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Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization Soewignjo Agus Nugroho; Hendra Fernando; Reni Suryanita
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp210-220

Abstract

Artificial neural networks (ANNs) are often used recently by researchers to solve complex and nonlinear problems. Standard penetration test (SPT) and cone penetration test (CPT) are field tests that are often used to obtain soil parameters. There have been many previous studies that examined the value obtained through the SPT test with the CPT test, but the research carried out still uses equations that are linear. This research will conduct an estimated value of SPT on cohesive soil using CPT data in the form of end resistance and blanket resistance, and laboratory test data such as effective overburden pressure, liquid limit, plastic limit and percentage of sand, silt and clay. This study used 242 data with testing areas in several cities on the island of Sumatra, Indonesia. The developed artificial neural network will be created without data normalization. The final results of this study are in the form of root mean square error (RMSE) values 3.441, mean absolute error (MAE) 2.318 and R2 0.9451 for training data and RMSE 2.785, MAE 2.085, R2 0.9792 for test data. The RMSE, MAE and R2 values in this study indicate that the ANN that has been developed is considered quite good and efficient in estimating the SPT value.
Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization Hendra Fernando; Soewignjo Agus Nugroho; Reni Suryanita; Mamoru Kikumoto
International Journal of Artificial Intelligence Research Vol 5, No 2 (2021): December 2021
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (445.835 KB) | DOI: 10.29099/ijair.v5i2.208

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.
Prediction of standard penetration test value on cohesive soil using artificial neural networks Soewignjo Agus Nugroho; Hendra Fernando; Reni Suryanita
Jurnal Informatika Vol 15, No 2 (2021): May 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jifo.v15i2.a19822

Abstract

Soil investigation is the main key in starting construction. Standard Penetration Test (SPT) and Cone Penetration Test (CPT) are field tests often used to estimate soil parameters for foundation design purposes. The SPT value (N-SPT) shows a correlation between the CPT value and other soil parameters. At present, there have been many conventional correlations examining these correlations, but the nonlinear nature of the soil due to very complex soil formations means that this correlation cannot be used in all situations. This research aimed to predict the value of SPT on cohesive soil based on CPT test data and soil physical properties using artificial neural network capabilities using the Backpropagation algorithm, and the activation function was bipolar sigmoid. This study used 284 data from several places in Sumatra Island, Indonesia, with data input were tip resistance, shaft resistance, effective overburden pressure, percentage of liquid limit, plastic limit, sand, silt, and clay. The results showed that the training data of RMSE was 3.441, MAE and R2 were 0.9451 and 2.318, respectively while test data showed RMSE, MAE, R2 were 2.785, 2.085, and 0.9792, respectively. It means that the proposed artificial neural network NN_Nspt(C) was promising to predict the N-SPT value with a minimum error value and a strong regression equation.
Prediksi Nilai SPT Pada Tanah Non Kohesif Berdasarkan Data CPT dan Sifat Fisik Tanah Menggunakan Jaringan Saraf Tiruan Soewignjo Agus Nugroho; Hendra Fernando; Reni Suryanita
Jurnal Teknik Sipil Vol 29 No 1 (2022): Jurnal Teknik Sipil
Publisher : Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/jts.2022.29.1.5

Abstract

Abstrak Standard Penetration Test (SPT) dan Cone Penetration Test (CPT) merupakan tes penyelidikan tanah awal yang sering digunakan saat memulai suatu konstruksi. Telah banyak penelitian sebelumnya yang membahas tentang korelasi linier antara nilai SPT dan CPT, namun nilai koefisien korelasinya (R2) cenderung kecil. Jaringan saraf tiruan (JST) merupakan teknik yang dapat memecahkan masalah yang kompleks dan non-linier. Pada penelitian ini akan dilakukan prediksi nilai SPT menggunakan jaringan saraf tiruan pada tanah granular menggunakan algoritma backpropagation. Panelitian ini menggunakan 117 data dari beberapa wilayah di Provinsi Riau. Data masukan yang digunakan berupa nilai tahanan ujung (qc) dan nilai tahanan selimut (fs) dari pengujian CPT dan nilai tekanan overburden efektif (σ'0) serta persentase pasir dan butiran halus. JST dianggap efektif dalam penelitian ini dengan nilai RMSE 3,646, MAE 2,533 dan R2 0,9103 untuk data latih dan RMSE 2,955, MAE 2,190, R2 0,9311 untuk data uji. Selanjutnya model JST ini disebut sebagai NN_Nspt (NC). Kata-kata Kunci: back-propagation, CPT, granular, Jaringan Saraf Tiruan, SPT Abstract The Standard Penetration Test (SPT) and the Cone Penetration Test (CPT) are kinds of Soil Investigation Tests that are used to determine bearing capacity and soil parameters for designing a construction. There are many previous studies had been defined the linear correlation between SPT and CPT values. However, the linear correlation predisposed get correlation coefficient (R2) small. Artificial neural networks (ANN) is an Artificial Intelligence model that can solve complex and non-linear problems. This research aims to conduct SPT value prediction using ANN in granular soil (non-cohesive) with a backpropagation algorithm function. This study used 117 data taken from several provinces on Sumatera island. The variables of input data are taken from CPT, i.e cone resistance (qc)and sleeve resistance (fs), and from the UDS test. The laboratory data were effective overburden pressure (σ‘0), the percentage of sand, and the percentage of fine grain. The best ANN model had a single hidden layer and 40 neurons with RMSE values 3.646, MAE 2.533, and R2 0.9103 for training data and RMSE 2.955, MAE 2.190, R2 0.9311 for testing data. Thus, the best ANN model has been proposed as NN_Nspt (NC). Keywords: Artificial Neuron Network, back-propagation, CPT, granular, SPT