Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials

The California bearing ratio (CBR) and modified proctor parameters (maximum dry unit weight γd(max)γd(max) and optimum moisture content woptwopt) are valuable indicators of the compaction quality of subgrades, embankments and granular fills. In the engineering practice, correlations of these variabl...

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Autores:
Duque, Jose
Fuentes, William
Rey, Silvia
Molina, Enois
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7820
Acceso en línea:
https://hdl.handle.net/11323/7820
https://doi.org/10.1007/s13369-020-04673-6
https://repositorio.cuc.edu.co/
Palabra clave:
California bearing ratio (CBR)
Grain size distribution
Modified proctor
Granular soils
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openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_491547b13603ad4c40264c829c302c9e
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7820
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
title Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
spellingShingle Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
California bearing ratio (CBR)
Grain size distribution
Modified proctor
Granular soils
title_short Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
title_full Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
title_fullStr Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
title_full_unstemmed Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
title_sort Effect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materials
dc.creator.fl_str_mv Duque, Jose
Fuentes, William
Rey, Silvia
Molina, Enois
dc.contributor.author.spa.fl_str_mv Duque, Jose
Fuentes, William
Rey, Silvia
Molina, Enois
dc.subject.spa.fl_str_mv California bearing ratio (CBR)
Grain size distribution
Modified proctor
Granular soils
topic California bearing ratio (CBR)
Grain size distribution
Modified proctor
Granular soils
description The California bearing ratio (CBR) and modified proctor parameters (maximum dry unit weight γd(max)γd(max) and optimum moisture content woptwopt) are valuable indicators of the compaction quality of subgrades, embankments and granular fills. In the engineering practice, correlations of these variables with granulometric properties of the soil are required, especially since testing for these variables can be time-consuming when a large number of samples are analyzed. In this work, 20 different granular materials with varying grain size distributions were prepared and tested. Their grain size distribution properties and their parameters CBR, γd(max)γd(max) and woptwopt were determined. These results were analyzed along with a compilation of 77 additional experimental results on granular materials reported in the literature. The influence of some granulometric properties on the parameters CBR, γd(max)γd(max) and woptwopt was statistically examined, and some correlations were proposed for these variables. Subsequently, it was demonstrated that the proposed correlations show better accuracy tother reported correlations in the literature. Finally, this work ends with some concluding remarks.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-02-03T13:59:09Z
dc.date.available.none.fl_str_mv 2021-02-03T13:59:09Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7820
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/s13369-020-04673-6
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7820
https://doi.org/10.1007/s13369-020-04673-6
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. NCHRP.: Correlation of CBR values with soil index guide for mechanistic and empirical—design for new a rehabilitated pavement structures, Illinois (2001)
2. Satyanarayana, R.; Pavani, K.: Mechanically stabilized soils-regression equation for CBR evaluation. In: Proceedings of the Indian Geotechnical Conference: Geotechnical Engineering - Indian Experience, Chennai, India, pp. 731–734 (2006). ISBN:8184241321
3. Vinod, P.; Reena, C.: Prediction of CBR value of lateritic soils using liquid limit and gradation characteristics data. Highw. Res. J. 1(1), 89–98 (2008)
4. Patel, R.; Desai, M.: CBR predicted by index properties for alluvial soils of South Gujarat. Proceedings of the Indian geotechnical conference, Mumbai, pp. 79–82 (2010)
5. Yildirim, B.; Gunaydin, O.: Estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. 38(5), 6381–6391 (2011)
6. Attique, R.; Khalid, F.; Hassan, M.: Prediction of California bearing ratio (CBR) and compaction characteristics of granular soils. Acta Geotech. Slov. 14(1), 63–72 (2017)
7. Katte, V.; Mfoyet, S.; Manefouet, B.; Ludovic, A.; Bezeng, L.: Correlation of California bearing ratio (CBR) value with soil properties of road subgrade soil. Geotech. Geol. Eng. 37(1), 217–234 (2019)
8. Blotz, L.; Benson, C.; Boutwell, G.: Estimating optimum water content and maximum dry unit weight for compacted clays. J. Geotech. Geoenviron. Eng. 124(9), 907–912 (1998)
9. Omar, M.; Abdallah, S.; Basma, A.; Barakat, S.: Compaction characteristics of granular soils in the United Arab Emirates. Geotech. Geol. Eng. 21(3), 283–295 (2003)
10. Gurtug, Y.; Sridharan, A.: Compaction behavior and prediction of its characteristics of fine grained soils with particular reference to compaction energy. Soils Found. 44(5), 27–36 (2004)
