This study aimed to examine the shear strength characteristics of sand–granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0%, 10%, 20%, 30%, and 50%). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr–Coulomb theory, which applies to rigid particles only, is not applicable in sand–rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand–rubber mixtures.

More from our Archive

• DOI: 10.3390/rs15133402 2023

  Hyperspectral Image Classification Based on Fusing S3-PCA, 2D-SSA and Random Patch Network

  Huayue Chen, Tingting Wang, Tao Chen, Wu Deng

• DOI: 10.3390/rs15143534 2023

  Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

  Godson Ebenezer Adjovu, Haroon Stephen, David James, Sajjad Ahmad

• DOI: 10.1029/2022gl102493 2023

  Projected Changes in Hot, Dry, and Compound Hot‐Dry Extremes Over Global Land Regions

  Paolo De Luca, Markus G. Donat

• DOI: 10.3390/geosciences13070197 2023

  Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning

  Firas Daghistani, Abolfazl Baghbani, Hossam Abuel Naga, Roohollah Shirani Faradonbeh

• DOI: 10.3390/rs15133327 2023

  Coseismic Faulting Model and Post-Seismic Surface Motion of the 2023 Turkey–Syria Earthquake Doublet Revealed by InSAR and GPS Measurements

  Jing-Jing Zhao, Qiang Chen, Ying-Hui Yang, Qian Xu

• DOI: 10.1093/oxfimm/iqad006 2023

  The current state and future of T-cell exhaustion research

  Edward Jenkins, Toby Whitehead, Martin Fellermeyer, Simon J Davis, Sumana Sharma

• DOI: 10.3390/rs15133364 2023

  Unveiling Air Pollution in Crimean Mountain Rivers: Analysis of Sentinel-5 Satellite Images Using Google Earth Engine (GEE)

  Vladimir Tabunschik, Roman Gorbunov, Tatiana Gorbunova

• DOI: 10.3390/rs15133314 2023

  A Benchmark for Multi-Modal LiDAR SLAM with Ground Truth in GNSS-Denied Environments

  Ha Sier, Qingqing Li, Xianjia Yu, Jorge Peña Queralta, Zhuo Zou, Tomi Westerlund

• DOI: 10.3390/rs15143569 2023

  UAV-Based Terrain Modeling in Low-Vegetation Areas: A Framework Based on Multiscale Elevation Variation Coefficients

  Jiaxin Fan, Wen Dai, Bo Wang, Jingliang Li, Jiahui Yao, Kai Chen

• DOI: 10.3390/rs15143556 2023

  Investigation of the Vertical Distribution Characteristics and Microphysical Properties of Summer Mineral Dust Masses over the Taklimakan Desert Using an Unmanned Aerial Vehicle

  Xiaowen Zhou, Tian Zhou, Shuya Fang, Bisen Han, Qing He