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

International Journal of artificial intelligence research

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Title Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization
Creator Fernando, Hendra
Nugroho, Soewignjo Agus
Suryanita, Reni
Kikumoto, Mamoru
Subject Artificial Intelligence; Computer; Science
Artificial Neural Network; Normalization Data; Cohesive Soil; SPT Value; Cone Penetration Test
Description 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.
Publisher STMIK Dharma Wacana
Date 2021-07-30
Type info:eu-repo/semantics/article
Peer-reviewed Article
Format application/pdf
Source International Journal of Artificial Intelligence Research; Vol 5, No 2 (2021): December; 123 - 131
Language eng
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research

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