Titolo della tesi: Enhancing the Project/Process Information Management through BPMN-BIM Integration and AI
This dissertation investigates the integration of digital technologies in reshaping the management-design relationship within the Architecture, Engineering, Construction, and Operations (AECO) sector. As projects grow increasingly complex, the persistent disconnect between design and project management—exacerbated by fragmented tools, incompatible data formats and resistance to change—has resulted in inefficiencies, miscommunication, and suboptimal outcomes. This research explores the potential of integrating Business Process Modeling Notation (BPMN) with Building Information Modeling (BIM) to create a framework that may enhance collaboration, communication, and decision-making among project stakeholders.
The findings suggest that BPMN-BIM integration could help address the industry's longstanding challenges of interoperability and rigid file formats by establishing a synergistic relationship that enables information flow between process-oriented tasks and data-rich BIM environments. Through a concrete delivery case study, the research indicates how this integration approach shows potential for facilitating communication, process automation, and dynamic data exchange, offering possibilities for transforming traditionally static file formats into more interactive information sources that could help reduce data silos. Moreover, the integration appears to enhance constructability and aspects of the design process by creating feedback loops that may allow for more informed decision-making earlier in the project lifecycle.
While identifying potential benefits, the research also acknowledges implementation challenges that must be addressed for wider industry adoption, including software compatibility issues and required upfront investments. The study concludes by exploring the potential of AI-driven tools in further enhancing this framework, specifically in generating BPMN models from textual descriptions. Despite current limitations in AI capabilities, the research contributes to addressing critical interoperability, collaboration, and integration challenges, offering a foundation for steering the AECO sector toward a potentially more integrated, efficient, and technologically advanced future.