As global infrastructure systems face increasing environmental, social, and operational challenges, enhancing their resilience through digital and intelligent technologies has become a strategic priority. Digital Twin (DT) and Artificial Intelligence (AI) technologies offer transformative capabilities for monitoring, predicting, and optimizing infrastructure performance under stress. However, research on their integration within resilience frameworks remains fragmented. This study presents a comprehensive bibliometric analysis to clarify how DT and AI are being applied to strengthen infrastructure resilience (IR). Using data exclusively from the Web of Science (WoS) database, co-occurrence and overlay visualizations were employed to map thematic structures, identify research clusters, and track emerging trends. The analysis revealed six interconnected research domains linking DT, AI, and resilience, including artificial intelligence and industrial applications, digital twins and machine learning, cyber–physical systems, smart cities and sustainability, data-driven resilience modeling, and methodological frameworks. Overlay mapping revealed a temporal shift from early work on sensors and cyber–physical systems toward integrated, sustainability-oriented applications, including predictive maintenance, urban digital twins, and environmental resilience. The findings underscore the need for adaptive and interoperable DT ecosystems incorporating AI-driven analytics, ethical data governance, and sustainability metrics, providing a unified foundation for advancing resilient and intelligent infrastructure systems.

Firefighters operate in high-risk, rapidly evolving environments where exposure to extreme heat, toxic gases, and physiological stress significantly increases the likelihood of injury and fatality. This study systematically maps the emerging research landscape of real-time artificial intelligence (AI)-driven digital twins for environmental and physiological risk prediction in firefighting contexts. A combined bibliometric and qualitative content analysis was conducted using peer-reviewed literature retrieved from the Web of Science database (2010–2025). Bibliometric techniques were used to identify publication trends and thematic clusters, while content analysis examined the integration of sensing technologies, AI models, and digital twin architectures. The results reveal four dominant technological domains shaping the field: AI-enabled fire risk modeling, sensor data acquisition systems, IoT-based digital infrastructures, and predictive analytics for disaster simulation. Sensing technologies such as temperature, gas, particulate matter, thermal imaging, heart rate, and blood oxygen monitoring form the foundational data layer, while machine learning and deep learning models enable real-time hazard prediction and situational awareness. Digital twin architectures serve as the integration layer, fusing multi-source data and supporting simulation-based decision-making. Despite rapid advancements, key gaps persist, including limited integration of environmental and physiological data, insufficient predictive capabilities, a lack of standardized architectures, and minimal development of human-centered decision-support systems. This study provides a structured synthesis of current technologies and identifies future research directions toward integrated, explainable, and real-time digital twin systems to enhance firefighter safety and operational resilience.

Firefighters operate in dynamic urban environments where limited real-time situational awareness can compromise emergency response and personnel safety. This study presents a digital twin-enabled architecture and proof-of-concept prototype for multimodal physiological and environmental monitoring of firefighters. The proposed architecture integrates wearable and environmental sensing, communication, data processing, digital twin representation, and decision-support components within a scalable layered framework. A Grafana-based prototype was developed using simulated physiological and environmental data streams to demonstrate multimodal data integration, threshold-based risk classification, and real-time visualization at individual and fleet levels. Risk classification was implemented using parameter-specific threshold rules rather than a trained machine learning model. At the same time, advanced AI-based prediction, anomaly detection, and adaptive response capabilities are defined as future extensions of the proposed architecture. The prototype dashboard implementation demonstrates the feasibility of the proposed system by visualizing physiological metrics, environmental exposure, geospatial information, and operational risk indicators at both individual and fleet levels. The study’s primary contribution is therefore architectural and integrative, providing a structured framework for connecting multimodal sensing, risk indication, visualization, and decision support while establishing a foundation for future development and validation of predictive digital twin systems for firefighter safety and urban emergency response.