Predictive model for normalized shear modulus of cohesive soils

Predictive Model for Normalized Shear Modulus of Cohesive Soils

called maximum shear modulus, Gmax. It can be determined by in-situ methods from the measurements of the shear wave velocity, Vs, by means of crosshole, down-hole, up-hole, or surface wave testing. The relationship between maximum shear modulus and shear wave velocity is rigorous: 2 max s G V    where ρ is soil density. This parameter can be also obtained in laboratory by resonant column or ...

A method for measuring the critical shear stress of cohesive soils

Effects of Degree of Consolidation and Anisotropic Consolidation Stresses on Shear Modulus and Damping Ratio of Cohesive Soils at Low Strain

During consolidation process of saturated cohesive soil the soil stiffness increases. Increase of the effective stress due to dissipation of excess pore pressure causes additional stiffness of soil mass. This phenomenon has a very important effect on the behavior of saturated cohesive soils during dynamic loading. In the current investigation the changes in maximum shear modulus. Gmax and dampi...

Performance Evaluation of Dynamic Modulus Predictive Models for Asphalt Mixtures

Dynamic modulus characterizes the viscoelastic behavior of asphalt materials and is the most important input parameter for design and rehabilitation of flexible pavements using Mechanistic–Empirical Pavement Design Guide (MEPDG). Laboratory determination of dynamic modulus is very expensive and time consuming. To overcome this challenge, several predictive models were developed to determine dyn...

A mathematical model for optimization of strength of concrete: A case study for shear modulus of Rice Husk Ash Concrete

Rice Husk Ash (RHA) is natural Pozzolan containing reactive silica and/or aluminum. When the material is mixed with lime in powdered form and in the presence of water, it will set and harden like cement. This work uses Osadebe’s optimization model to optimize the shear modulus of concrete made from RHA. The strengths predicted by the model are in good agreement with their corresponding experime...