Feng Fu

Dr. Feng Fu is a Senior Lecturer at the School of Mathematics, Computer Science and Engineering, City, University of London. He serves as Editor-in-Chief of the Proceedings of the Institution of Civil Engineers - Structures and Buildings and holds leadership roles in professional organizations like the Institution of Structural Engineers and American Society of Civil Engineers (ASCE). His career blends academic research, industry consultancy, and code development, including contributions to ASCE standards on blast protection and disproportionate collapse mitigation.

• MSc in Software Engineering (University of Oxford), MBA (University of Manchester), PhD in Structural Engineering (University of Leeds), and BSc/MSc in Structural Engineering (Beijing University of Technology).
• Worked at WSP Group Ltd, Waterman Plc, and Beijing Institute of Architectural Design and Research, contributing to iconic projects like The Shard and Nakheel Tower.

His research focuses on progressive collapse, structural fire analysis, tensegrity structures, and steel composite joints, with recent interdisciplinary work on machine learning and IoT applications in structural design. He has authored 160+ peer-reviewed papers, four books, and developed the first numerical procedure for progressive collapse analysis used in major projects.

The 15 most recent articles span diverse topics including biomaterials for diabetic wound healing, seismic performance of composite structures, advanced sensors for structural monitoring, and multi-hazard analysis, reflecting his interdisciplinary expertise.

• Scientific Awards: Excellence in Teaching (2016), IStructE Young Researchers' Competition (2005), WUN Award (2003).
• Fellowships: Institution of Structural Engineers (2019–present), American Society of Civil Engineers (2020–present), Institution of Civil Engineers (2020–present), Higher Education Academy (2012–present).

He supervises research students working on topics like progressive collapse under multi-hazards, FRP-reinforced concrete, and machine learning applications in structural analysis. His lab collaborates on international projects, including EU-funded initiatives and multi-institutional studies on structural resilience.