliant designs that also fit the owner’s needs. The developed designs are the main deliverables exchanged and handed over. However, these deliverables do not include any explanation of design intentions or documentation of design decisions. Communication among parties and reuse of knowledge are hindered by the absent explanation of existing design. To overcome this deficiency, this paper proposes a methodology for digitally documenting design decisions, incorporating their intention and rationale. Architectural concepts and evaluation criteria are represented in the form of explanation tags as well as spatial and semantic constraints, which are assigned to the individual model elements and properties. Additionally, to document how design decisions fulfill owner requirements and regulatory documents, natural language processing (NLP) is employed to facilitate querying those documents and then the individual requirements are linked to specific elements, properties, and constraints. To evaluate the proposed methodology, a prototype was implemented as a plugin inside a BIM- Authoring tool and multiple real-world use cases are discussed.

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.