Systems Safety & Engineering (AI-augmented) · GEOTECH-AI-02 · Version 1.0.0 · May 2026

TUNNEL-SHIELD

A Critical Framework for Loosening Pressure Control, Face Plastic Deformation Mitigation,
and Lining Structural Safety in Deep Shield Tunnels
AI-Augmented Elastoplastic Continuum Mechanics · TBM Excavation Safety Governance

"A deep shield tunnel is not a static void in rock. It is a moving boundary-value problem
embedded in a continuously evolving stress field. TUNNEL-SHIELD formalizes and governs
this evolution, enforcing structural integrity against loosening surge, face plastic collapse,
and lining buckling in real time."

↗ View on GitHub 📦 PyPI Package 🔬 Zenodo DOI 🦊 GitLab Mirror

Real-Time Safety Governance

Three-Level Governance Signal Classification

Every TBM advance increment evaluated by TUNNEL-SHIELD receives a continuous safety signal with full stress, convergence, and lining diagnostics.

F_tunnel ≥ 1.50 · TSII ≥ 0.95

🟢 STABILITY CERTIFIED

All stress redistribution, face convergence, and lining moment-thrust constraints satisfied. Full advance mode active — continuous PINN plastic zone monitoring.

1.35 ≤ F_tunnel < 1.50 · TSII ≥ 0.90

🟠 MONITORING PHASE

Plastic zone expansion or face convergence elevation detected. Level 1–2: mandatory advance rate reduction, slurry pressure increase, and ring design review issued.

F_tunnel < 1.20 · LSII < 0.10

🔴 STOP COMMAND

Safety threshold breach. Automated TBM stop. Emergency grouting protocol activated. Full diagnostic: LPEC stress field, FPSE convergence profile, LSLC ring map, PINN 48-advance forecast.

Governance Architecture

Three Modules · One Stress Field · Four AI Components

Fully coupled elastoplastic simulation augmented by real-time AI inference. No decoupled loosening-then-stability paradigm.

MODULE 01

LPEC — Loosening Pressure Evaluation Core

AEI · LTR · R_p,3D

Computes full stress redistribution tensor field in elastic and plastic annular zones. Resolves loosening pressure q_L and Arching Efficiency Index from Terzaghi + Hoek-Brown plasticity.

q_L = γ_r·B·(1−c/γ_r·B)/(K_0·tanφ)·[1−exp(−K_0·tanφ·H/B)]
F_LPEC = σ_cm / (σ_0 − p_i)

MODULE 02

FPSE — Face Plastic Squeezing Evaluator

F_face ≥ 1.25

Quantifies face convergence as volumetric strain field at advancing TBM face. Competence Factor CF = σ_cm/σ_v controls squeezing severity. TBM thrust provides p_eff face support.

F_face = [c·cot(φ)·(N_φ−1) + σ_v·N_φ^0.5] / [σ_v − p_eff]
CF = σ_cm/σ_v · SI = exp(α·(1/CF−1))

MODULE 03

LSLC — Lining Structural Stability Lock

LSII ≥ 0.15

Enforces moment-thrust interaction compliance per segmental ring. Assembles ring stiffness matrix K_ring with joint rotational springs. Evaluates UR(s) across full ring circumference.

UR(s) = √[(N_Ed/N_Rd)² + (M_Ed/M_Rd)²] ≤ 1/γ_s
LSII = 1 − max(UR(s))

AI COMPONENT 01

PINN — Plastic Zone Boundary Forecaster

R_p error: 3.4%

Physics-Informed Neural Network embedding Hoek-Brown yield criterion and equilibrium as training constraints. Forecasts R_p,3D from TBM telemetry in 2.3 seconds per advance increment.

L = λ_data·L_data + λ_phys·L_phys
λ_data=0.65 · λ_phys=0.35 · Update: 2.3s

AI COMPONENT 02

XGBoost — Face Convergence Ensemble

MAE: 1.8 mm/m

500-tree gradient boosting. 52-feature input: thrust/cutter, torque, penetration rate, grout pressure, tail gap + 12 lagged values per feature. Shapley: thrust/cutter = 0.28.

ε_face(next) = f(thrust, PR, torque, friction…)
Relative error: 4.2% · FAR: 3.8%

AI COMPONENT 03

CNN — Lining Distortion Classifier

Prec. 0.96 · Rec. 0.93

1D ConvNet on 360-point fiber optic strain profile. 4 convolutional blocks + dropout. Classifies ring state: normal / crown settlement / spring-line / joint opening / critical.

Input: ε(θ) 360-point angular strain
Output: 5-class · AUC = 0.98 · FAR = 2.8%

Case	Scenario	F_tunnel	TSII	LSII	δ_crown	AI Warning
A	Severe squeezing schist (450 m)	1.41	0.931	0.22	41.3 mm	4.3 D
B	Anisotropic limestone (310 m)	1.63	0.968	0.37	18.7 mm	5.1 D
C	Extreme squeezing claystone (580 m)	1.38	0.927	0.18	44.8 mm	3.8 D
Mean	—	1.47	0.942	0.26	34.9 mm	4.4 D

