Dam Safety Monitoring and Management Through Artificial Intelligence Techniques
A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydraulics and Hydrodynamics".
Deadline for manuscript submissions: 20 December 2026 | Viewed by 75
Editors
Interests: numerical modeling; resilience assessment; hydraulic structures; multi‑hazard analysis; coupled simulation; digital twin; structural safety
Special Issues, Collections and Topics in MDPI journals
Interests: coupled simulation; digital twin; structural safety; dam risk management
Special Issues, Collections and Topics in MDPI journals
Interests: dam risk analysis; structural health monitoring; hydraulic structures; numerical analysis; machine learning; uncertainty qualification
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Dam safety monitoring and management are critical to ensuring the reliable operation of hydraulic infrastructure, preventing catastrophic failures, and safeguarding downstream communities. Traditional monitoring methods often rely on manual inspection and limited sensor data, which can be insufficient for real-time anomaly detection and predictive maintenance. Recent advances in artificial intelligence (AI), including machine learning, deep learning, and computer vision, offer transformative opportunities to enhance the accuracy, efficiency, and intelligence of dam safety systems. This Special Issue invites original research articles that apply AI techniques to dam safety monitoring and management. We welcome both theoretical developments and practical applications across various disciplines, aiming to bridge the gap between cutting-edge AI research and practical dam engineering needs.
Topics of interest include, but are not limited to:
- AI-based analysis of structural health data (e.g., deformation, seepage and stress);
- Automated defect detection from inspection images or videos;
- Time-series prediction of dam behavior;
- Fusion of multi-source monitoring data;
- Early warning systems;
- Uncertainty quantification;
- Risk-informed decision support.
Prof. Dr. Tongchun Li
Dr. Huijun Qi
Dr. Chaoning Lin
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- artificial intelligence
- dam safety monitoring
- machine learning
- structural health monitoring
- anomaly detection
- predictive maintenance
- data fusion
- early warning system
- risk assessment
- digital twin
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