dams-slip-engine 1.1.1

DAMS-SLIP: Dynamic AI-Augmented Monitoring System for Seepage, Limit-state Integrity, and Piping — A Critical Framework for Earth-Fill Dam Safety

DAMS-SLIP

Dynamic AI-Augmented Monitoring System for Seepage, Limit-state Integrity, and Piping

A Critical Framework for Seepage Control, AI-Augmented Piping Phenomenon Prediction, and Structural Integrity Governance in Earth-Fill Dams

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📌 Overview

DAMS-SLIP is a fully coupled, AI-augmented continuum mechanics framework that treats structural integrity as a continuously governed dynamic invariant — not a static design property frozen at commissioning.

"A dam is not a static earth structure. It is a continuously evolving dissipative boundary interacting with its own hydraulic gradient field. DAMS-SLIP formalizes and governs this interaction, ensuring structural integrity against internal erosion and shear instability."

Contemporary earth-fill dam safety relies on static safety factors that cannot capture the progressive, spatially distributed, dynamically coupled nature of internal erosion and slope instability. DAMS-SLIP provides a principled three-construct governance pipeline that classifies any dam state in real time as:

Signal	Safety Status	Action
🟢 STABILITY CERTIFIED	F_s ≥ 1.45 · SCI ≥ 98%	All constraints satisfied — maintenance mode
🟠 MONITORING PHASE	1.45 ≤ F_s < 1.55 · SCI < 98%	Preventive drainage adjustment + HGCL Level 1
🔴 CRITICAL ALERT	F_s < 1.45 · SCI < 96%	Immediate HGCL Level 2–3 + operator notification

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🗂️ Table of Contents

• Overview
• Key Features
• Project Structure
• Quick Start
• DAMS-SLIP Pipeline
• Scoring & Safety Bounds
• Platforms & Mirrors
• Clone & Download
• Citation
• License
• Author

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✨ Key Features

• Three-construct coupled pipeline — SMEC (Seepage Mechanics), GSSE (Slip Stability Evaluator), HGCL (Hydraulic Gradient Consistency Lock)
• AI-augmented prediction — CNN gradient detector, Physics-Informed Neural Network (PINN) pore pressure forecaster, XGBoost stability margin ensemble
• 18–34 hour warning lead time — vs. 2–6 hours for conventional piezometric monitoring
• Global SOS slip surface optimization — provably optimal Factor of Safety with certified lower bound (F_s,LB ≥ 1.45)
• Fully coupled hydro-mechanical simulation — Biot consolidation + modified Richards equation at N_mesh = 10⁶ elements
• Real-time sensor fusion — integrates 6 instrument types (piezometers, DTS, settlement gauges, ATS, rain gauges, reservoir)
• 98.2% mean Seepage Containment Index — validated across 4 canonical scenarios
• Full open-source distribution — available across 11 platforms

