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<p>The present research evaluates the performance of ANN for forecasting concrete compressive and flexural strength. The experimental dataset comprising of 265 rows of treated recycled aggregates (TRA), replacement level (%), slump, concrete curing age were considered as variables while compressive and flexural strength were the output parameters. LMA model demonstrated near-perfect prediction accuracy (R² values: 0.9998, 0.9994, 0.9995), demonstrating its precision in modeling. Compared to other approaches, LMA had the lowest gradient error (1.63 × 10⁻⁴), resulting in faster and more consistent convergence. This shows how LMA produces fast, accurate results.</p>

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Prediction of Compressive and Flexural Strength of Processed Recycled Aggregate Concrete Using Artificial Neural Network

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Ashutosh Shishodiya,
  • Yogesh Iyer Murthy,
  • Velaga Sarath Babu

摘要

The present research evaluates the performance of ANN for forecasting concrete compressive and flexural strength. The experimental dataset comprising of 265 rows of treated recycled aggregates (TRA), replacement level (%), slump, concrete curing age were considered as variables while compressive and flexural strength were the output parameters. LMA model demonstrated near-perfect prediction accuracy (R² values: 0.9998, 0.9994, 0.9995), demonstrating its precision in modeling. Compared to other approaches, LMA had the lowest gradient error (1.63 × 10⁻⁴), resulting in faster and more consistent convergence. This shows how LMA produces fast, accurate results.