A review: digital twin application in bridge fire engineering and management

Despite the substantial danger that fires create, the implementation of digital twin (DT) technology in fire engineering has not been fully developed. Fire suppression systems do not exist in bridges while they stand as open structures that become quickly vulnerable to thermal damage and material breakdown leading to possible collapse. Existing fire safety models perform static evaluations because they do not track urgent fire propagation or structural response behaviours in real-time. This review discloses a comprehensive analysis about DT functionality within bridge fire engineering by highlighting both present obstacles alongside potential growth prospects. The proposed framework builds an improved design technology system through the combination of fire modelling with artificial intelligence (AI)-driven predictions and automatic emergency responses. Through its proposed frameworks the system allows adaptive self-learning fire safety protocols which move beyond static fire resistance standards to use dynamic data-driven methods for risk evaluation. The presented work connects absent technical knowledge in fire security system engineering by integrating a complete technical framework that improves safety resilience alongside emergency planning and protection of damaged structures in vital bridge networks. Interdisciplinary teams need to develop advanced AI and Internet of Things (IoT) with cyber-physical systems to build DT foundation for future fire safety engineering according to the research findings.