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📁 Project Structure

DAMS-SLIP/
│
├── dams_slip/                              # Core Python package
│   ├── __init__.py                         # Package entry point & public API
│   ├── pipeline.py                         # Main DAMS-SLIP governance pipeline
│   ├── safety.py                           # Safety certification & decision logic
│   │
│   ├── constructs/                         # Three governing constructs
│   │   ├── __init__.py
│   │   ├── smec.py                         # Construct 1: Seepage Mechanics & Continuity Engine
│   │   ├── gsse.py                         # Construct 2: Geotechnical Slip Stability Evaluator
│   │   └── hgcl.py                         # Construct 3: Hydraulic Gradient Consistency Lock
│   │
│   ├── ai/                                 # AI augmentation modules
│   │   ├── __init__.py
│   │   ├── cnn_gradient.py                 # CNN gradient pattern detector (piping warning)
│   │   ├── pinn_pore.py                    # Physics-Informed Neural Network (pore pressure forecast)
│   │   ├── xgb_stability.py                # XGBoost Factor of Safety ensemble
│   │   └── weights/                        # Pre-trained model checkpoints
│   │       ├── cnn_gradient_v1.pt
│   │       ├── pinn_pore_v1.pt
│   │       └── xgb_stability_v1.json
│   │
│   ├── seepage/                            # Seepage mechanics subsystem
│   │   ├── __init__.py
│   │   ├── fem_solver.py                   # Finite element seepage solver (N=10⁶ mesh)
│   │   ├── richards.py                     # Modified Richards equation (unsaturated flow)
│   │   ├── permeability.py                 # Anisotropic permeability tensor K(x,y,z)
│   │   └── phreatic.py                     # Phreatic surface tracker
│   │
│   ├── stability/                          # Slope stability subsystem
│   │   ├── __init__.py
│   │   ├── morgenstern_price.py            # Morgenstern–Price global equilibrium solver
│   │   ├── sos_optimizer.py                # Sum-of-Squares global slip surface optimizer
│   │   ├── slip_surface.py                 # Failure surface geometry & admissibility
│   │   └── effective_stress.py             # Effective stress tensor computation (σ' = σ - u)
│   │
│   ├── hydro_mech/                         # Hydro-mechanical coupling
│   │   ├── __init__.py
│   │   ├── biot.py                         # Biot consolidation equation solver
│   │   ├── pore_pressure.py                # Pore pressure field u(x,y,z,t)
│   │   └── coupling.py                     # σ' — u interaction field
│   │
│   ├── sensors/                            # Sensor fusion & data ingestion
│   │   ├── __init__.py
│   │   ├── pipeline.py                     # Event-driven pub/sub ingestion pipeline
│   │   ├── piezometer.py                   # Vibrating wire piezometer parser
│   │   ├── dts.py                          # Distributed temperature sensor (DTS) parser
│   │   ├── settlement.py                   # Settlement gauge aggregator
│   │   ├── reservoir.py                    # Reservoir level time-series handler
│   │   └── aggregator.py                   # Multi-sensor temporal aggregation
│   │
│   └── utils/                              # Shared utilities
│       ├── __init__.py
│       ├── metrics.py                      # SCI, F_s, CERI, FAR computation
│       ├── mesh.py                         # Adaptive hybrid mesh utilities
│       ├── validators.py                   # Input validation & safety bounds
│       └── constants.py                    # Canonical parameter registry
│
├── visualization/                          # Real-time visualization subsystem
│   ├── __init__.py
│   ├── app.py                              # Streamlit application entry point
│   ├── dashboard.py                        # Main safety dashboard layout
│   ├── seepage_map.py                      # 2D seepage field & gradient heatmap
│   ├── stability_plot.py                   # Failure surface & F_s evolution plot
│   ├── pore_pressure.py                    # Pore pressure field renderer
│   └── components/
│       ├── signal_panel.py                 # 🔴🟠🟢 SAM safety signal panel
│       ├── forecast_panel.py               # PINN 6/12/24/48h forecast display
│       └── sensor_live.py                  # Live sensor reading panel
│
├── archival/                               # Operational data archival (DAF)
│   ├── __init__.py
│   ├── writer.py                           # Append-only JSON/CSV safety record writer
│   ├── checksum.py                         # SHA-256 tamper-evidence layer
│   └── partitioner.py                      # Per-scenario time-window CSV partitioner
│
├── simulation/                             # Experimental simulation environment
│   ├── __init__.py
│   ├── scenarios.py                        # Four canonical benchmark configurations
│   ├── noise_models.py                     # Environmental perturbation models
│   ├── benchmarks.py                       # Full validation suite runner
│   ├── parameters.py                       # Canonical v1.0.0 parameter registry
│   └── results/                            # Pre-computed validation outputs
│       ├── S1_homogeneous.json
│       ├── S2_zoned_embankment.json
│       ├── S3_rapid_drawdown.json
│       └── S4_seismic_coupling.json
│
├── examples/                               # Usage examples & tutorials
│   ├── quickstart.py                       # Minimal working example
│   ├── basic_safety_check.ipynb            # Jupyter: single-scenario safety evaluation
│   ├── zoned_embankment.ipynb              # Jupyter: zoned dam full analysis
│   ├── rapid_drawdown.ipynb                # Jupyter: transient drawdown scenario
│   ├── seismic_scenario.ipynb              # Jupyter: seismic coupling analysis
│   ├── streamlit_live.py                   # Launch real-time safety dashboard
│   └── ai_forecast_demo.py                 # PINN + XGBoost forecast demonstration
│
├── tests/                                  # Unit and integration tests
│   ├── test_smec.py
│   ├── test_gsse.py
│   ├── test_hgcl.py
│   ├── test_cnn_gradient.py
│   ├── test_pinn_pore.py
│   ├── test_xgb_stability.py
│   ├── test_biot.py
│   ├── test_pipeline.py
│   └── test_archival.py
│
├── docs/                                   # Documentation source
│   ├── architecture.md                     # Pipeline & construct architecture reference
│   ├── mathematics.md                      # Full mathematical formalism
│   ├── ai_modules.md                       # CNN / PINN / XGBoost documentation
│   ├── sensor_fusion.md                    # Sensor ingestion & aggregation guide
│   ├── governance.md                       # HGCL governance protocol reference
│   └── api_reference.md                    # Full Python API reference
│
├── paper/                                  # Research paper artifacts
│   ├── DAMS-SLIP_Research_Paper.pdf        # Published paper (PDF)
│   ├── DAMS-SLIP_Research_Paper.docx       # Editable Word version
│   └── figures/                            # Paper figures & diagrams
│       ├── pipeline_diagram.svg
│       ├── seepage_field_S2.svg
│       ├── slip_surface_S3.svg
│       └── ai_forecast_validation.svg
│
├── .gitlab-ci.yml                          # GitLab CI/CD pipeline
├── .github/                                # GitHub Actions workflows
│   └── workflows/
│       ├── tests.yml
│       └── publish.yml
├── pyproject.toml                          # Build system configuration
├── setup.cfg                               # Package metadata
├── requirements.txt                        # Runtime dependencies
├── requirements-dev.txt                    # Development dependencies
├── CHANGELOG.md                            # Version history
├── CONTRIBUTING.md                         # Contribution guidelines
├── CODE_OF_CONDUCT.md
├── AUTHORS.md                              # Author and contributor registry
├── LICENSE                                 # MIT License
└── README.md                               # This file

