RBM PoF Model and Supporting Data for Risk-Based and Condition-Based Crude-Oil Tank Inspection Optimisation

This dataset and Python-based simulation model support the research study “Optimising Crude-Oil Tank Inspection Through Risk-Based and Condition-Based Maintenance”.The package contains: Appendix A — External Ultrasonic Thickness Register (CSV) Appendix B — RPAV Validation Metrics (CSV) Appendix D — Economic Model (XLSX) Appendix E — Regulatory Compliance Matrix (XLSX) inputs.yaml — Model configuration parameters pof_results.csv — Calculated Probability of Failure results updated_pof_heatmap.png — Visualisation of updated PoF values risk_matrix.png — RBM risk categorisation heatmap rbm_model.py — Python script for PoF Monte Carlo simulation and sensitivity analysis Readme.txt — File descriptions and usage instructions The model implements a Monte Carlo–based risk projection for corrosion-related failures in crude-oil cargo tanks, incorporating external UTM data, RPAV inspection results, regulatory thresholds, and economic consequences.Two scenarios are included: Extended Horizon (10–15 years) Increased Corrosion Rate sensitivity This resource is intended to support further academic and industry research on remote inspection techniques, RBM/CBM methodologies, and cost–risk optimisation for tanker maintenance.