Twin Technologies for Dam Safety and Monitoring

Ensuring the long-term safety of dams under complex environmental and operational conditions remains a critical challenge. Traditional monitoring methods often fail to capture high dimensional spatiotemporal correlations or fuse heterogeneous sensor data for early warning. Moreover, climate change is intensifying extreme hydro meteorological events, introducing new uncertainties for dam safety and downstream regional sustainability. Recent advances in artificial intelligence, deep learning, and digital twin technologies offer transformative potential. By leveraging data driven and intelligent methodologies, it is now possible to shift from reactive maintenance to proactive, intelligent safety management. Integrating high-resolution climate data and eco-hydraulic insights into monitoring frameworks further enables the coupled analysis of human activities and climate induced risks, supporting adaptive planning in highly human altered landscapes. This Research Topic aims to showcase cutting-edge approaches that enhance dam structural health monitoring while addressing broader water-resource and climate resilience challenges.Conventional dam safety monitoring largely relies on threshold‑based rules, empirical models, or simplified statistical approaches. These methods often fail to fully exploit heterogeneous sensor data, capture complex spatiotemporal dependencies, or provide early warning under evolving environmental conditions. As a result, anomaly detection remains reactive, and decision‑making is frequently delayed or uncertain. Specifically, the coupled dynamics between human activities and climate-induced extremes require adaptive planning that goes beyond conventional structural measures.This Research Topic aims to address these gaps by bringing together cutting-edge research on AI, deep learning, and digital twin technologies for dam engineering. The goal is to advance intelligent, data-driven frameworks that enable proactive safety management—moving beyond traditional monitoring toward real-time perception, automated anomaly identification, predictive modeling, and uncertainty-aware assessment. Recent progress in spatiotemporal modeling, multisource information fusion, unsupervised learning, transfer learning, computer vision, and knowledge representation offers promising pathways. This collection also encourages exploring how high-resolution climate data and eco-hydraulic principles can enhance adaptive water-resource management and regional resilience in highly human-altered basins. By addressing the coupled impacts of human activities and extreme hydro-meteorological events, we seek to support sustainable dam operation and downstream ecosystem health under a changing climate.This Research Topic welcomes original research articles, comprehensive reviews, and case studies addressing the application of AI, deep learning, and digital twin technologies to dam safety monitoring, assessment, and design. Specific themes of interest include but are not limited to:1. Intelligent data perception, cleaning, and imputation for dam monitoring systems2. Spatiotemporal predictive modeling of dam displacements, seepage, and stresses3. Unsupervised or semi-supervised anomaly detection and early warning frameworks4. Digital twin architectures for real‑time simulation, visualization, and decision support5. Ontology, knowledge graph, or BIM‑based knowledge management for dam safety6. Computer vision and deep learning for surface/underwater defect detection and quantification7. Transfer learning, few-shot learning, or physics‑informed neural networks for dam applications8. Integration of high-resolution climate data and eco-hydraulic modeling to address climate‑induced risks and adaptive planning in human‑altered landscapes9. Coupled human-water systems analysis under extreme hydrological events for regional sustainabilityManuscript types accepted: Original Research, Review, Mini Review. Submissions must present novel contributions with rigorous validation and clear relevance to dam engineering practice. Topic editors: Xiaosong Shu, Yuhang Zhou, Jialin Chen, Yangtao Li Source: https://www.frontiersin.org/research-topics/81286/twin-technologies-for-dam-safety-and-monitoring