The effect of geotechnical soil properties on cbr value: review

Abstract This review paper summarizes the current state of research on relationships between geotechnical soil’s properties and the California Bearing Ratio (CBR) value. Geotechnical elements are pivotal in preventing civil engineering projects from collapses and settlement failures, so understanding detailed soil properties is an important task. CBR tests are used to assess the stiffness modulus and shear strength and guide the overlaying layer’s thickness in pavement designs. Despite such tests’ high expense and complexity, researchers have explored correlations and machine learning for CBR prediction from soil properties. This paper would delve into the varying influence of such properties as compaction properties (OMC and MDD) and index properties (LL, PL, and PI). By measuring the relevance of these properties to CBR, this paper examines their significance and potential interactions. In sum, this review sheds light on soil properties’ multifaceted effects on CBR value and provides support for informed pavement engineering decisions.

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Publication Details

Journal
AI in Civil Engineering
Published
2024-11-07
DOI
https://doi.org/10.1007/s43503-024-00039-1
Citations
4
Primary Topic
Landslides and related hazards
Type
article
Field-Weighted Citation Impact
2.84
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article

The effect of geotechnical soil properties on cbr value: review

Jerome Anabannye Yendaw, Botlhe B. Pule
4 citations
AI in Civil Engineering
Landslides and related hazards
2.84
article

The effect of geotechnical soil properties on cbr value: review

Jerome Anabannye Yendaw, Botlhe B. Pule
article en
4 citations

Abstract

Abstract This review paper summarizes the current state of research on relationships between geotechnical soil’s properties and the California Bearing Ratio (CBR) value. Geotechnical elements are pivotal in preventing civil engineering projects from collapses and settlement failures, so understanding detailed soil properties is an important task. CBR tests are used to assess the stiffness modulus and shear strength and guide the overlaying layer’s thickness in pavement designs. Despite such tests’ high expense and complexity, researchers have explored correlations and machine learning for CBR prediction from soil properties. This paper would delve into the varying influence of such properties as compaction properties (OMC and MDD) and index properties (LL, PL, and PI). By measuring the relevance of these properties to CBR, this paper examines their significance and potential interactions. In sum, this review sheds light on soil properties’ multifaceted effects on CBR value and provides support for informed pavement engineering decisions.

AI in Civil EngineeringVol. 3(1)
Botswana International University of Science and Technology (BW)
Openalex Percentile: Top 9%
Landslides and related hazards
2.84
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