ANNs-Based Prediction Models for Consistency and Compaction Characteristics of Bentonite–Sand Mixtures - Yücel, Melda; Akbay Arama, Zülal; GENÇDAL, HAZAL BERRAK; BAŞBUĞ, BEGÜM; SEÇKİN, EDİP (Springer Nature, 2024)
This study is fictionalized with the use of ANNs logic to estimate the compaction parameters of bentonite–sand mixtures. Totally 230 sets of tests were digitized from the nine well-accepted literature sources to specify the grain size, consistency, and compaction parameters of the bentonite–sand mixtures. Matlab R2018a software is used to perform the estimation process of the compaction parameters, and representative expressions were derived to ease the determination process of mixtures. Consequently, the applicability of the suggested expressions has been checked by the determination and comparison of well-known international metric measurements. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
1st International conference on Mediterranean Geosciences Union, MedGU 2021, 25 November 2021through 28 November 2021
Keywords: Artificial neural networks (ANNs); Bentonite–sand mixtures; Compaction; Consistency; Prediction