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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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    Area of Science:

    • Artificial Intelligence
    • Computational Intelligence
    • Machine Learning

    Background:

    • Interval Type-2 Fuzzy Neural Networks (IT2FNNs) often require numerous fuzzy rules for high-dimensional nonlinear system identification, leading to computational inefficiency.
    • The 'explosion of fuzzy rules' problem hinders the practical application of IT2FNNs in complex systems.

    Purpose of the Study:

    • To develop a self-organizing IT2FNN that mitigates the rule explosion issue.
    • To enhance the efficiency and accuracy of nonlinear system identification using IT2FNNs.

    Main Methods:

    • A relation-aware strategy to construct rotatable type-2 fuzzy rules (RT2FRs) for interpreting interactive high-dimensional inputs.
    • An information evaluation mechanism for structural adjustment (growing/pruning) of the IT2FNN.
    • A multicriteria-based optimization algorithm for simultaneous parameter updates of RT2FRs.

    Main Results:

    • The proposed IA-SOIT2FNN effectively avoids the explosion of fuzzy rules.
    • The method achieves a compact network structure while maintaining high identification accuracy.
    • Experimental results demonstrate competitive performance against state-of-the-art approaches.

    Conclusions:

    • The IA-SOIT2FNN offers an efficient and accurate solution for nonlinear system identification.
    • The developed techniques provide a robust framework for designing compact and effective fuzzy neural networks.
    • This approach advances the application of IT2FNNs in complex, high-dimensional data analysis.