11. Arvelo, A.: Effects of the soil properties on the maximum dry density obtained from the standard proctor test. Master Thesis, University of Central Florida (2004)
12. Huang, Y.: Pavement Analysis and Design, 2nd edn. Pearson Education, London (2004)
13. Mesri, G.; Vardhanabhuti, B.: Compression of granular materials. Can. Geotech. J. 46(4), 369–392 (2009)
14. Mishra, D.; Tutumluer, E.; Butt, A.: Quantifying effects of particle shape and type and amount of fines on unbound aggregate performance through controlled gradation. Transp. Res. Record J. Transp. Res. Board. 2167, 61–71 (2010)
15. Jiang, Y.; Yuen, L.; Ren, J.: A numerical test method of California bearing ratio on graded crushed rocks using particle flow modeling. J. Traffic Transp. Eng. 2(2), 107–115 (2015)
16. Kwon, J.; Kim, S.; Tutumluer, E.; Wayne, M.: Characterization of unbound aggregate materials considering physical and morphological properties. Int. J. Pavement Eng. 18(4), 303–308 (2017)
17. Mendoza, C.; Caicedo, B.: Elastoplastic framework of relationships between CBR and Young’s modulus for granular material. Road Mater. Pavement Des. 19(8), 1796–1815 (2018)
18. Sreelekshmypillai, G.; Vinod, P.: Prediction of CBR value of fine grained soils at any rational compactive effort. Int. J. Geotech. Eng. 13(6), 558–563 (2019)
19. Sivrikaya, O.; Togrol, E.; Kayadelen, C.: Estimating compaction behavior of fine-grained soils based on compaction energy. Can. Geotech. J. 45(6), 877–887 (2008)
20. Matteo, L.; Bigotti, F.; Ricco, R.: Best-fit model to estimate proctor properties of compacted soils. J. Geotech. Geoenviron. Eng. 135(7), 992–996 (2009)
21. Mutjaba, H.; Farooq, K.; Sivakugan, N.; Das, B.: Correlation between gradational parameters and compaction characteristics of sandy soils. Int. J. Geotech. Eng. 7(4), 395–401 (2013)
22. Araujo, W.; Ruiz, G.: Correlation equations of CBR with index properties of soil in the city of Piura. In: 14th LACCEI International Multi Conference for Engineering, Education and Technology: “Engineering Innovations for Global Sustainability‬”, San Jose, Costa Rica, 20–22 July, pp. 1–7 (2016)
23. Janjua, Z.; Chand, J.: Correlation of CBR with index properties of soil. Int. J. Civ. Eng. Technol. 7(5), 57–62 (2016)
24. Aderinola, O.; Emmanuel, O.; Ajibola, Q.: Correlation of California bearing ratio value of clays with soil index and compaction characteristics. Int. J. Sci. Res. Innov. Technol. 4(4), 12–22 (2017)
25. Bekele, A.: Correlation of CBR with index properties of soils in Sulsulta town. Master Thesis, Addis Ababa Institute of Technology (2017)
26. Ardakani, A.; Kordnaeji, A.: Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur. J. Environ. Civ. Eng. 23(4), 449–462 (2019)
27. Black, W.: A method of estimating the California bearing ratio of cohesive soils from plasticity data. Géotechnique 12(4), 271–282 (1962)
28. Korfiatis, G.; Manikopoulos, C.: Correlation of maximum dry density and grain size. J. Geotech. Eng. 108(9), 1171–1176 (1982)
29. Brown, S.: Soil mechanics in pavement engineering. Géotechnique 46(3), 383–426 (1996)
30. Breytenbach, J.; Green, P.; Rooy, J.: The relationship between index testing and California bearing ratio values for natural road construction materials in South Africa. J. S. Afr. Inst. Civ. Eng. 52(2), 65–69 (2010)
31. Ferede, Z.: Prediction of California bearing ratio (CBR) value from index properties of soil. Master Thesis, Addis Ababa Institute of Technology (2012)
30. Breytenbach, J.; Green, P.; Rooy, J.: The relationship between index testing and California bearing ratio values for natural road construction materials in South Africa. J. S. Afr. Inst. Civ. Eng. 52(2), 65–69 (2010)
31. Ferede, Z.: Prediction of California bearing ratio (CBR) value from index properties of soil. Master Thesis, Addis Ababa Institute of Technology (2012)
32. Talukdar, D.: A study of correlation between California Bearing Ratio (CBR) value with other properties of soil. Int. J. Emerg. Technol. Adv. Eng. 4(1), 559–562 (2014)
33. Look, B.: Spatial and statistical distribution models using the CBR test. Aust. Geomech. 44(1), 37–41 (2009)
34. McGough, P.: A method for the prediction of soaked CBR of remolded samples from standard classification tests. Aust. Geomech. 45(4), 75–86 (2010)
35. Patra, C.; Sivakugan, N.; Das, B.; Rout, S.: Correlations for relative density of clean sand with median grain size and compaction energy. Int. J. Geotech. Eng. 4(2), 195–203 (2010)
36. Saklecha, P.; Katpatal, Y.; Rathore, S.; Agarawal, D.: Spatial correlation of mechanical properties of subgrade soil for foundation characterization. Int. J. Comput. Appl. 36(11), 20–25 (2011)
37. Singh, D.; Reddy, K.; Yadu, L.: Moisture and compaction based statistical model for estimating CBR of fine-grained subgrade soils. Int. J. Earth Sci. Eng. 4(6), 100–103 (2011)
38. Leliso, Y.: Correlation of CBR value with soil index properties for Addis Abba subgrade soils. Master Thesis, Addis Ababa University (2013)
39. Gratchev, I.; Pitawala, S.; Gurung, N.; Monteiro, E.: A chart to estimate CBR of plastic soils. Aust. Geomech. J. 53(1), 1–5 (2018)
40. Verma, G.; Kumar, B.: Prediction of compaction parameters for fine-grained and coarse-grained soils: a review. Int. J. Geotech. Eng. (2019).
