Structural Safety & Reliability Engineering · TOWER-SAFETY-01 · Version 1.0.0 · May 2026

TOWER-CORE

A Critical Framework for Structural Integrity Assessment, Dynamic Stability Monitoring,
and Safety Governance in Vertical Tower Systems
Structural Reliability Engineering · Dynamic Loading Analysis · Fatigue Accumulation Governance

"A vertical tower operating under sustained wind loading is not in static equilibrium — it is a dynamically evolving system
accumulating fatigue damage, experiencing natural frequency drift, and approaching or receding from its overturning
stability limit in real time. TOWER-CORE quantifies these changes continuously and governs the safety margin accordingly."

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

Real-Time Safety Governance

Four-Level TSII Signal Classification

Every tower operational state evaluated by TOWER-CORE receives a continuous safety signal with full frequency, fatigue, and stability diagnostics.

TSII ≥ 0.90

🟢 STEADY STATE

All frequency, fatigue, and stability constraints satisfied. Normal operation active — continuous SSI-COV modal monitoring.

0.75 ≤ TSII < 0.90

🟠 MONITORING PHASE 1

Natural frequency drift or fatigue accumulation detected. Enhanced monitoring frequency — targeted inspection at flagged details.

0.65 ≤ TSII < 0.75

🟠 MITIGATION PHASE 2

Operational load restriction required. Immediate structural review — P-delta verification and hot-spot inspection.

TSII < 0.65

🔴 CRITICAL BREACH

Safety threshold breach. Immediate shutdown. Proximity zone evacuation. Full diagnostic: DFMM modal analysis, SJFAM damage map, GSOAM stability report.

Governance Architecture

Three Modules · One Structural State · Continuous Assessment

Fully coupled dynamic stability assessment augmented by real-time sensor fusion. No decoupled frequency-then-fatigue paradigm.

MODULE 01

DFMM — Dynamic Frequency Monitoring Module

NFS < 5% · RSM ≥ 10%

SSI-COV ambient modal identification at ±0.1% frequency resolution. Tracks natural frequency shift NFS_i(t) and resonance safety margin RSM_i(t) in real time.

f_n(P) = f₀·√(1 - P/P_cr)
SSI-COV: Y = [y|ŷ] · SVD · modal extraction

MODULE 02

SJFAM — Structural Joint Fatigue Assessment Module

D_fatigue < 0.80

ASTM E1049-85 rainflow cycle counting. Palmgren-Miner linear damage accumulation with Goodman mean stress correction. Eurocode FAT class S-N curves.

D_fatigue(t) = Σ n_i/N_i(Δσ_i)
σ_a,eq = σ_a / (1 - σ_m/σ_UTS)

MODULE 03

GSOAM — Global Stability & Overturning Module

F_stability ≥ 1.50

Davenport gust response factor for dynamic wind loading. P-delta geometric nonlinearity correction. Guyed mast tension and restoring force analysis.

F_stability = M_restoring / (M_wind + M_oper)
θ_stab = P·δ / (EI/h²) ≤ 0.10

DEGRADATION

S_deg — Stiffness Degradation Index

S_deg < 0.25

Continuum damage mechanics model (Chaboche). ISO 9224 corrosion rate prediction. Remaining life estimation from modal frequency reduction.

S_deg = 1 - K_damaged/K_intact
T_rem = (A_rem - A_crit) / (dA/dt)

TSII

Tower Structural Integrity Index

TSII ≥ 0.90

Weighted composite of stiffness degradation, overturning stability, and fatigue damage. Continuous real-time safety certification with 24-48h forecast.

TSII = 0.40·(1-S_deg) + 0.35·(F_stab/1.50) + 0.25·(1-D_fatigue)

SENSOR FUSION

Kalman Filter State Estimation

Real-time · 100Hz

Multi-sensor fusion: accelerometers, strain gauges, tiltmeters, anemometers. Outlier rejection and missing data interpolation.

x̂(t|t) = x̂(t|t-1) + K(t)[z(t) - H·x̂(t|t-1)]
K(t) = P(t|t-1)·Hᵀ·[H·P·Hᵀ + R]⁻¹

Experimental Validation

Three Canonical Benchmark Scenarios

Validated against guyed telecom mast, lattice tower, and monopole scale model under storm, fatigue, and progressive loading conditions.