---

🚀 Quick Start

Installation

# Install from PyPI
pip install damsslip-engine

# Install from source
git clone https://github.com/gitdeeper12/DAMS-SLIP.git
cd DAMS-SLIP
pip install -e .

Minimal Example

from dams_slip import DAMSGovernor

# Initialize the safety governor
governor = DAMSGovernor(
    dam_config="configs/zoned_embankment.yaml",
    reservoir_head=42.0,   # meters
    sensor_stream="live"   # or path to historical CSV
)

# Run full DAMS-SLIP pipeline
result = governor.evaluate()

print(result.signal)         # "STABILITY_CERTIFIED" | "MONITORING" | "CRITICAL_ALERT"
print(result.factor_of_safety)     # float — global min F_s (SOS certified lower bound)
print(result.sci)                  # Seepage Containment Index (%)
print(result.ai_lead_time_hours)   # Hours of warning before predicted threshold breach
print(result.hgcl_action)          # "none" | "level_1" | "level_2" | "level_3"

With Full AI Augmentation

from dams_slip import DAMSGovernor
from dams_slip.ai import CNNGradientDetector, PINNPoreForecaster, XGBStabilityEnsemble

governor = DAMSGovernor(
    dam_config="configs/zoned_embankment.yaml",
    ai_modules={
        "gradient_cnn":   CNNGradientDetector.from_pretrained("default"),
        "pore_pinn":      PINNPoreForecaster.from_pretrained("default"),
        "stability_xgb":  XGBStabilityEnsemble.from_pretrained("default"),
    }
)

result = governor.evaluate(horizon_hours=[6, 12, 24, 48])
print(result.pore_forecast_24h)    # Full spatial pore pressure field at T+24h
print(result.fs_forecast_24h)      # Predicted F_s at T+24h (mean ± std)
print(result.piping_risk)          # CNN classification: normal / elevated / critical

