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Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms

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Iranian Journal of Science and Technology, Transactions of Civil Engineering Aims and scope Submit manuscript

Abstract

This study implements hybrid machine learning models that utilize six commonly employed meta-heuristic algorithms to predict the compressive strength (CS) of manufactured sand concrete (MSC). Six hybrid artificial neural network (ANN) models were created utilizing multiple meta-heuristic algorithms of different groups. A sum of 275 records were used to determine concrete CS of MSC. The hybrid framework, combining ANN and firefly algorithm, i.e., ANN-FF, shows exceptional accuracy in predicting the CS. During the model development stage, the ANN-FF model achieved R2 = 0.9536 and RMSE = 0.0498. During testing phase, the values of these indices are R2 = 0.9276 and RMSE = 0.0656. The results of the sensitivity analysis demonstrate that the constructed ANN-FF framework effectively estimates the magnitude of the correlation between influential parameters and the CS. The evaluation of outcomes was examined using a variety of tools including Taylor diagram, error matrix, and OBJ criterion. In terms of objective criterion, ANN-FF achieved the best predictive precision. Based on the findings, the constructed ANN-FF can serve as a viable alternative for supporting engineers in civil engineering endeavours. The MATLAB developed ANN-FF model (constructed using eleven distinct influencing parameters) is also attached that can readily be implemented to predict the CS of MSC.

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Data Availability

The original dataset can be obtained from the study of Zhao et al. (2017). The reproduced dataset and the developed ANN-FF model are attached as supplementary materials.

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Authors and Affiliations

  1. Department of Civil Engineering, National Institute of Technology Patna, Patna, India

    Abidhan Bardhan

  2. Department of Civil Engineering, Nalanda College of Engineering, Chandi, Nalanda, India

    Sudeep Kumar

  3. Department of Civil Engineering, Government Engineering College, Sheikhpura, India

    Avinash Kumar

  4. Department of Civil Engineering, Bhagalpur College of Engineering, Bhagalpur, India

    Subodh Kumar Suman

  5. Department of Applied Mechanics, Visvesvaraya National Institute of Technology Nagpur, Nagpur, India

    Rahul Biswas

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  1. Abidhan Bardhan
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  3. Avinash Kumar
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Al authors contributed equally.

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Correspondence to Abidhan Bardhan.

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Appendix-A: MATLAB implementation of ANN-FF paradigm

Appendix-A: MATLAB implementation of ANN-FF paradigm

figure a

  • Details of weights and biases

Input to hidden weights (11 × 10)

1.0000

0.3553

0.3366

− 0.5411

0.2290

− 0.6848

0.5514

− 0.4170

− 0.9822

− 0.0493

0.3922

− 0.3509

− 0.5327

− 0.9995

0.2668

− 0.9935

− 0.4050

0.2108

− 0.2185

0.5664

− 0.1372

0.2050

1.0000

0.9999

− 1.0000

1.0000

− 0.5024

− 1.0000

0.5212

− 0.8684

− 0.7664

− 0.3723

− 0.0982

− 0.8612

0.7258

0.2180

0.9991

0.9811

− 0.6478

− 0.2477

− 0.4119

− 0.7631

− 0.6174

0.3896

− 0.1982

− 0.4955

0.2391

− 0.2930

0.9995

0.1058

− 0.0722

− 0.8596

− 0.2365

0.3575

− 0.3189

− 0.9875

− 0.3844

− 0.9981

− 0.5963

0.9683

0.4796

− 0.6437

0.8626

− 0.9983

0.2107

0.2624

0.3155

0.8681

0.0031

0.5761

− 0.8149

− 0.0163

− 0.7180

0.0790

− 0.5860

− 0.8722

− 1.0000

− 0.0553

− 0.9973

0.7705

− 0.7864

0.9999

0.0888

− 0.6935

0.8317

0.0276

− 1.0000

0.1158

− 1.0000

0.9950

0.8772

− 0.0787

− 0.0674

0.8758

− 0.0006

0.0845

0.8767

− 0.3215

− 0.1753

− 0.6916

0.3760

0.2494

0.2704

0.6276

0.5013

− 0.6468

− 0.9209

− 0.3032

− 0.0577

0.7387

Hidden biases (10 × 1)

Hidden to output weights (10 × 1)

Output bias

− 0.1236

− 0.3139

− 0.1236

− 1.0000

0.7471

 

1.0000

0.4464

 

− 0.2578

− 0.3991

 

− 0.6572

− 0.6543

 

− 0.0828

− 0.5151

 

0.0845

− 0.2546

 

− 0.2637

− 0.4603

 

0.2883

0.2698

 

− 0.2702

− 0.4722

 

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Bardhan, A., Kumar, S., Kumar, A. et al. Compressive Strength Estimation of Manufactured Sand Concrete Using Hybrid ANN Paradigms Constructed with Meta-heuristic Algorithms. Iran J Sci Technol Trans Civ Eng (2024). https://doi.org/10.1007/s40996-024-01406-9

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