Modeling packing density of granular mixtures: An artificial intelligence approach

Siavash Manafi Khajeh Pasha, H. Hazarika, S. P.G. Madabhushi, N. Yoshimoto

Research output: Contribution to conferencePaper

Abstract

The minimum and maximum packing density of soil-Scrap Tire Derived Materials are often estimated based on limited laboratory test results or to some extent, an empirical correlation. However precise modeling of void ratio characteristics of such materials is complex and usually involves many parameters might be beyond the capability of most of common physically based engineering methods. To solve this issue, Artificial Neural Network (ANN) method is used for simulating maximum and minimum packing density of Gravel-Tire Chips mixtures (GTCM). In this study, a series of maximum and minimum void ratio tests were conducted on GTCM with different fraction of gravel in mixture (GF=VG /VT ) at different mean particle size ratio of tire chips to gravel (D50,R /D50,G ). The outcome revealed that the ANN model is able to precisely predict void ratios of binary mixtures.

Original languageEnglish
Publication statusPublished - 2020
Event16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019 - Taipei, Taiwan, Province of China
Duration: Oct 14 2019Oct 18 2019

Conference

Conference16th Asian Regional Conference on Soil Mechanics and Geotechnical Engineering, ARC 2019
CountryTaiwan, Province of China
CityTaipei
Period10/14/1910/18/19

All Science Journal Classification (ASJC) codes

  • Geotechnical Engineering and Engineering Geology

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