Case	Tower Type	TSII Accuracy	Frequency Detection	Fatigue MAE	F_stab Error	Status
V1	Guyed telecom mast — storm events	±2.8%	95.1%	2.4%	±4.1%	✅ PASS
V2	Lattice tower — fatigue forensics	±3.2%	94.8%	2.9%	N/A	✅ PASS
V3	Monopole scale model — progressive	±2.5%	96.3%	1.8%	±3.7%	✅ PASS
MEAN	—	±2.83%	95.4%	2.37%	±3.9%	🏆 CERTIFIED

TSII certification threshold = 0.90 · F_stability minimum = 1.50 · Damage limit = 0.80

Quick Start

Deploy Safety Governance in 60 Seconds

pip install tower-core-engine

from tower_core import TowerCoreAssessor

# Initialize with tower configuration
assessor = TowerCoreAssessor(
    tower_config="configs/lattice_120m.yaml",
    sensor_stream="live"
)

result = assessor.evaluate()

print(result.signal)          # "STEADY_STATE" | "MONITORING_PHASE_1" | "MITIGATION_PHASE_2" | "CRITICAL_BREACH"
print(result.tsii)            # Tower Structural Integrity Index [0,1]
print(result.nfs)             # Natural Frequency Shift (%)
print(result.fatigue_damage_max) # Palmgren-Miner damage
print(result.f_stability)    # Overturning stability factor
print(result.governance_level) # "none" | "level_1" | "level_2" | "stop"

from tower_core.fatigue import RainflowCounter, PalmgrenMiner, SNCurve
import numpy as np

# Load strain time series
strain_ts = np.random.randn(10000) * 50

# Rainflow cycle counting (ASTM E1049-85)
counter = RainflowCounter()
cycles, amplitudes = counter.count(strain_ts)

# S-N curve (Eurocode FAT90)
sn = SNCurve(fat_class="FAT90")

# Palmgren-Miner damage accumulation
miner = PalmgrenMiner()
damage = miner.from_amplitudes(amplitudes, cycles, sn)

print(f"Fatigue damage: {damage:.4f} (limit: 0.80)")

from tower_core.stability import OverturningStability, GustResponse

stab = OverturningStability(f_stab_min=1.50)
M_restoring = stab.restoring_moment(W=100000, B=10)
M_wind = 50000
F_stability = stab.factor(M_restoring, M_wind)

gust = GustResponse()
G = gust.gust_factor(z=50, f_n=0.85)

print(f"F_stability: {F_stability:.2f} (minimum: 1.50)")
print(f"Gust factor: {G:.2f}")

# Launch real-time Streamlit TSII governance dashboard
$ streamlit run examples/streamlit_dashboard.py

# Dashboard at: http://localhost:8501
# Panels: Natural frequency trend · Fatigue damage map · Overturning stability · TSII gauge · 24-48h forecast

Citation

Cite TOWER-CORE in Your Research

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

@software{baladi2026towercore_pypi,
  author    = {Baladi, Samir},
  title     = {{TOWER-CORE}: A Critical Framework for Structural Integrity
               Assessment, Dynamic Stability Monitoring, and Safety
               Governance in Vertical Tower Systems},
  year      = {2026},
  version   = {1.0.0},
  publisher = {Python Package Index},
  url       = {https://pypi.org/project/tower-core-engine},
  note      = {Python package, MIT License, Series TOWER-SAFETY-01}
}

@dataset{baladi2026towercore_zenodo,
  author    = {Baladi, Samir},
  title     = {{TOWER-CORE}: A Critical Framework for Structural Integrity
               Assessment, Dynamic Stability Monitoring, and Safety
               Governance in Vertical Tower Systems —
               Research Paper and Simulation Data},
  year      = {2026},
  publisher = {Zenodo},
  version   = {1.0.0},
  doi       = {10.5281/zenodo.20394041},
  url       = {https://doi.org/10.5281/zenodo.20394041},
  note      = {Structural Safety & Reliability Engineering · TOWER-SAFETY-01}
}

@article{baladi2026towercore,
  author  = {Baladi, Samir},
  title   = {{TOWER-CORE}: A Critical Framework for Structural Integrity
             Assessment, Dynamic Stability Monitoring, and Safety
             Governance in Vertical Tower Systems},
  year    = {2026},
  month   = {May},
  version = {1.0.0},
  doi     = {10.5281/zenodo.20394041},
  url     = {https://doi.org/10.5281/zenodo.20394041},
  note    = {Ronin Institute / Rite of Renaissance,
             Series TOWER-SAFETY-01}
}

Baladi, S. (2026). TOWER-CORE: A Critical Framework for Structural Integrity
Assessment, Dynamic Stability Monitoring, and Safety Governance in
Vertical Tower Systems (Version 1.0.0, Series TOWER-SAFETY-01). Zenodo.
https://doi.org/10.5281/zenodo.20394041