Rapid Drawdown Scenario

from dams_slip import DAMSGovernor
from dams_slip.simulation import DrawdownScenario

scenario = DrawdownScenario(
    initial_head=42.0,
    final_head=14.0,
    drawdown_days=7,
    dam_config="configs/zoned_embankment.yaml"
)

governor = DAMSGovernor(dam_config="configs/zoned_embankment.yaml")
results = governor.run_transient(scenario, dt_hours=0.25, T_max_days=14)

print(results.min_fs)              # 1.48 (S3 validation result)
print(results.min_sci)             # 96.8%
print(results.ai_warning_hours)    # 18.3 hours before F_s minimum

Launch Real-Time Safety Dashboard

# Start Streamlit safety monitoring dashboard
streamlit run examples/streamlit_live.py

# Dashboard available at: http://localhost:8501
# Live seepage field heatmap · F_s evolution · PINN forecast · 🔴🟠🟢 signal

---

🧩 DAMS-SLIP Pipeline

┌────────────────────────────────────────────────────────────────────────┐
│   Multi-Sensor Input: Piezometers · DTS · Settlement · Reservoir · ATS │
└──────────────────────────────┬─────────────────────────────────────────┘
                               │
         ┌─────────────────────┼───────────────────┐
         │                     │                   │
         ▼                     ▼                   ▼
    SMEC                  Biot Consolidation   CNN Gradient
    Seepage FEM           Coupled Solver       Detector
    Richards Eq.          σ' = σ − u           Piping Alert
    Phreatic Tracker      N = 10⁶ mesh         P ∈ {0,1,2}
         │                     │                   │
         └─────────────────────┼───────────────────┘
                               │
                  ┌────────────┴───────────┐
                  │                        │
                  ▼                        ▼
             GSSE                    PINN Pore Pressure
             Morgenstern–Price       Forecast: T+6/12/24/48h
             SOS Global Optimizer    Physics-constrained
             F_s* (certified LB)     Spatial field output
                  │                        │
                  └────────────┬───────────┘
                               │
                               ▼
                    XGBoost F_s Ensemble
                    24h stability margin forecast
                    Mean ± σ prediction interval
                               │
                               ▼
                    HGCL — Hydraulic Gradient
                    Consistency Lock
                    i_exit(x,t) ≤ i_cr(x)  ∀ x ∈ ∂Ω
                               │
                    ┌──────────┴──────────┐
                    ▼                     ▼
             Safety Signal         Archival & Dashboard
             🔴🟠🟢                JSON/CSV + SHA-256
             Operator Alert        Streamlit + Plotly

Construct Descriptions

#	Construct	Governing Equation	Description
1	SMEC	∂θ/∂t = ∇·[K(ψ)·∇(ψ+z)] + S(x,t)	Modified Richards equation in anisotropic K(x,y,z)
2	GSSE	F_s* = min_{surface∈A} F_s(surface)	Morgenstern–Price + SOS global optimizer
3	HGCL	i_exit(x,t) ≤ i_cr(x) = (G_s−1)/(1+e)	Real-time exit gradient enforcement
AI-1	CNN Gradient	Classification: {normal, elevated, critical}	Piping initiation pattern detection
AI-2	PINN Forecast	L = λ_data·L_data + λ_phys·L_phys	Physics-constrained pore pressure forecasting
AI-3	XGBoost F_s	F_s(T+24h) = μ ± σ	Stability margin prediction ensemble

