Estimation of the Spacing Factor Based on Air Pore Distribution Parameters in Air-Entrained Concrete
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Summary
This summary is machine-generated.Analyzing air-entrained concrete's chord length distribution using artificial neural networks (ANN) provides a more accurate spacing factor (L2) estimate. This method correlates well with standard calculations, offering a practical approach for concrete quality assessment.
Area Of Science
- Civil Engineering
- Materials Science
- Concrete Technology
Background
- The EN-480-11 standard defines air-void characteristics in concrete.
- Powers' model for spacing factor calculation is criticized for not considering chord length distribution.
- Accurate spacing factor estimation is crucial for concrete durability.
Purpose Of The Study
- To investigate if analyzing chord length distribution improves spacing factor (L2) estimation.
- To develop an artificial neural network (ANN) model for predicting the spacing factor.
- To identify key air-void structure parameters influencing the spacing factor.
Main Methods
- Analysis of a dataset of 110 air-entrained concretes.
- Application of artificial neural network (ANN) modeling.
- Input parameters included chord size distribution characteristics (A, d, W), cement paste content (P), and number of large pores (N5).
Main Results
- The ANN model accurately estimates the spacing factor (L2).
- Peak amplitude (A), peak width (W), and cement paste content (P) were the most influential factors.
- Strong correlation observed between ANN model results and standard spacing factor (L2) calculations.
Conclusions
- Chord length distribution analysis enhances spacing factor (L2) estimation accuracy.
- The developed ANN model offers a reliable method for calculating the spacing factor.
- A practical method for implementing the ANN model in Excel was demonstrated.
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