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| Format: | Recurso digital |
| Language: | English |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.20370291 |
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| _version_ | 1866902122758406144 |
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| author | Baladi, Samir |
| author_facet | Baladi, Samir |
| contents | <p>DAMS-SLIP (Dynamic AI-Augmented Monitoring System for Seepage, Limit-State Integrity, and Piping) is an integrated geotechnical safety framework designed for real-time monitoring and predictive analysis of earth-fill dam stability. The system couples physics-based hydro-mechanical modeling with AI-driven modules including convolutional neural networks, physics-informed neural networks, and gradient boosting ensembles.<br>The framework unifies seepage mechanics, slope stability analysis, and hydraulic gradient enforcement into a single continuous governance architecture. It introduces three core constructs: SMEC (Seepage Mechanics and Continuity Engine), GSSE (Geotechnical Slip Stability Evaluator), and HGCL (Hydraulic Gradient Consistency Lock).<br>DAMS-SLIP provides early warning capabilities for internal erosion and piping initiation, delivering quantified safety metrics such as Seepage Containment Index (SCI), factor of safety (Fₛ), and AI-based lead time prediction. The system has been validated across multiple canonical dam failure scenarios including homogeneous embankments, zoned dams, rapid drawdown, and seismic coupling conditions.<br>The framework aims to enhance dam safety management through AI-augmented decision support and physically constrained modeling.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20370291 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DAMS-SLIP : AI-Augmented Earth-Fill Dam Safety System Baladi, Samir Geotechnical Engineering Civil and Environmental Engineering Engineering Structural Safety Engineering Hydro-Mechanical Systems Physical Sciences and Mathematics Artificial Intelligence and Robotics Civil engineering Computational Mechanics DAMS-SLIP dam safety seepage modeling piping earth-fill dams AI monitoring PINN CNN XGBoost hydro-mechanics structural integrity real-time systems <p>DAMS-SLIP (Dynamic AI-Augmented Monitoring System for Seepage, Limit-State Integrity, and Piping) is an integrated geotechnical safety framework designed for real-time monitoring and predictive analysis of earth-fill dam stability. The system couples physics-based hydro-mechanical modeling with AI-driven modules including convolutional neural networks, physics-informed neural networks, and gradient boosting ensembles.<br>The framework unifies seepage mechanics, slope stability analysis, and hydraulic gradient enforcement into a single continuous governance architecture. It introduces three core constructs: SMEC (Seepage Mechanics and Continuity Engine), GSSE (Geotechnical Slip Stability Evaluator), and HGCL (Hydraulic Gradient Consistency Lock).<br>DAMS-SLIP provides early warning capabilities for internal erosion and piping initiation, delivering quantified safety metrics such as Seepage Containment Index (SCI), factor of safety (Fₛ), and AI-based lead time prediction. The system has been validated across multiple canonical dam failure scenarios including homogeneous embankments, zoned dams, rapid drawdown, and seismic coupling conditions.<br>The framework aims to enhance dam safety management through AI-augmented decision support and physically constrained modeling.</p> |
| title | DAMS-SLIP : AI-Augmented Earth-Fill Dam Safety System |
| topic | Geotechnical Engineering Civil and Environmental Engineering Engineering Structural Safety Engineering Hydro-Mechanical Systems Physical Sciences and Mathematics Artificial Intelligence and Robotics Civil engineering Computational Mechanics DAMS-SLIP dam safety seepage modeling piping earth-fill dams AI monitoring PINN CNN XGBoost hydro-mechanics structural integrity real-time systems |
| url | https://doi.org/10.5281/zenodo.20370291 |