Improved dataset for establishing novel relationships between compaction characteristics and physical properties of soils

Abstract

In the past, several studies were performed for assessment of compaction properties of different types of soils. A comprehensive evaluation of compaction parameters is essential for engineers working in practice. The main goals of compaction in landfills including highways and railways can be listed as reducing permeability and developing strength as well as enhancing the stability of soils. Literature includes various correlations proposed for establishing the link between the compaction properties of soils and Atterberg limits. Besides, many researchers performed laboratory studies to obtain correlations among soil index, strength, compression, and compaction characteristics of soils. In this study, in addition to authors' own data composed of compaction, strength, index, and consistency identifiers of sand-clay mixtures from three different types of sands (S1, S2, Q) and two types of clays (kaolinite and bentonite), a vast amount of data from past studies including tests on different types of soils around the world were also compiled. The global database was evaluated to propose novel correlative relationships among compaction characteristics, grain size distribution properties, and Atterberg limits. Proposed equations and relationships for estimation of compaction characteristics seem to be viable to use in practice.

Effects of Lime Content on Road Performance of Low Liquid Limit Clay

“…and chemical approaches. The three primary additions utilized in chemical stability are lime, cement, and fly ash [5][6][7][8][9]. Due to its low cost, positive effects, and practical construction, lime has drawn the interest of several researchers and produced many research-related findings.…”

A Simple Relationship to Estimate Parameters of the Optimum Compaction Point

“…In some situations, for example, at the prefeasibility stage of a project (before conducting the required tests), it may be useful to estimate γ dmax and w Opt to obtain an early overview of the conditions that can be anticipated. In this regard, various predictive models have been developed to estimate γ dmax using routinely available data such as the liquid limit (w L ) and the plastic limit (w P ), the pasticity index (I p ), gravel content, sand content, and fine-grained content [10][11][12][13][14][15][16][17][18]. The proposed equations were based on multilinear regression or linear regression and their results were validated using literature data.…”