Prediction of UCS and CBR of microsilica-lime stabilized sulfate silty sand using ANN and EPR models; application to the deep soil mixing

• Published in: Soils and foundations Vol. 58; no. 1; pp. 34 - 49
• Main Authors: Ghorbani, Ali, Hasanzadehshooiili, Hadi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2018
• Subjects: California bearing ratio; Deep soil mixing; Evolutionary polynomial regression; Microsilica-lime stabilization; Neural networks; Sensitivity analysis; Sulfate silty sand; Unconfined compressive strength; Microsilica-lime stabilization; Sensitivity analysis; Neural networks; Evolutionary polynomial regression; Deep soil mixing; California bearing ratio; Sulfate silty sand; Unconfined compressive strength

Desert sands in Iran, which usually contain small amounts of silt and sulfate, do not have significant strength, and thus, are not suitable for foundations or road construction. This paper applies the results of 90 Unconfined Compressive Strength (UCS) and California Bearing Ratio (CBR) tests on sul...