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Ferry Fatnanta
Universitas Riau
Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Nursing Physics Public Health Social Sciences Transportation Other

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Journal : Journal of Geoscience, Engineering, Environment, and Technology

 1 
Sensitivity Analysis Based on Physical Properties to Permeability Coefficient of Cohesive Soil Using Artificial Neural Network Fatnanta, Ferry; Suprayogi, Imam; Nugroho, Soewignjo Agus; Satibi, Syawal; Saputra, Riola
Journal of Geoscience, Engineering, Environment, and Technology Vol. 9 No. 1 (2024): JGEET Vol 09 No 01 : March (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2024.9.1.13536

Abstract

Permeability is the ability of a soil to allow liquids to pass through. Of course the soil has a physical characteristic that can be known by laboratory testing. This study aims to determine the physical properties that most affect the coefficient of cohesive soil permeability using the Artificial Neural Network (ANN) tool, the results obtained will later be matched with actual conditions according to the context of engineering geology. The research method begins with an influence or sensitivity analysis using ANN which will produce a correlation coefficient (R). Then, these results will be compared with the influence analysis based on the value of the coefficient of determination (R2). After that, accuracy and error tests will be carried out using the Mean Absolute Percentage Error (MAPE), the highest accuracy values is categorized as the most influential physical property of the 7 physical property parameters, namely liquid limit, plastic limit, plasticity index, %sand, %fines, %silt, and %clay. Based on the result of the analysis, %fines is the parameter that most influences permeability and is able to make very strong predictions with an R value using an ANN of 0.9941875, an R2 value of 0.6336, an accuracy of 99.6962%, and a MAPE of 0.3038%. These results are compared with the existing empirical equations with an accuracy of 96.4393% and MAPE of 3.5607%. It can be concluded that ANN is more effective and optimal in making predictions. In this case, in the context of engineering geology, the more %fines, the smaller the permeability coefficient of the soil.
Correlation of CBR Values and Mackintosh Probe on Clay Soil with Variations of Bentonite, Kaolin and Sand Nugroho, Soewignjo Agus; Fatnanta, Ferry; Wibisono, Gunawan; Muliyono
Journal of Geoscience, Engineering, Environment, and Technology Vol. 10 No. 4 (2025): JGEET Vol 10 No 04 : December (2025)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2025.10.4.24252

Abstract

Coastal areas are typically characterized by non-uniform soil properties, often featuring soft, water-saturated soils with high plasticity, which frequently results in low soil bearing capacity. This study aims to investigate the relationship between California Bearing Ratio (CBR) values and Mackintosh Probe (MP) test results by utilizing a mixture of clay soil comprising bentonite and kaolin with sand in various compositions. These mixtures were prepared as laboratory test samples to simulate the soil conditions in these areas. The primary objective of this research is to develop a faster and more efficient alternative method for estimating soil bearing capacity in coastal regions. A total of 81 samples were prepared with variations in moisture content, compaction levels, and the composition of sand and clay mixtures. Testing was conducted using both CBR and MP methods. The analysis revealed a significant positive correlation between MP and CBR values, represented by the linear regression model: CBR = 0.7498 * MP, with a coefficient of determination (R²) of 0.9542. This indicates that approximately 95.42% of the variation in CBR values can be predicted from the MP test results. The model's accuracy was further validated through training and testing using 5 randomly selected data points from the sample set. The findings suggest that the Mackintosh Probe can serve as a preliminary tool for estimating soil bearing capacity in coastal areas, particularly in field conditions where laboratory equipment is limited. However, for broader applicability, further validation of this model is necessary to accommodate more complex soil conditions in the field.
Co-Authors ', Muhardi . Zulfan Abdul Halim Muqorrobin Abdullah Ihsan Alkubuwi Achmad Noerkhaerin Putra Adi, Sapria Adi, Sapria Adnan Ruziq Ihsan Agus Ika Putra Agus Nugroho, Soewignjo Aisyah Putri Al Ridho, Muhammad Faisal Albajili, Farian Alfian Alfian Alfian Malik Alridho, Muhammad Faizal Andius Dasa Putra Anggara, Vebrian Aprillia. H.R., Bella Aras Mulyadi Ari Sandhyavitri Arief Rahman Arifan Farhan Ariza, Ariza Azizah . Azra Zulnasari Bramson P Manik, Bramson P Charles AN Daniel Irfan Dede Subhan Dodi Pratama Dodi Pratama Doris Ade Widyarti Effendi Sianipar Ela Fitriana Endala Siboro Epi Mili Yanti Erwin Satria Anugrah Fachry Abda El Rahman Fadel Muhammad Fadhilah, Randy Fadhlurrahman, Fadhlurrahman Fadlan Fadlan Fajrian Saddek Fauzan Hidayattullah Fauzi, Manyuk FERI SETIAWAN Fikri Ananda Fikri Hidayat Frimadofi Frimadofi Galang Maulana Gina Purnama Sari Giri Prayoga Gunawan Wibisono Gunawan Wibisono Gussyafri Gustika, Rani Ardeylina Hadiyan Putra, Hadiyan Haryo Dwito Armono Hasibuan, Putra S Hengki Tornando Herli Fajri Himmatul Azizah HR, Bella Aprilia Ihsan, Adnan Ruziq Imam Hanafi Imam Suprayogi Irfan Hasan Iskandar Romey Sitompul Joshua, Norman Julperizal Julperizal Khairatu Zaro Laksono Trisnantoro Lembasi, Muhammad Khadafi Lingga Panji Subrata, Lingga Panji Lisa Trisnawati Lubis, Shakila Fuadah M Faizal Alridho M. Yusa Maulidi, M Aldi Monita Olivia Mufriadi Mufriadi Muhamad Yusa Muhammad Ikhsan Muhammad Khadafi Lembasi Muhammad Naufal Muhammad Rico Saputra Muhammad Safrianda Muhammad Yevizal Muhammad Yusa Muhardi Muhardi Muhardi Mukhelnalis Sutazril Muliyono Nova, Septi Devita Novan, Andre Ongko, Andarsin Prayogo, Giri Rafika Rani Zainuddin Rahman, Sayful Ralan Ditra Ranata, Nicola Rabb Remon Muslim Riady, Azhar Rinaldi Rinaldi Rinaldi Rinaldi Rio Hidayat Rio Zambika Rizqi Yuliana Rizqy Ridho Prakasa Robi Handi Putra Ronny Tigor Sitanggang S A Nugroho Said Defri Ariandi Saputra, Adetia Saputra, Riola Satibi, Syawal Satria, Zoni Sayoga, Davin Simanjuntak, Lambok Jadiaman Soewignjo Agus Nugroho Soewignjo Agus Nugroho Sofyan Subhan, Dede Suhaimi Siregar Suratman Suratman Suryadi Ramadhan Sutikno, Sigit Syamsul Arifin Tiara Mahardika Tornando, Hengki Trimaijon Trimaijon Trimaijon, Trimaijon Unzi Marwan Usman Muhammad Tang Usman, Fauzan Vebrian Anggara Wahyudi Citrosiswoyo Wahyudi Citrosiswoyo Wahyudi Citrosiswoyo Widi Agoes Pratikto Widi Pratikto Widi Pratikto, Widi Wulan Sri Rahayu Wulandari, Deny Yolanda Widyan Yoyon Kurniawan Yusa, M. Yusa, Muhammad Zoni Satria Zulnasari, Azra