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

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

Dettagli Bibliografici
Parent link:Mathematics
Vol. 9, iss. 24.— 2021.— [3295, 13 p.]
Ente Autore: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Altri autori: El-Shinawi А. А. Abdelaziz, Ali Ibrahim R. Rehab, Abualigah L. Laith, Zelenakova M. Martina, Mokhamed Elsaed (Mohamed Abd Elaziz) A. M. Akhmed Mokhamed
Riassunto: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.
Lingua:inglese
Pubblicazione: 2021
Soggetti:
электронный ресурс
труды учёных ТПУ
machine learning techniques
liquid limit
clay fraction
swelling potentiality
машинное обучение
жидкости
глинистые фракции
набухание
нейро-нечеткие системы
алгоритмы поиска
геотехнические свойства
Accesso online:https://doi.org/10.3390/math9243295
Natura: Elettronico Capitolo di libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=667789

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