Enhanced Adaptive Neuro-Fuzzy Inference System Using Reptile Search Algorithm for Relating Swelling Potentiality Using Index Geotechnical Properties: A Case Study at El Sherouk City, Egypt; Mathematics; Vol. 9, iss. 24
| Parent link: | Mathematics Vol. 9, iss. 24.— 2021.— [3295, 13 p.] |
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| Müşterek Yazar: | |
| Diğer Yazarlar: | , , , , |
| Özet: | Title screen The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets. |
| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
2021
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| Konular: | |
| Online Erişim: | https://doi.org/10.3390/math9243295 |
| Materyal Türü: | Elektronik Kitap Bölümü |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=667789 |
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| 200 | 1 | |a Enhanced Adaptive Neuro-Fuzzy Inference System Using Reptile Search Algorithm for Relating Swelling Potentiality Using Index Geotechnical Properties: A Case Study at El Sherouk City, Egypt |f А. А. El-Shinawi, R. Ali Ibrahim, L. Abualigah [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 21 tit.] | ||
| 330 | |a The swelling potentiality is a vital property of fine-grained soils strictly related to the index properties and chemical composition. The integration of machine learning techniques and geotechnical parameters provided a new integrative approach for predicting the free swelling index (FSI) and the swelling pressure (SP). In this paper, an adaptive neuro-fuzzy inference system (ANFIS) using named Reptile Search Algorithm (RSA) is presented to predict the swelling potentiality for fine-grained soils in the foundation bed at El Sherouk city, Egypt. The developed predictive model, named RSA-ANFIS, used as input measured 108 natural fine-grained soil samples of index geotechnical parameters and chemical composition as input data and the measured data of the free swelling index and the swelling pressure as output data. To justify the performance of the developed model, a comparative study was carried out, and the results show that the developed RSA-ANFIS has a high performance over the competitive methods in terms of coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE). This new integrative approach is considered at the highly developed stage to predict and improve the analysis of multi-parameter soil behavior and could be applied in other objective variable datasets. | ||
| 461 | |t Mathematics | ||
| 463 | |t Vol. 9, iss. 24 |v [3295, 13 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a machine learning techniques | |
| 610 | 1 | |a liquid limit | |
| 610 | 1 | |a clay fraction | |
| 610 | 1 | |a swelling potentiality | |
| 610 | 1 | |a машинное обучение | |
| 610 | 1 | |a жидкости | |
| 610 | 1 | |a глинистые фракции | |
| 610 | 1 | |a набухание | |
| 610 | 1 | |a нейро-нечеткие системы | |
| 610 | 1 | |a алгоритмы поиска | |
| 610 | 1 | |a геотехнические свойства | |
| 701 | 1 | |a El-Shinawi |b А. А. |g Abdelaziz | |
| 701 | 1 | |a Ali Ibrahim |b R. |g Rehab | |
| 701 | 1 | |a Abualigah |b L. |g Laith | |
| 701 | 1 | |a Zelenakova |b M. |g Martina | |
| 701 | 1 | |a Mokhamed Elsaed (Mohamed Abd Elaziz) |b A. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University |f 1987- |g Akhmed Mokhamed |3 (RuTPU)RU\TPU\pers\46943 | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |c 2017- |x TPU |7 ca |8 rus |9 28330 |
| 801 | 0 | |a RU |b 63413507 |c 20220421 |g RCR | |
| 856 | 4 | |u https://doi.org/10.3390/math9243295 | |
| 942 | |c CF | ||