Advancements in Multi-Scale Sensing, Simulation, and Prediction of Urban Resilience from Infrastructure to City Level

Urban environments depend on complex assemblages of infrastructure assets, interconnected networks, and socio-technical systems that sustain essential community functions. As cities face increasing exposure to natural and human-induced hazards, their capacity to withstand, adapt to, and recover from disruptions has become an urgent research priority. Recent advances in structural sensing, inspection technologies, and monitoring systems enable increasingly data-driven assessments of infrastructure condition and degradation. In parallel, developments in multi-scale modeling, simulation, and predictive analytics offer new opportunities to evaluate resilience not only at the component level, but also across networked systems and city-wide contexts. As urban systems become more integrated and interdependent, there is a growing need to link infrastructure performance with societal impacts, providing comprehensive insight into how disruptions propagate and affect safety, durability, economic stability, and community well-being.This Research Topic aims to advance knowledge, methodologies, and applications related to the sensing, simulation, and prediction of urban resilience across multiple spatial scales, from individual infrastructure components to interconnected networks and entire city systems. Key challenges include developing algorithms for infrastructure state awareness using inspection and structural health monitoring data, characterizing post-hazard changes in performance, and understanding how infrastructure degradation influences socio-economic systems and community functionality. Recent progress in multi-source data fusion, digital modeling, scenario-based simulation, and forecasting frameworks provides new pathways for quantifying resilience and assessing uncertainty in both physical and systemic responses. This Topic seeks contributions that propose analytical methods, predictive tools, and empirical studies that bridge engineering perspectives, system-level interdependencies, and urban-scale outcomes. Applied case studies and practical implementations are particularly encouraged, especially those demonstrating how resilience-informed decision-making can enhance safety, sustainability, and recovery across diverse infrastructure types and urban environments. Through this collection, we aim to foster interdisciplinary dialogue and support the development of resilient cities grounded in measurable, scalable, and data-driven evidence.We welcome original research articles, systematic reviews, case studies, and theoretical perspectives addressing (but not limited to) the following themes:• Algorithms for infrastructure state awareness based on inspection data or structural health monitoring.• Characterization of post-hazard changes in infrastructure resistance, safety, and durability.• Impacts of post-disaster infrastructure degradation on socio-economic systems and community functioning.• Quantitative frameworks and uncertainty analysis methods for assessing urban resilience.• Urban resilience prediction using multi-source data fusion, simulation, and forecasting models.• Applied case studies and practical implementations across multiple spatial scales, including individual infrastructure systems, interconnected networks, and community- or city-level contexts.Submissions should present innovative methods, analytical frameworks, or applied investigations that advance multi-scale sensing, simulation, or prediction of urban resilience. Interdisciplinary contributions are strongly encouraged, including work drawing from civil and structural engineering, urban systems science, data analytics, remote sensing, computational modeling, disaster risk studies, and resilience planning. Both methodological advances and real-world case studies are welcome, particularly those offering practical insights, cross-scale applicability, and pathways toward resilient, adaptive, and sustainable urban environments. Topic editors: Boqiang Xu, Chao Liu, Qian-Cheng Wang, Hongyuan Guo, Mingkai LI Source: https://www.frontiersin.org/research-topics/76386/advancements-in-multi-scale-sensing-simulation-and-prediction-of-urban-resilience-from-infrastructure-to-city-level