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<p>This study involves an integrated approach to predict the mechanical properties of luffa fiber and marble dust- based concrete. It employs Artificial Neural Network (ANN) for mechanical properties prediction aiming for a higher accuracy than currently available models. The composite material used marble dust in the proportion 0–40% as fine aggregate replacement and luffa fiber in the proportion 0–2% as the natural reinforcement. Experimental results implied that the composite containing 20% marble dust and 1% luffa fiber exhibited greatest mechanical characteristics- Compressive strength of 34.5&#xa0;MPa, flexural strength of 6.2&#xa0;MPa and split tensile strength of 4.25&#xa0;MPa. This improvement was attributed to enhanced particle packing by marble dust and effective crack bridging by treated luffa fiber. A single multi-output feedforward multilayer perceptron (MLP) ANN consisting of two hidden layers of 64 and 32 neurons with ReLU activation functions and a three-neuron linear output layer was developed for simultaneously modelling the nonlinear interactions between the input variables and strength outputs. The model was trained on 70% of the dataset, with 15% for validation and 15% for testing. The ANN model was able to predict all three mechanical strength properties simultaneously with a high degree of accuracy as demonstrated by R² values of 0.89 (compressive strength), 0.94 (flexural strength), and 0.96 (split tensile strength) for the training data sets and small root mean square error (RMSE) values and negligible bias. The 100% a20 score indicated that all the samples from the predictions fell within ± 20% of the actual experimental values, demonstrating good robustness or generality. This research basically aims to address a major gap in the existing works by exploring the limited application of ANN in predicting the performance of hybrid sustainable concrete mixes incorporating both marble dust and plant-based fibers by developing a predictive model which is capable of capturing the complex interactions between multiple factors and enhancing the prediction efficiency, minimizing the dependency on extensive experimental investigations thereby promoting data driven evaluation of strength characteristics in sustainable concrete composites.</p>

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Predictive modeling of strength characteristics of sustainable concrete using artificial neural networks with luffa fiber and marble dust

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月22日

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  • S. Anandaraj,
  • S. Shaniya,
  • S. Swaminathan,
  • S. Suganya,
  • G. Sindhu Priya,
  • L. Balaji,
  • A. Dhanalakshmi,
  • Dawit Tafesse Gebreyohannes

摘要

This study involves an integrated approach to predict the mechanical properties of luffa fiber and marble dust- based concrete. It employs Artificial Neural Network (ANN) for mechanical properties prediction aiming for a higher accuracy than currently available models. The composite material used marble dust in the proportion 0–40% as fine aggregate replacement and luffa fiber in the proportion 0–2% as the natural reinforcement. Experimental results implied that the composite containing 20% marble dust and 1% luffa fiber exhibited greatest mechanical characteristics- Compressive strength of 34.5 MPa, flexural strength of 6.2 MPa and split tensile strength of 4.25 MPa. This improvement was attributed to enhanced particle packing by marble dust and effective crack bridging by treated luffa fiber. A single multi-output feedforward multilayer perceptron (MLP) ANN consisting of two hidden layers of 64 and 32 neurons with ReLU activation functions and a three-neuron linear output layer was developed for simultaneously modelling the nonlinear interactions between the input variables and strength outputs. The model was trained on 70% of the dataset, with 15% for validation and 15% for testing. The ANN model was able to predict all three mechanical strength properties simultaneously with a high degree of accuracy as demonstrated by R² values of 0.89 (compressive strength), 0.94 (flexural strength), and 0.96 (split tensile strength) for the training data sets and small root mean square error (RMSE) values and negligible bias. The 100% a20 score indicated that all the samples from the predictions fell within ± 20% of the actual experimental values, demonstrating good robustness or generality. This research basically aims to address a major gap in the existing works by exploring the limited application of ANN in predicting the performance of hybrid sustainable concrete mixes incorporating both marble dust and plant-based fibers by developing a predictive model which is capable of capturing the complex interactions between multiple factors and enhancing the prediction efficiency, minimizing the dependency on extensive experimental investigations thereby promoting data driven evaluation of strength characteristics in sustainable concrete composites.