AI-Powered LCA/LCCA Framework with Cross-Category Interaction Analysis
Developed by Dr. Yehia Abdelhamid Attia | PhD Researcher in AI & Civil Engineering | Cairo University
This tool provides a comprehensive Life Cycle Assessment (LCA) and Life Cycle Cost Analysis (LCCA) framework for monorail transit systems. It integrates AI-driven explainability, multi-objective optimization, and cross-category sustainability interaction modeling — calibrated against peer-reviewed benchmarks.
The tool is built in Python (Tkinter) with Plotly interactive visualizations and follows ISO 14040/14044 and ASTM E917 standards.
- Integrated Sustainability Scoring — Material, Environmental, Operational & Economic dimensions with cross-category coupling
- Synergy-Trade-off Interaction Model — Based on PNAS 2019 methodology (SDG interactions framework)
- Benchmark Validation — Calibrated against Chen et al. (2022), Complexity Journal (Q2), DOI:
10.1155/2022/3872069 - 3D Pareto Front Optimization — NSGA-II-style multi-objective analysis
- 12-Element Parallel Coordinates — Comprehensive sustainability element visualization
- Monte Carlo Uncertainty Analysis — Conformal prediction with 1000+ simulations & 95% confidence intervals
- 3D Sensitivity Surface — Non-linear parameter interaction visualization
- SHAP-Style Explainability (XAI) — Feature importance ranking for transparent AI analysis
- Urban Analytics 3D — Geospatial station-level impact assessment (Cairo, Chongqing, Osaka)
- Export Functions — Excel, CSV, and text report generation
Alaa-Project/
├── SD_LCA_LCCA_Enhanced.py # Main application (full GUI + calculation engine)
├── run_enhanced_app.py # Application launcher script
├── Launch_Monorail_App.bat # Windows batch launcher
├── requirements.txt # Core dependencies
├── requirements_enhanced.txt # Enhanced dependencies (all features)
├── Rules.txt # Project rules and guidelines
├── 3d_sensitivity_surface.html # Pre-generated 3D sensitivity visualization
├── pareto_front_3d.html # Pre-generated Pareto front visualization
├── parallel_coordinates_12elements.html # 12-element parallel coordinates chart
├── shap_feature_importance.html # SHAP feature importance chart
├── uncertainty_analysis_montecarlo.html # Monte Carlo analysis visualization
├── urban_analytics_cairo.html # Cairo case study urban analytics
└── README.md
| Dimension | Weight | Key Metrics |
|---|---|---|
| Material Efficiency | 25% | 6 materials + recycling rates |
| Environmental | 30% | CO2 emissions, embodied energy, renewables |
| Operational | 25% | Energy efficiency, time savings, availability |
| Economic | 20% | LCCA, job creation, multiplier effects |
| Interaction | Coefficient | Type |
|---|---|---|
| Material → Environmental | -0.25 | Trade-off |
| Environmental → Operational | +0.15 | Synergy |
| Operational → Economic | +0.30 | Synergy |
| Economic → Material | +0.20 | Synergy |
| Material → Operational | +0.10 | Synergy |
| Environmental → Economic | -0.15 | Trade-off |
Chen, J., Wang, H., Li, X. (2022). Quantifying Carbon Emissions Generated by Monorail Transits: A Life Cycle Assessment Approach. Complexity (Hindawi), 2022, 3872069. DOI: 10.1155/2022/3872069 | Q2 | Open Access CC BY 4.0
- Python 3.8+
- pip
# Clone the repository
git clone https://github.com/Dr-Yehia/Alaa-Project.git
cd Alaa-Project
# Install dependencies
pip install -r requirements_enhanced.txt
# Run the application
python run_enhanced_app.pyDouble-click Launch_Monorail_App.bat
tkinter # GUI framework (built-in)
matplotlib # Static visualizations
plotly # Interactive 3D charts
numpy # Numerical computations
pandas # Data management & export
scipy # Statistical analysis
scikit-learn # Data preprocessing (MinMaxScaler)
openpyxl # Excel export
- Input Parameters — Materials, Environmental, Operational & Economic inputs
- Results & Analysis — Full assessment report with synergy/trade-off breakdown
- Benchmark & Validation — Comparison against Chen et al. (2022)
- Pareto Optimization — 3D multi-objective Pareto front
- 12-Element Analysis — Parallel coordinates for 12 sustainability dimensions
- Uncertainty Analysis — Monte Carlo with conformal prediction intervals
- 3D Sensitivity Surface — Parameter interaction surface plots
- AI Explainability — SHAP-style feature importance analysis
- Urban Analytics — Geospatial station-level analysis (Cairo, Chongqing, Osaka)
- About & Methodology — Full scientific documentation
- Chen et al. (2022) — Quantifying Carbon Emissions, Complexity, DOI: 10.1155/2022/3872069
- Nilsson et al. (2016) — SDG interactions, Nature, 534, 320–322
- Fonseca et al. (2020) — Synergies and trade-offs, PNAS, 116(45)
- ISO 14040:2006 — Environmental Management, LCA Principles
- ASTM E917 — Life-Cycle Cost Measurement
- IPCC AR6 (2019) — Greenhouse Gas Inventory Guidelines
- Ecoinvent Database v3.8 — Life Cycle Inventory data
Yehia Abdelhamid Attia
- PhD Candidate in AI & Civil Engineering, Cairo University
- IEEE Member | Published Author
- AI Optimization & Evaluation Algorithms Expert
- LinkedIn: yehia-attia-b661101a2
- GitHub: @Dr-Yehia
If you use this tool in your research, please cite:
@software{attia2025monorail,
author = {Yehia Abdelhamid Attia},
title = {Enhanced Monorail Sustainability Assessment Tool:
Integrated LCA/LCCA with Cross-Category Interaction Analysis},
year = {2025},
institution = {Cairo University},
version = {2.0},
note = {Implements synergy-trade-off interaction model
based on PNAS 2019 methodology}
}This project is for academic and research purposes. All rights reserved by the author.
Built with Python | Powered by Plotly & Tkinter | Validated against Chen et al. (2022)