Enterprise AI Analysis
CFG-DWC: a hybrid correlation-driven feature engineering framework for optimized machine learning performance in carbonation depth analysis of concrete subjected to natural environments
This paper introduces a novel hybrid Correlation-Driven Feature Generation (CFG) framework, enhanced with Dynamic Weighted Correlation (DWC), to improve machine learning (ML) predictions for carbonation depth in concrete. Using a new dataset from natural environments, the study compares DWC with traditional correlation methods (Spearman, Pearson, Kendall's tau) across various ML algorithms (Linear Regression, Random Forest, XGBoost). DWC dynamically weights feature segments, capturing both linear and non-linear relationships, and generates new correlated features that significantly boost model performance. XGBoost with DWC achieves the highest accuracy (R2 = 0.86, 20.5% MAE reduction), with concrete age identified as the most influential parameter. The framework offers a robust, interpretable tool for durable concrete design and maintenance.
Predictive Power & Durability Impact
The CFG-DWC framework significantly enhances the accuracy of carbonation depth prediction, directly contributing to more durable and cost-effective reinforced concrete structures. By identifying key material parameters and leveraging advanced feature engineering, this AI model empowers engineers to make informed decisions, reducing maintenance costs and extending the lifespan of critical infrastructure.
0.86 R²
XGBoost R² (DWC)
20.5%
MAE Reduction (DWC)
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Key Influencer: Concrete Age
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Data Collection & Cleaning
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Update Dataset with CFG (DWC)
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Applied Correlation Methods
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Applied ML Methods
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Obtain Performance Metrics
0.86 R²
XGBoost Prediction Accuracy (DWC Enhanced)
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Concrete Age
Most Influential Factor for Carbonation Depth
Enhanced Durability Design
By accurately predicting carbonation depth with XGBoost + DWC (R² = 0.86), engineers can optimize concrete mix designs, ensuring longer lifespan for structures in natural environments. This reduces the need for premature repairs and enhances public safety. The framework's ability to identify concrete age as the most influential factor underscores its practical utility in long-term infrastructure planning.
20.5%
MAE Reduction with DWC
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Implementation Roadmap
Phase 1: Data Integration & Model Setup
Integrate existing concrete durability data, configure the CFG-DWC framework, and establish baseline ML models. (Estimated: 2-4 Weeks)
Phase 2: Custom Feature Engineering & Optimization
Tailor DWC parameters, generate application-specific interaction features, and optimize model performance for your unique material compositions. (Estimated: 3-6 Weeks)
Phase 3: Validation, Deployment & Training
Validate the predictive models with real-world scenarios, deploy the solution, and train your engineering teams for effective use. (Estimated: 4-8 Weeks)
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