Influence of cohesion on California bearing ratio of clay-gravel mixtures

An understanding of the behavior of cohesive sediment is required to solve various engineering problems such as scour around bridge elements, mitigation of soil erosion, pavement design, river bed degradation,stable channel design. Pavement foundation designers principally use the California bearing ratio(CBR)to describe the subgrade and subbase materials and their strength. Several laboratory experiments were done to study the variation in the CBR of cohesive mixtures comprised of clay-gravel m...

Influence of cohesion on California bearing ratio of clay–gravel mixtures

An understanding of the behavior of cohesive sediment is required to solve various engineering problems such as scour around bridge elements, mitigation of soil erosion, pavement design, river bed degradation, stable channel design. Pavement foundation designers principally use the California bearing ratio (CBR) to describe the subgrade and subbase materials and their strength. Several laboratory experiments were done to study the variation in the CBR of cohesive mixtures comprised of clay–gravel mixtures. Nine different clay–gravel mixtures were used in which the clay content varies from 10% to 50% by weight. The variation of the CBR with clay percentage, moisture content, and undrained shear strength parameters was studied. The CBR value reduces with the increase in the moisture content and clay fraction in the mixtures and increases with an increase in the dry density of the mixture under unsoaked conditions. The CBR also increases with the increase of the angle of internal friction of clay–gravel mixtures. A functional relation has been identified to estimate the CBR of clay–gravel mixtures. Using multiple linear regression analysis (MLRA), a relation is proposed to estimate the CBR of clay–gravel mixtures under unsoaked conditions. A statistical analysis was done to judge the behavior of the pertinent variables on the CBR. The proposed relation predicts the CBR of clay–gravel mixtures very well. Artificial neural network (ANN) analysis using R programming was also done to determine the effects of the pertinent variables on the CBR. ANN methodology was applied to predict the contribution of each variable. Three different methods: Garson algorithm, Olden algorithm, and Lek's profile model are used to assess the influence of variable parameters. The Olden algorithm and Lek's profile both show positive association of cohesion with CBR in an unsoaked condition. The role of moisture content was found to be marginally negative in the Olden algorithm and Lek's profile results. It is found that both the ANN and MLRA models are accurate in predicting the CBR of clay–gravel mixtures. It was further found that the MLRA and ANN models are reliable and rapid tools for correct assessment of the CBR of cohesive soil mixtures using the basic soil properties.