The construction industry is at a critical juncture, facing both unprecedented opportunities and challenges driven by emerging technologies like BIMS-GPT, which combines Building Information Models (BIMs) with Generative Pre-trained Transformer (GPT) language models. This study investigates the drivers and barriers to the adoption of BIMS-GPT in the construction industry through empirical research using questionnaires and expert interviews. The results indicate that the most significant drivers are BIMS-GPT’s ability to automate tasks, enhance decision-making, improve safety, and optimize processes across the entire building lifecycle. The main barriers include high development and training costs, lack of legal frameworks, data security concerns, and resistance to change among employees. Furthermore, the study analyzes differences in perceptions among respondents based on their years of experience and departmental roles. Those with 6-10 years of experience show the highest interest in adopting BIMS-GPT, while management and technical departments prioritize different aspects of the technology. The findings provide valuable insights for construction companies looking to implement BIMS-GPT and establish a solid foundation for its promotion and implementation. By understanding stakeholders’ attitudes and addressing the identified drivers and barriers, the industry can fully leverage the potential of this transformative technology, paving the way for a smarter, more efficient, and sustainable future in construction.

Sustainability in the construction industry entails measures to reduce the environmental impact of construction projects, achieve economic viability, and ensure a livable environment before, during, and after construction. However, construction activities generate substantial pollution, including dust and noise, and given their large scale, long duration, and involvement of multiple public service departments, considerable information is produced throughout each project. Traditional approaches to pollution control have proven largely ineffective, whereas platform-based models have demonstrated efficiency and cost-effectiveness in many sectors. This study therefore explores the digital transformation of public service governance for construction pollution through a platform-based approach. Timed Petri nets are employed to model and analyze both traditional and platform governance modes, providing a theoretical foundation for the digital upgrading of the construction industry using public data (e.g., construction pollution data). Results indicate that the platform governance mode reduces governance time by over 40% and substantially enhances information exchange efficiency compared with the traditional mode. By contrasting time delays between the two approaches, the superior efficiency of the platform model is highlighted. The paper further summarizes key findings, offers recommendations for institutional support and future research, and validates the proposed governance model through an empirical case study.