Akbay Arama, ZülalGENÇDAL, HAZAL BERRAK2023-04-042023-04-042022Arama, Z. A., & Gençdal, H. B. (2022). Simple regression models to estimate the standard and modified proctor characteristics of specific compacted fine-grained soils. In Proceedings of the 7th world congress on civil, structural, and environmental engineering (pp. 1-9).978-192787799-92371-5294https://doi.org/10.11159/icgre22.232https://hdl.handle.net/11413/8421– This study is formed to obtain regression models both for the prediction of Standard compaction and Modified compaction characteristics of cohesive soils with the use of simple laboratory tests. For this purpose, a sequential research process has been performed considering both experimental applications and empirical prediction steps to interpret the relationship between the obtained optimum water content and maximum dry unit weight values of cohesive soils via the conducted standard proctor and modified proctor tests. The simple material characteristics of the soils have been obtained by performing sieve analysis and consistency limit tests. With an aim to relate the grain size characteristics and consistency parameters with both of the compaction test outputs two dimensional regression analyses have been conducted. In addition, the availability of Modified Proctor test results in terms of the Standard Proctor test were also investigated. Consequently, mathematical expressions have been achieved to determine both the compaction characteristics in terms of fine content ratio and liquid limit values and also the results of the Modified Proctor tests were obtained with high accuracy by the use of Standard Proctor tests. © 2022, Avestia Publishing. All rights reserved.eninfo:eu-repo/semantics/openAccessCompacted Fine-grained SoilsModified Proctor Test (MP)Parameter PredictionStandard Proctor Test (SP)Simple Regression Models to Estimate the Standard and Modified Proctor Characteristics of Specific Compacted Fine-Grained Soils7th World Congress on Civil, Structural, and Environmental Engineering, CSEE 2022conferenceObject2-s2.0-85139069191