Enterprise AI Analysis
Machine learning models for mechanical properties prediction of basalt fiber-reinforced concrete incorporating graphical user interface
This study utilized advanced machine learning models (SVR, RFR, DT, BR, GBR) and SHAP analysis to predict compressive and splitting tensile strength of basalt fiber-reinforced concrete (BFRC) using published experimental datasets. A user-friendly graphical user interface (GUI) was developed to enable easy and affordable prediction of these mechanical properties, contributing to sustainable and efficient concrete mix design. The GBR model achieved an R² of 0.99 (training) and 0.86 (testing) for compressive strength, while the SVR model achieved an R² of 0.99 (training) and 0.97 (testing) for splitting tensile strength. Cement and silica fume were identified as key positive influencers for CS, and basalt fiber diameter for STS.
Executive Impact: Key Performance Indicators
This analysis highlights the tangible benefits and performance metrics achieved through AI-driven optimization in Basalt Fiber-Reinforced Concrete (BFRC) applications.
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GBR R² (CS Testing)
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SVR R² (STS Testing)
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Carbon Emission Reduction
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Maintenance Cost Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
SHAP Overview
SHAP (SHapley Additive exPlanations) is a game theory-based approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory. SHAP values represent the average marginal contribution of a feature value across all possible coalitions.
CS Interpretation
For Compressive Strength (CS), SHAP analysis showed that Cement had the highest positive influence. Higher cement content generally leads to higher CS. Silica Fume and Fine Aggregate also contributed positively, enhancing material density and particle packing. Other parameters like Fly Ash and Fiber content had lesser impact.
STS Interpretation
For Splitting Tensile Strength (STS), Basalt Fiber Diameter was the most influential feature. Larger fiber diameters facilitate better stress transfer and crack bridging, significantly improving tensile resistance. Fine Aggregate also showed a notable positive effect on STS.
Compressive Strength (CS) Prediction Model Performance
Machine learning models, particularly GBR, achieved high accuracy in predicting compressive strength, with GBR demonstrating the most precise predictions.
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GBR R² (Training)
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GBR R² (Testing)
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SVR R² (Training)
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SVR R² (Testing)
Splitting Tensile Strength (STS) Prediction Model Performance
SVR model outperformed others in STS prediction, achieving exceptional R² values for both training and testing phases.
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SVR R² (Training)
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SVR R² (Testing)
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GBR R² (Training)
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GBR R² (Testing)
Key Influencers on Compressive Strength
SHAP analysis revealed that cement content had the most significant positive impact on BFRC compressive strength, followed by silica fume and fine aggregate.
Cement Highest positive influence on CSKey Influencers on Splitting Tensile Strength
Basalt fiber diameter was found to be the most influential parameter positively affecting splitting tensile strength.
BF Diameter Highest positive influence on STSEnd-to-End ML Prediction Workflow for BFRC
The study utilized a systematic machine learning workflow, including data preparation, model training with hyperparameter tuning, and comprehensive evaluation.
BFRC Data Collection
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Data Splitting (70% Train, 30% Test)
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Hyperparameter Tuning (Grid Search)
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Model Training & Evaluation (SVR, RFR, DT, BR, GBR)
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SHAP Analysis for Interpretability
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GUI-based Prediction
Streamlined BFRC Design with Interactive GUI
A user-friendly Graphical User Interface (GUI) was developed to enable concrete designers to easily and affordably predict CS and STS without expensive computations or experiments, enhancing practical applicability and decision-making.
Impact: The developed GUI translates complex ML models into a practical tool, facilitating efficient and sustainable concrete mix design.
| Feature | Traditional Steel | Basalt Fiber-Reinforced Concrete (BFRC) |
|---|---|---|
| Carbon Emissions | High (74% more) | Low |
| Corrosion Resistance | Low | High |
| Durability | Moderate | Exceptional |
| Cost (Long-term) | Higher (maintenance) | Lower |
Economic & Environmental Impact of BFRC
The adoption of BFRC contributes to substantial carbon emission reductions and significant savings in infrastructure maintenance costs.
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Carbon Emission Reduction
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Maintenance Cost Reduction (USD)
Projected Enterprise ROI
Estimate the potential return on investment for integrating AI-driven BFRC design optimizations into your enterprise workflows. Adjust parameters to see personalized projections.
Estimated Annual Savings
Annual Hours Reclaimed
Strategic Implementation Roadmap
A phased approach to integrate AI-driven BFRC design into your enterprise, ensuring a smooth transition and maximizing benefits.
Phase 1: Data Integration & Model Validation
Integrate existing BFRC datasets and validate ML models against new experimental data to confirm generalizability.
Phase 2: Custom GUI Deployment & Training
Deploy the interactive GUI within engineering teams and provide training on its use for rapid CS/STS prediction and mix design.
Phase 3: Pilot Project Implementation
Apply AI-driven BFRC designs to a pilot construction project, monitoring performance and optimizing mix proportions in real-world scenarios.
Phase 4: Scaled Rollout & Continuous Improvement
Expand AI integration across multiple projects, establishing feedback loops for continuous model refinement and material innovation.
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