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Bibliographic Details
Main Author:	Baladi, Samir
Format:	Recurso digital
Language:	English
Published:	Zenodo 2026
Subjects:	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
Online Access:	https://doi.org/10.5281/zenodo.20370291
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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. 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). 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. The framework aims to enhance dam safety management through AI-augmented decision support and physically constrained modeling.

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