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Prediction of California Bearing Ratio (CBR) Using Index Soil Properties and Compaction Parameters of Low Plastic Fine-Grained Soil
https://orcid.org/http://orcid.org/0000-0003-1792-0770
California bearing ratio (CBR) is a significant test for geotechnical and highway engineering. CBR requires extensive time and physical effort, which affects the productivity of the projects. This study aims to develop correlations to predict CBR with index properties of fine-grained soils. Several natural soil samples were collected from different areas of Islamabad, Pakistan. Geotechnical laboratory tests of grain size analysis, specific gravity, Atterberg limits, standard Proctor, and California bearing ratio were performed. Multi-linear regression analysis (MLRA) was performed using statistical analysis software, SPSS. Multiple predictive models for CBR were developed using SPSS in three stages, each stage with a different set of index soil properties as input variables. Based on this study, it is observed that CBR of fine-grained soils can be predicted with excellent accuracy using index soil properties and compaction parameters as input parameters with high R2 values ranging from 0.786 to 0.957 and significance of 0.000 for all predictive models. MLRA models presented in this study are based on low plastic fine-grained soils and might not be suitable to predict CBR for high plastic or course-grained soils.
Prediction of California Bearing Ratio (CBR) Using Index Soil Properties and Compaction Parameters of Low Plastic Fine-Grained Soil
https://orcid.org/http://orcid.org/0000-0003-1792-0770
California bearing ratio (CBR) is a significant test for geotechnical and highway engineering. CBR requires extensive time and physical effort, which affects the productivity of the projects. This study aims to develop correlations to predict CBR with index properties of fine-grained soils. Several natural soil samples were collected from different areas of Islamabad, Pakistan. Geotechnical laboratory tests of grain size analysis, specific gravity, Atterberg limits, standard Proctor, and California bearing ratio were performed. Multi-linear regression analysis (MLRA) was performed using statistical analysis software, SPSS. Multiple predictive models for CBR were developed using SPSS in three stages, each stage with a different set of index soil properties as input variables. Based on this study, it is observed that CBR of fine-grained soils can be predicted with excellent accuracy using index soil properties and compaction parameters as input parameters with high R2 values ranging from 0.786 to 0.957 and significance of 0.000 for all predictive models. MLRA models presented in this study are based on low plastic fine-grained soils and might not be suitable to predict CBR for high plastic or course-grained soils.
Prediction of California Bearing Ratio (CBR) Using Index Soil Properties and Compaction Parameters of Low Plastic Fine-Grained Soil
https://orcid.org/http://orcid.org/0000-0003-1792-0770
California bearing ratio (CBR) is a significant test for geotechnical and highway engineering. CBR requires extensive time and physical effort, which affects the productivity of the projects. This study aims to develop correlations to predict CBR with index properties of fine-grained soils. Several natural soil samples were collected from different areas of Islamabad, Pakistan. Geotechnical laboratory tests of grain size analysis, specific gravity, Atterberg limits, standard Proctor, and California bearing ratio were performed. Multi-linear regression analysis (MLRA) was performed using statistical analysis software, SPSS. Multiple predictive models for CBR were developed using SPSS in three stages, each stage with a different set of index soil properties as input variables. Based on this study, it is observed that CBR of fine-grained soils can be predicted with excellent accuracy using index soil properties and compaction parameters as input parameters with high R2 values ranging from 0.786 to 0.957 and significance of 0.000 for all predictive models. MLRA models presented in this study are based on low plastic fine-grained soils and might not be suitable to predict CBR for high plastic or course-grained soils.
Prediction of California Bearing Ratio (CBR) Using Index Soil Properties and Compaction Parameters of Low Plastic Fine-Grained Soil
Transp. Infrastruct. Geotech.
Hassan, Jawad (author) / Alshameri, Badee (author) / Iqbal, Faizan (author)
Transportation Infrastructure Geotechnology ; 9 ; 764-776
2022-12-01
13 pages
Article (Journal)
Electronic Resource
English
California bearing ratio (<italic>CBR</italic>) , Multi-linear regression analysis (MLRA) , SPSS , Maximum dry density (<italic>MDD</italic>) , Optimum moisture content (<italic>OMC</italic>) , Index soil properties Engineering , Geoengineering, Foundations, Hydraulics , Geotechnical Engineering & Applied Earth Sciences , Building Materials
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