AI and Digital Twins Emerge as Key Tools for Managing Aging Bridge Networks
Researchers analyzing two decades of bridge management research identified artificial intelligence, digital twins, and machine learning as the field's fastest-growing priorities. The findings matter because aging infrastructure worldwide demands smarter maintenance strategies—and companies and agencies investing in these technologies now could gain competitive advantages in a sector facing mounting safety and cost pressures.
Originaltitel: Exploring a comprehensive knowledge map for bridge management research: a Delphi-enhanced scientometric analysis
Bridge infrastructure worldwide is aging, while demands for safety, sustainability, and cost-efficiency intensify challenges in bridge management (BM). This study develops a comprehensive knowledge map of BM through scientometric analysis, clarifying its intellectual structure, technological frontiers, and emerging themes. Using CiteSpace, 1582 peer-reviewed articles (2003-2024) from the Web of Science Core Collection were analyzed, with keywords and gaps refined via a Delphi process involving five senior experts. Scientometric techniques-including co-citation, cluster mapping, citation bursts, and keyword time-zone analysis-were employed to trace field evolution. Results reveal three stages: exploratory (2003-2009), steady growth (2010-2016), and digital transformation (2017-2024). Ten thematic clusters emerged, spanning intelligent management, hybrid decision-making, inspection technologies, and life-cycle sustainability. Research hotspots highlight digital twins, machine learning, condition assessment, and structural health monitoring. Five evidence-informed future priorities were identified: integrated AI-driven frameworks, hybrid decision models, network-level inspection planning, sustainability-oriented life-cycle assessment, and robust resilience modeling. Co-authorship and institutional collaboration networks further reveal global hubs shaping BM knowledge exchange. This study provides an updated mapping of BM, offering a structured agenda that links quantitative evidence with practical directions for advancing intelligent, sustainable, and risk-resilient BM.