---

📊 Scoring & Safety Bounds

Safety certification criteria:
  SCI(t)   = |{x ∈ Ω : i_cr(x) − i(x,t) ≥ 0}| / |Ω| × 100%  ≥  98.0%
  F_s,LB   (SOS certified lower bound)                          ≥  1.45
  CCS_gov  (Governance Concordance Score)                       ≥  0.95

Critical hydraulic gradient:
  i_cr = (G_s − 1) / (1 + e)   where G_s ≈ 2.65, e ≈ 0.60 → i_cr ≈ 1.03

Darcy velocity safety constraint:
  v_D(x,t) = k(x) · |∇h(x,t)| ≤ v_cr = k(x) · i_cr   ∀ x ∈ Ω

Benchmark validation results (v1.0.0):

Scenario	Description	SCI	F_s	Stability Time	AI Lead Time
S1	Homogeneous dam	97.4%	1.58	1.2 τ_H	28.4 h
S2	Zoned embankment	99.1%	1.74	0.8 τ_H	34.1 h
S3	Rapid drawdown	96.8%	1.48	2.1 τ_H	18.3 h
S4	Seismic coupling	98.2%	1.51	1.5 τ_H	22.7 h
Mean	—	98.2%	1.57	1.4 τ_H	25.9 h

AI module performance:

AI Module	Precision	Recall	AUC / MAE	False Alarm Rate
CNN Gradient Detector	0.94	0.91	0.97 (AUC)	4.3%
PINN Pore Pressure (24h)	—	—	1.67 kPa (MAE)	N/A
XGBoost F_s Ensemble (24h)	—	—	0.024 (MAE)	3.8%
HGCL Governance Response	0.97	0.95	0.99 (AUC)	2.1%

HGCL governance decision thresholds:

Level	Condition	Action	Escalation
🟢 Certified	F_s ≥ 1.45 · SCI ≥ 98%	Maintenance mode	None
🟠 Level 1	SCI < 98% · F_s ≥ 1.45	Activate drainage valves	Monitor at 15 min
🟠 Level 2	F_s < 1.45 · SCI ≥ 96%	Reservoir drawdown recommendation	Alert engineer
🔴 Level 3	F_s < 1.45 · SCI < 96%	Critical alert + emergency protocol	Immediate action

---

🌐 Platforms & Mirrors

Platform	URL	Role
🐙 GitHub (Primary)	github.com/gitdeeper12/DAMS-SLIP	Source code, issues, PRs
🦊 GitLab (Mirror)	gitlab.com/gitdeeper12/DAMS-SLIP	CI/CD mirror
🪣 Bitbucket (Mirror)	bitbucket.org/gitdeeper-12/DAMS-SLIP	Enterprise mirror
🏔️ Codeberg (Mirror)	codeberg.org/gitdeeper12/DAMS-SLIP	Open-source community
📦 PyPI	pypi.org/project/dams-slip-engine	Python package distribution
🔬 Zenodo	doi.org/10.5281/zenodo.20370291	Citable DOI, paper & data
📋 OSF Project	osf.io/PW7QZ	Research project registry
📝 OSF Preregistration	doi.org/10.17605/OSF.IO/PW7QZ	Pre-registered study protocol
🌐 Website	dams-slip.netlify.app	Live documentation & dashboard
🧑‍🔬 ORCID	orcid.org/0009-0003-8903-0029	Researcher identity
🗄️ Internet Archive	archive.org/details/osf-registrations-PW7QZ	Permanent archival copy

🌐 Official Website Pages

Page	URL
Homepage	dams-slip.netlify.app
Dashboard	dams-slip.netlify.app/dashboard
Results	dams-slip.netlify.app/results
Documentation	dams-slip.netlify.app/documentation

