Prediction of standard penetration test value on cohesive soil using artificial neural networks

نویسندگان

چکیده

Soil investigation is the main key in starting construction. Standard Penetration Test (SPT) and Cone (CPT) are field tests often used to estimate soil parameters for foundation design purposes. The SPT value (N-SPT) shows a correlation between CPT other parameters. At present, there have been many conventional correlations examining these correlations, but nonlinear nature of due very complex formations means that this cannot be all situations. This research aimed predict on cohesive based test data physical properties using artificial neural network capabilities Backpropagation algorithm, activation function was bipolar sigmoid. study 284 from several places Sumatra Island, Indonesia, with input were tip resistance, shaft effective overburden pressure, percentage liquid limit, plastic sand, silt, clay. results showed training RMSE 3.441, MAE R2 0.9451 2.318, respectively while RMSE, MAE, were 2.785, 2.085, 0.9792, respectively. It proposed NN_Nspt(C) promising N-SPT minimum error strong regression equation.

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ژورنال

عنوان ژورنال: Jurnal Informatika

سال: 2021

ISSN: ['1411-0105', '2528-5823']

DOI: https://doi.org/10.26555/jifo.v15i2.a19822