VIRTUAL Lisbon Livable Cities

Abstract

The integration of Artificial Intelligence (AI) and Building Information Modeling (BIM) is transforming the Architectural, Engineering, and Construction (AEC) industry by driving innovation in design, enhancing project efficiency, and shaping smarter urban environments. This study employs a meta-synthesis approach to systematically investigate challenges in AI-BIM integration across pre- and post-implementation phases. Using MAXQDA 2020 software, qualitative data were analyzed, and key challenges visualized through heat maps. Findings highlight that pre-implementation barriers include poor data quality, technical complexities, and AI-BIM interoperability, while post-implementation challenges encompass stakeholder resistance, scalability, security, privacy, and standardization. By addressing these obstacles, AI-BIM integration can optimize design processes, enabling data-driven decision-making that enhances the functionality and sustainability of urban infrastructure. For instance, improved BIM-AI interoperability facilitates real-time design adjustments, reducing material waste and construction costs, while AI-driven analytics support resilient urban planning, making cities safer and more livable. This research provides actionable insights for researchers, architects, and policymakers to prioritize strategic solutions, such as standardized protocols and stakeholder training, to overcome integration barriers. By fostering greater adoption of AI-BIM technologies, the study contributes to advancing construction innovation and creating urban spaces that are more efficient, sustainable, and responsive to residents’ needs, aligning with global demands for smarter cities.