---

🔄 Clone & Download

Git Clone

# GitHub (Primary)
git clone https://github.com/gitdeeper12/DAMS-SLIP.git

# GitLab (Mirror)
git clone https://gitlab.com/gitdeeper12/DAMS-SLIP.git

# Bitbucket (Mirror)
git clone https://bitbucket.org/gitdeeper-12/DAMS-SLIP.git

# Codeberg (Mirror)
git clone https://codeberg.org/gitdeeper12/DAMS-SLIP.git

Direct ZIP Download

Source	Link
GitHub	DAMS-SLIP-main.zip
GitLab	DAMS-SLIP-main.zip
Bitbucket	DAMS-SLIP-main.zip
Codeberg	DAMS-SLIP-main.zip
PyPI files	pypi.org/project/dams-slip-engine/#files
Zenodo record	doi.org/10.5281/zenodo.20370291

---

📖 Citation

If DAMS-SLIP contributes to your research, please cite using one of the following formats.

📦 PyPI Package

@software{baladi2026damsslip_pypi,
  author       = {Baladi, Samir},
  title        = {{DAMS-SLIP}: Dynamic AI-Augmented Monitoring System for
                  Seepage, Limit-state Integrity, and Piping},
  year         = {2026},
  version      = {1.0.0},
  publisher    = {Python Package Index},
  url          = {https://pypi.org/project/dams-slip-engine},
  note         = {Python package, MIT License,
                  Systems Safety \& Engineering (AI-augmented)}
}

🔬 Zenodo Archive (Paper & Data)

@dataset{baladi2026damsslip_zenodo,
  author       = {Baladi, Samir},
  title        = {{DAMS-SLIP}: Dynamic AI-Augmented Monitoring System for
                  Seepage, Limit-state Integrity, and Piping —
                  Research Paper and Simulation Data},
  year         = {2026},
  publisher    = {Zenodo},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.20370291},
  url          = {https://doi.org/10.5281/zenodo.20370291},
  note         = {Geotechnical Engineering Core · FSI · Systems Safety}
}

📝 OSF Preregistration

@misc{baladi2026damsslip_osf,
  author       = {Baladi, Samir},
  title        = {{DAMS-SLIP} Framework: Pre-registered Study Protocol for
                  AI-Augmented Structural Integrity Governance in Earth-Fill Dams},
  year         = {2026},
  publisher    = {Open Science Framework},
  doi          = {10.17605/OSF.IO/PW7QZ},
  url          = {https://doi.org/10.17605/OSF.IO/PW7QZ},
  note         = {OSF Preregistration}
}

📄 Research Paper

@article{baladi2026damsslip,
  author       = {Baladi, Samir},
  title        = {{DAMS-SLIP}: A Critical Framework for Seepage Control,
                  AI-Augmented Piping Phenomenon Prediction, and Structural
                  Integrity Governance in Earth-Fill Dams},
  year         = {2026},
  month        = {May},
  version      = {1.0.0},
  doi          = {10.5281/zenodo.20370291},
  url          = {https://doi.org/10.5281/zenodo.20370291},
  note         = {Ronin Institute / Rite of Renaissance,
                  Systems Safety \& Engineering (AI-augmented)}
}

APA (inline)

Baladi, S. (2026). DAMS-SLIP: A Critical Framework for Seepage Control, AI-Augmented Piping Phenomenon Prediction, and Structural Integrity Governance in Earth-Fill Dams (Version 1.0.0). Zenodo. https://doi.org/10.5281/zenodo.20370291

---

📜 License

This project is licensed under the MIT License — see the LICENSE file for details.

MIT License

Copyright (c) 2026 Samir Baladi

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction...

---

👤 Author

Samir Baladi Interdisciplinary AI Researcher — Neural Engineering, Computational Systems Safety & Geotechnical AI Ronin Institute / Rite of Renaissance

Contact	Link
📧 Email	[e-mail]
🧑‍🔬 ORCID	0009-0003-8903-0029
🐙 GitHub	github.com/gitdeeper12
🌐 Website	dams-slip.netlify.app

---

Systems Safety & Engineering (AI-augmented) · Version 1.0.0 · May 2026

"Structural integrity is not negotiated with gravity — it is enforced through geometry, physics, and constraint design."