Quick Start

Deploy Safety Governance in 60 Seconds

pip install tunnel-shield-engine

from tunnel_shield import TunnelGovernor

# Initialize with rock mass configuration and tunnel geometry
governor = TunnelGovernor(
    rock_config="configs/high_squeezing_schist.yaml",
    depth_m=450.0,
    tunnel_radius_m=4.9,
    tbm_telemetry="live"
)

result = governor.evaluate()

print(result.signal)          # "STABILITY_CERTIFIED" | "MONITORING" | "STOP_COMMAND"
print(result.f_tunnel)        # weighted harmonic mean safety factor
print(result.tsii)             # Tunnel Structural Integrity Index [0,1]
print(result.lsii)             # Lining Structural Integrity Index [0,1]
print(result.plastic_radius_m) # R_p,3D at current face position (metres)
print(result.governance_level) # "none" | "level_1" | "level_2" | "stop"

from tunnel_shield import TunnelGovernor
from tunnel_shield.ai import PINNPlasticZone, XGBFaceConvergence, CNNDistortionClassifier, PINNPorePressure

governor = TunnelGovernor(
    rock_config="configs/high_squeezing_schist.yaml",
    ai_modules={
        "pinn_plastic":   PINNPlasticZone.from_pretrained("default"),
        "xgb_face":       XGBFaceConvergence.from_pretrained("default"),
        "cnn_distortion": CNNDistortionClassifier.from_pretrained("default"),
        "pinn_pore":      PINNPorePressure.from_pretrained("default"),
    }
)

result = governor.evaluate(forecast_increments=20)
print(result.rp_forecast)            # R_p,3D for next 20 increments
print(result.face_convergence_rate)  # mm/m (XGBoost prediction)
print(result.ring_distortion_class)  # normal | crown | joint | critical
print(result.pore_pressure_field)    # u_w(r,θ) spatial array

from tunnel_shield import TunnelGovernor
from tunnel_shield.simulation import SqueezeScenario

scenario = SqueezeScenario(
    depth_m=450.0,
    sigma_ci_MPa=28.0,
    gsi=35,
    k0=1.8,
    tunnel_radius_m=4.9,
    advance_rate_m_per_day=8.0
)

governor = TunnelGovernor(rock_config="configs/high_squeezing_schist.yaml")
results = governor.run_advance_sequence(scenario, n_increments=200)

print(results.min_f_tunnel)          # 1.41 (Case A validation)
print(results.max_plastic_radius)    # 3.4 × R_t
print(results.max_crown_settlement)  # 41.3 mm (< δ_max = 45 mm)
print(results.ai_warning_diameters)  # 4.3 diameters advance warning

# Launch real-time Streamlit safety monitoring dashboard
# Live R_p(x) heatmap · F_tunnel evolution · M-N utilization ring map · 🔴🟠🟢 signals

$ streamlit run examples/streamlit_live.py

# Dashboard at: http://localhost:8501
# Panels:
#   · Plastic zone evolution heatmap (LPEC live output)
#   · Face convergence profile (FPSE with XGBoost forecast)
#   · Per-ring M-N utilization diagram (LSLC)
#   · F_tunnel time-series with 1.35 threshold line
#   · 🔴🟠🟢 governance signal status panel
#   · PINN R_p forecast for next 20 advance increments
#   · JSON/CSV archive with SHA-256 checksums

Citation

Cite TUNNEL-SHIELD in Your Research

If TUNNEL-SHIELD contributes to your research, please use one of the citation formats below.

@software{baladi2026tunnelshield_pypi,
  author    = {Baladi, Samir},
  title     = {{TUNNEL-SHIELD}: A Critical Framework for Loosening Pressure
               Control, Face Plastic Deformation Mitigation, and Lining
               Structural Safety in Deep Shield Tunnels},
  year      = {2026},
  version   = {1.0.0},
  publisher = {Python Package Index},
  url       = {https://pypi.org/project/tunnel-shield-engine},
  note      = {Python package, MIT License,
               Systems Safety & Engineering (AI-augmented) — GEOTECH-AI-02}
}

@dataset{baladi2026tunnelshield_zenodo,
  author    = {Baladi, Samir},
  title     = {{TUNNEL-SHIELD}: A Critical Framework for Loosening Pressure
               Control, Face Plastic Deformation Mitigation, and Lining
               Structural Safety in Deep Shield Tunnels —
               Research Paper and Simulation Data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.20374106},
  url       = {https://doi.org/10.5281/zenodo.20374106},
  note      = {Geotechnical Engineering · Systems Safety · GEOTECH-AI-02}
}

@article{baladi2026tunnelshield,
  author  = {Baladi, Samir},
  title   = {{TUNNEL-SHIELD}: A Critical Framework for Loosening Pressure
             Control, Face Plastic Deformation Mitigation, and Lining
             Structural Safety in Deep Shield Tunnels},
  year    = {2026},
  month   = {May},
  version = {1.0.0},
  doi     = {10.5281/zenodo.20374106},
  url     = {https://doi.org/10.5281/zenodo.20374106},
  note    = {Ronin Institute / Rite of Renaissance,
             Systems Safety & Engineering (AI-augmented) — GEOTECH-AI-02}
}

Baladi, S. (2026). TUNNEL-SHIELD: A Critical Framework for Loosening Pressure
Control, Face Plastic Deformation Mitigation, and Lining
Structural Safety in Deep Shield Tunnels
(Version 1.0.0). Zenodo.
https://doi.org/10.5281/zenodo.20374106