A Life Cycle AI-Assisted Model for Optimizing Sustainable Material Selection
Revolutionize Sustainable Material Selection with AI-Powered Precision
This research presents a groundbreaking AI-assisted model for Sustainable Material Selection (SMS), addressing key challenges like fragmented data, reliance on experience, and lack of life cycle integration. Our novel approach integrates design, construction, operation & maintenance, and end-of-life phases, offering a dual-interface system for comprehensive material assessment. By formalizing closed-loop feedback, the model ensures practical insights inform earlier design decisions, leading to optimized building performance and environmental impact.
Key Findings at a Glance
The built environment has a significant impact, and our model offers a path to substantial improvements. Here's the executive summary of our findings:
40-50%
Global Annual Resource Extraction (Construction Sector)
37%
Global Energy & CO2 Emissions (Built Environment)
100
Total Credits in AI-Assisted SMS Model
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Model Development Phases
The proposed AI SMS assisted model is developed as a comprehensive Excel sheet that can be used directly or inserted into any dedicated “Spreadsheet to App" platforms for a user-friendly interface. The model constitutes three sequential phases.
Gantt Chart
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Main Credits Interface
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Detailed Credit Interface
AHP for Weighting
A weighted allocation approach was implemented using the Analytical Hierarchy Process (AHP). Expert judgments were elicited through in-person, structured interviews.
1-9 Saaty's Fundamental Scale for Comparisons| Aspect | Previous Studies | This Study |
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Thermal Properties Evaluation Proof of Concept
To illustrate the process, the interface for evaluating thermal properties is scrutinized as a representative case study. This criterion was selected for its significant cross-impact on two pivotal sustainability categories, EA and IEQ, as highlighted in previous studies.
Details: The model applies property-component networking, distributing credits based on relative impact (e.g., exterior walls/roofs receiving more U-value credits). It then uses a material-element network to calculate actual credit value based on selected material properties against benchmarks (e.g., double-pane glass for windows).
Key Takeaway: The model's algorithm synthesizes these two networks to provide a precise, quantitative evaluation of the sustainability impact of each material choice.
Practitioner Survey Results
A structured questionnaire was developed to assess the proposed SMS model in practice, administered between January and October 2025. The results indicate strong positive feedback.
4.86 Mean User-Friendliness Rating (out of 5)Statistical Significance
All three hypotheses—regarding practicality, user-friendliness, and effectiveness—were statistically supported at the p < 0.001 level. Medium to large effect sizes were obtained for all three constructs.
p < 0.001 Statistical Significance LevelAdvanced ROI Calculator: Optimize Your Sustainable Material Investments
Quantify the potential savings and reclaimed hours by integrating our AI-assisted SMS model into your enterprise workflows.
Estimated Annual Savings
Annual Hours Reclaimed
Your AI Implementation Roadmap
A phased approach to integrate the AI-assisted SMS model into your operations, ensuring maximum impact and smooth transition.
Phase 1: Discovery & Customization
Initial assessment of current material selection processes, data integration, and model customization to align with specific project types and sustainability goals.
Phase 2: Pilot Deployment & Training
Implement the model on a pilot project, train design and construction teams, and gather initial feedback for refinement.
Phase 3: Full Integration & Scaling
Integrate the AI model across all relevant project phases and departments, establish continuous data feedback loops, and scale for enterprise-wide adoption.
Phase 4: Continuous Optimization & AI Evolution
Regular model updates with new material data and AI advancements (Machine Learning), performance monitoring, and recalibration for ongoing sustainability optimization.
Ready to Transform Your Sustainable Practices?
Our AI-assisted SMS model is designed to empower your enterprise with data-driven decisions, leading to significant environmental and economic benefits. Don't let fragmented data and outdated methods hold you back.
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