Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization

Nugroho, Soewignjo Agus; Fernando, Hendra; Suryanita, Reni

doi:10.11591/ijai.v11.i1.pp210-220

Akses terbuka Jurnal SINTA 1

Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization

IAES International Journal of Artificial Intelligence · Vol. 11 No. 1 (2022) · hlm. 210–220

DOI: 10.11591/ijai.v11.i1.pp210-220
ISSN: 2252-8938, 2089-4872
Terbit: 2022-03-01
Penerbit: Institute of Advanced Engineering and Science
Bahasa: eng
Lisensi: creativecommons.org/licenses/by-sa/4.0

Abstrak

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.

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Riau University
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Hasanuddin University
Yamaguchi University
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Rekan penulis	Organisasi	Artikel bersama
Zulkifli Zulkifli	Hasanuddin University	1
Rinaldi Rinaldi	Riau University	1
Sigit Sutikno	Riau University	1
Syawal Satibi	Riau University	1
Agus Ika Putra	Riau University	1
Muhamad Yusa	Riau University	1
Koichi Yamamoto	Yamaguchi University	1
Randhi Saily	Riau University	1
Harnedi Maizir	Sekolah Tinggi Teknologi Pekanbaru	1
Raihan Arditama Harnedi	Afiliasi tidak diketahui	1
Intan Monica.MG	Riau University	1
Vinka Lyona	Riau University	1
Sjelly Haniza	Sekolah Tinggi Teknologi Pekanbaru	1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">Estimation of standard penetration test value on cohesive soil using artificial neural network without data normalization</dc:title>
	<dc:creator>Nugroho, Soewignjo Agus</dc:creator>
	<dc:creator>Fernando, Hendra</dc:creator>
	<dc:creator>Suryanita, Reni</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Artificial neural network; Cohesive soil; Cone penetration test; Data normalization; Standard penetration test</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
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	<dc:date>2022-03-01</dc:date>
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	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/21420</dc:identifier>
	<dc:identifier>10.11591/ijai.v11.i1.pp210-220</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 11, No 1: March 2022; 210-220</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v11.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21420/13318</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 Institute of Advanced Engineering and Science</dc:rights>
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