Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization

(1) Hendra Fernando Mail (Riau University, Indonesia)
(2) * Soewignjo Agus Nugroho Mail (Riau University, Indonesia)
(3) Reni Suryanita Mail (Riau University, Indonesia)
(4) Mamoru Kikumoto Mail (yokohama national university, Japan)
*corresponding author

Abstract


Artificial neural networks (ANN) are now widely used and are becoming popular among researchers, especially in the geotechnical field. In general, data normalization is carried out to make ANN whose range is in accordance with the activation function used. Other studies have tried to create an ANN without normalizing the data and ANN is considered capable of making predictions. In this study, a comparison of ANN with and without data normalization was carried out in predicting SPT values based on CPT data and soil physical properties on cohesive soils. The input data used in this study are the value of tip resistance, sleeve resistance, effective soil overburden pressure, liquid limit, plastic limit and percentage of sand, silt and clay. The results showed that the ANN was able to make predictions effectively both on networks with and without data normalization. In this study, it was found that the ANN without data normalization showed a smaller error value than the ANN with data normalization. In the network model without data normalization, RMSE values were 3.024, MAE 1.822, R2 0.952 on the training data and RMSE 2.163, MAE 1.233 and R2 0.976 on the test data. Whereas in the ANN with data normalization, the RMSE values were 3.441, MAE 2.318, R2 0.936 in the training data and RMSE 2.785, MAE 2.085 and R2 0.963 in the test data. ANN with normalization provides a simpler architecture, which only requires 1 hidden layer compared to ANN without normalization which requires 2 hidden layer architecture.

Keywords


Artificial Neural Network; Normalization Data; Cohesive Soil; SPT Value; Cone Penetration Test

   

DOI

https://doi.org/10.29099/ijair.v5i2.208
      

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