Re Cecconi Fulvio

Fulvio Re Cecconi is an expert in building technology and construction management, bringing over 15 years of field experience in roles such as project manager, planning director, and construction safety manager. Leveraging his strong industry background, he transitioned to academia, lecturing on building technology, maintenance management, and project management. His current research concentrates on Digital Asset Management, emphasising Digital Twins (DTs), Artificial Intelligence, and advanced data structures, thereby bridging the gap between industrial practice and academic innovation. From 2010 to 2020, he served as Scientific Secretary of the CIB W080 Working Commission on service life prediction. He has led and participated in multiple public and private research initiatives on whole-life building performance, building logbooks, BIM, and multi-hazard risk reduction methodologies. He has published extensively, acting as co-editor and editor of scientific journals.

Career

MsC in Civil Engineering, PhD in Building Engineering.

Research

There are currently two main active research strands. The first concerns the democratisation of digital twins through the use of generative artificial intelligence, while the second focuses on computer vision applied to construction sites. The first strand combines large language models and digital twins to modernise existing asset and facility management processes, or to create new ones, with the aim of addressing the global sustainability challenges facing the construction sector. This line of work concentrates on harmonising inherently heterogeneous data, such as real time sensor data and the static data embedded in BIM models, in order to develop digital twins that can effectively operate with small, private language models. The objective is to broaden the pool of potential users of the collected data and thereby improve as many decision making processes as possible.

The second strand employs vision language models to enhance site safety and productivity through the automatic recognition of construction activities and their associated risks. In this case, the central challenge lies in mitigating hallucinations, and this is the primary focus of the ongoing research.

Selected Publications

F. Re Cecconi, A. Khodabakhshian, L. Rampini, Building Tomorrow: Unleashing the Potential of Artificial Intelligence in Construction, 2024, Springerbriefs in Applied Sciences and Technology, Springer Cham.