Framework para Digital Twins : integração entre o BIM, IoT e plataformas em nuvem para projetos de estruturas de edifícios industriais

Abstract

The management of industrial building structures' life cycle plays a pivotal role within companies, directly impacting economic, environmental, and social aspects of society. Leveraging digital tools is crucial for effective management in this context. Building Information Modeling (BIM), which integrates various technologies on cloud platforms, has emerged as a powerful solution for the construction industry. Furthermore, the rise of the Internet of Things (IoT) has amplified BIM's potential and facilitated the development of Digital Twins (DT) from project inception. Taking a holistic view, the seamless integration of these technologies promises heightened project efficiency by enabling proactive assessment and optimization of future performance. However, despite their immense potential, the combined capabilities of BIM, IoT, and DT remain underutilized due to slow implementation processes and cultural barriers within organizations. Additionally, the construction industry's overall digital maturity poses challenges in incorporating these technologies into daily operations. To address these complexities, our research introduces a Digital Twin framework grounded in Design Science Research. This framework aims to tackle real-world complexities in industrial construction. For its development, the Front-End Loading (FEL) methodology and the V-Model structure were employed in various life cycle phases. The Business Model Canvas (BMC) was adopted for problem identification and assisted in creating the Value Proposition. For its implementation, a cloud platform was integrated with BIM and IoT. However, successful implementation requires human interactions and actions, tailoring the value proposition to each business context. By embracing digital technologies, organizations can revolutionize their operations, enhance asset value, and ensure transparency, interoperability, and data reuse throughout the entire life cycle.