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Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system

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Abstract

A falling weight deflectometer is a testing device used in civil engineering to measure and evaluate the physical properties of pavements, such as the modulus of the subgrade reaction (Y1) and the elastic modulus of the slab (Y2), which are crucial for assessing the structural strength of pavements. In this study, we developed a novel hybrid artificial intelligence model, i.e., a genetic algorithm (GA)-optimized adaptive neuro-fuzzy inference system (ANFIS-GA), to predict Y1 and Y2 based on easily determined 13 parameters of rigid pavements. The performance of the novel ANFIS-GA model was compared to that of other benchmark models, namely logistic regression (LR) and radial basis function regression (RBFR) algorithms. These models were validated using standard statistical measures, namely, the coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE). The results indicated that the ANFIS-GA model was the best at predicting Y1 (R = 0.945) and Y2 (R = 0.887) compared to the LR and RBFR models. Therefore, the ANFIS-GA model can be used to accurately predict Y1 and Y2 based on easily measured parameters for the appropriate and rapid assessment of the quality and strength of pavements.

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Acknowledgements

We acknowledge the support provided by the University of Transport Technology.

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Authors and Affiliations

  1. Faculty of Civil Engineering, University of Transport Technology, Hanoi, 10000, Vietnam

    Long Hoang Nguyen, Dung Quang Vu, Duc Dam Nguyen, Vinh The Dang, Hiep Van Le & Binh Thai Pham

  2. Department of Civil Engineering, State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, 200025, China

    Fazal E. Jalal

  3. Department of Civil Engineering, University of Engineering and Technology, Peshawar, 25120, Pakistan

    Mudassir Iqbal

  4. DDG (R) Geological Survey of India, Gandhinagar, 382010, India

    Indra Prakash

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  1. Long Hoang Nguyen
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  2. Dung Quang Vu
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  3. Duc Dam Nguyen
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  4. Fazal E. Jalal
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  5. Mudassir Iqbal
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  6. Vinh The Dang
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  7. Hiep Van Le
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  8. Indra Prakash
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  9. Binh Thai Pham
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Correspondence to Binh Thai Pham.

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Nguyen, L.H., Vu, D.Q., Nguyen, D.D. et al. Prediction of falling weight deflectometer parameters using hybrid model of genetic algorithm and adaptive neuro-fuzzy inference system. Front. Struct. Civ. Eng. 17, 812–826 (2023). https://doi.org/10.1007/s11709-023-0940-7

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