This study aimed to ascertain the research status of complexity issues in building information modeling (BIM) diffusion and identify future research directions in this field. A total of 366 relevant journal articles were holistically evaluated. The visualization analysis indicated that management aspects, emergent trends (such as green building, facility management, and automation), and theme clusters (such as interoperability, waste management, laser scanning, stakeholder management, and energy efficiency) are shaping BIM research towards complexity. Areas such as supply chain, cost, digital twin, and web are also essential. The manual qualitative evaluation classified the complexity issues in BIM diffusion research into three types (complexities of network-based BIM evolution, impact of BIM adoption circumstances, and BIM-based complexity reduction for informed decision making). It was concluded that BIM has been shifting towards information models and systems-based life cycle management, waste control for healthy urban environments, and complex data analysis from a big data perspective, not only in building projects but also in heritage and infrastructure, or at the city scale, for informed decision making and automatic responses. Future research should investigate the co-evolution between collaborative networks and BIM artefacts and work processes, quality improvement of BIM-based complex networks, BIM post-adoption behaviors influenced by complex environmental contexts, and BIM-based complexity reduction approaches.

It is well-known that the HVAC (heating, ventilation and air conditioning) dominates electricity consumption in commercial buildings. In this paper, we focus on one of the core problems in building operation, namely chiller sequencing to reduce HVAC electricity consumption. Our contributions are threefold. First, we make a case for why it is important to quantify the performance profile of a chiller, namely coefficient of performance (COP), at run-time, by developing a data-driven COP estimation methodology. Second, we show that predicting COP accurately is a non-trivial problem, requiring considerable computation time. To overcome this barrier, we develop a data-driven COP prediction model and an edge-based chiller sequencing framework integrating the COP predictions, and show that they strike a good balance between electricity saving and ease of use for real-world deployment. Finally, we evaluate the performance of our scheme by applying it to real-world data, spanning 4 years, obtained from multiple chillers across 3 large commercial buildings in Hong Kong. The results show an electricity saving of over 30% compared to baselines. We offer our edge based data-driven chiller sequencing framework as a cost-effective and practical mechanism to reduce electricity consumption associated with HVAC operation in commercial buildings.