Advancing bridge management systems: Optimizing maintenance planning through deep learning, big data, and digital twins

• Comprehensive review of recent advancements in BMS integrating DTs, DL and BD. • Identification and classification of key sources of uncertainty across BMS modules and strategies for their management. • Critical analysis of DT and DL-based frameworks for reliability enhancement and informed decision-making. • Discussion of research gaps and opportunities for integrating advanced technologies into bridge maintenance and monitoring. • Emphasis on future directions toward intelligent, adaptive, and uncertainty-aware bridge management systems Bridge infrastructure, as a critical capital asset and an integral component of transportation networks, is increasingly affected by aging, deterioration, and external damage, potentially compromising its safety, performance, and functionality. These challenges, compounded by the growing demand for infrastructure expansion and limited funding resources, underscore the necessity of adopting efficient bridge management systems to prioritize maintenance and remediation strategies. To address these demands, different Bridge Management Systems (BMSs) have been developed to support operators in maintaining safe operations while optimizing budget allocation and maintenance strategies. Despite advancements in this field, most state-of-the-art research lacks a comprehensive overview of the role of Digital Twins (DTs), Deep learning (DL) and Big Data (BD) integration in enhancing BMS, particularly in managing uncertainties across BMS modules. Therefore, this paper provides a dedicated review of recent research in BMSs, with particular emphasis on the integration of DT and DL-enabled frameworks into bridge management practices and organizes key sources of uncertainty and discusses how emerging technologies can address these aspects in BMSs, which has yet to be comprehensively addressed. The review critically explores current methodologies, highlights challenges and identifies opportunities for uncertainty management in BMSs through the integration of advanced technologies and future trends for more reliable bridge management.