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Developing artificial neural network models to predict corrosion of reinforcement in mechanically stabilized earth walls

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Abstract

Corrosion of reinforcement in mechanically stabilized earth (MSE) walls is mainly due to physical and chemical properties of back-fill materials, types of metal reinforcement, and environmental factors. Therefore, it is imperative to evaluate the corrosion levels of reinforcement in the design and construction of MSE walls. This study employed artificial neural networks (ANN) to build prediction models of corrosion of reinforcement (COR) based on collected corrosion factors from 489 in-situ boring samples of MSE walls on highways in France. The ANN models were built, trained and achieved the best performance at an ANN structure (12-20-18-1) with one input layer (12 neurons), two hidden layers (20 and 18 neurons) and one output layer (1 neuron). The results have shown that the proposed model can provide a high coefficient correlation (R = 0.878) and a low mean squared error (MSE = 0.00454 µm2). Furthermore, a sensitivity study has shown that the factors of sulfate ion (SO42−), humidity, and time were the most important variables which influenced COR values. The research work could contribute significantly to geotechnical engineering with an optimal ANN model to predict the COR values of reinforcement metal in MSE walls.

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Data availability

All data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Abbreviations

ANN:

Artificial neural network

COR:

Corrosion of reinforcement

MSE walls:

Mechanically stabilized earth walls

R :

Coefficient of correlation

MSE:

Mean squared error

RMSE:

Root mean squared error

R 2 :

Coefficient of determination

w :

Weight

θ :

Bias

W :

Humidity

R es :

Resistivity

b :

Average width

E 0 :

Average thickness

N :

Tensile strength

F :

Tensile force of reinforcement

ε :

Elongation

E zn :

Zinc thickness

t :

Time

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Acknowledgements

This work was supported by The University of Danang, University of Science and Technology, the code number of Project: T2022-02-21.

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  1. Faculty of Road and Bridge Engineering, The University of Danang – University of Science and Technology, 54 Nguyen Luong Bang Str., Danang City, Viet Nam

    Thu-Ha Nguyen, Truong-Linh Chau, Tung Hoang & Teron Nguyen

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Correspondence to Truong-Linh Chau or Tung Hoang.

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Nguyen, TH., Chau, TL., Hoang, T. et al. Developing artificial neural network models to predict corrosion of reinforcement in mechanically stabilized earth walls. Neural Comput & Applic 35, 6787–6799 (2023). https://doi.org/10.1007/s00521-022-08043-1

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