41. Moreno, N.: Zonificación geotécnica de los suelos en Barranquilla. In: Twelfth LACCEI Latin American and Caribbean Conference for Engineering and Technology, “Excellence in Engineering to Enhance a Country’s Productivity”,‬ Guayaquil, Ecuador, 22–24 July 2014, pp. 1–9 (2014)
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spelling Duque, JoseFuentes, WilliamRey, SilviaMolina, Enois2021-02-03T13:59:09Z2021-02-03T13:59:09Z2020https://hdl.handle.net/11323/7820https://doi.org/10.1007/s13369-020-04673-6Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The California bearing ratio (CBR) and modified proctor parameters (maximum dry unit weight γd(max)γd(max) and optimum moisture content woptwopt) are valuable indicators of the compaction quality of subgrades, embankments and granular fills. In the engineering practice, correlations of these variables with granulometric properties of the soil are required, especially since testing for these variables can be time-consuming when a large number of samples are analyzed. In this work, 20 different granular materials with varying grain size distributions were prepared and tested. Their grain size distribution properties and their parameters CBR, γd(max)γd(max) and woptwopt were determined. These results were analyzed along with a compilation of 77 additional experimental results on granular materials reported in the literature. The influence of some granulometric properties on the parameters CBR, γd(max)γd(max) and woptwopt was statistically examined, and some correlations were proposed for these variables. Subsequently, it was demonstrated that the proposed correlations show better accuracy tother reported correlations in the literature. Finally, this work ends with some concluding remarks.Duque, JoseFuentes, WilliamRey, SilviaMolina, Enoisapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Arabian Journal for Science and Engineering volumehttps://link.springer.com/article/10.1007/s13369-020-04673-6California bearing ratio (CBR)Grain size distributionModified proctorGranular soilsEffect of grain size distribution on california bearing ratio (CBR) and modified proctor parameters for granular materialsArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. NCHRP.: Correlation of CBR values with soil index guide for mechanistic and empirical—design for new a rehabilitated pavement structures, Illinois (2001)2. Satyanarayana, R.; Pavani, K.: Mechanically stabilized soils-regression equation for CBR evaluation. In: Proceedings of the Indian Geotechnical Conference: Geotechnical Engineering - Indian Experience, Chennai, India, pp. 731–734 (2006). ISBN:81842413213. Vinod, P.; Reena, C.: Prediction of CBR value of lateritic soils using liquid limit and gradation characteristics data. Highw. Res. J. 1(1), 89–98 (2008)4. Patel, R.; Desai, M.: CBR predicted by index properties for alluvial soils of South Gujarat. Proceedings of the Indian geotechnical conference, Mumbai, pp. 79–82 (2010)5. Yildirim, B.; Gunaydin, O.: Estimation of California bearing ratio by using soft computing systems. Expert Syst. Appl. 38(5), 6381–6391 (2011)6. Attique, R.; Khalid, F.; Hassan, M.: Prediction of California bearing ratio (CBR) and compaction characteristics of granular soils. Acta Geotech. Slov. 14(1), 63–72 (2017)7. Katte, V.; Mfoyet, S.; Manefouet, B.; Ludovic, A.; Bezeng, L.: Correlation of California bearing ratio (CBR) value with soil properties of road subgrade soil. Geotech. Geol. Eng. 37(1), 217–234 (2019)8. Blotz, L.; Benson, C.; Boutwell, G.: Estimating optimum water content and maximum dry unit weight for compacted clays. J. Geotech. Geoenviron. Eng. 124(9), 907–912 (1998)9. Omar, M.; Abdallah, S.; Basma, A.; Barakat, S.: Compaction characteristics of granular soils in the United Arab Emirates. Geotech. Geol. Eng. 21(3), 283–295 (2003)10. Gurtug, Y.; Sridharan, A.: Compaction behavior and prediction of its characteristics of fine grained soils with particular reference to compaction energy. Soils Found. 44(5), 27–36 (2004)11. Arvelo, A.: Effects of the soil properties on the maximum dry density obtained from the standard proctor test. Master Thesis, University of Central Florida (2004)12. Huang, Y.: Pavement Analysis and Design, 2nd edn. Pearson Education, London (2004)13. Mesri, G.; Vardhanabhuti, B.: Compression of granular materials. Can. Geotech. J. 46(4), 369–392 (2009)14. Mishra, D.; Tutumluer, E.; Butt, A.: Quantifying effects of particle shape and type and amount of fines on unbound aggregate performance through controlled gradation. Transp. Res. Record J. Transp. Res. Board. 2167, 61–71 (2010)15. Jiang, Y.; Yuen, L.; Ren, J.: A numerical test method of California bearing ratio on graded crushed rocks using particle flow modeling. J. Traffic Transp. Eng. 2(2), 107–115 (2015)16. Kwon, J.; Kim, S.; Tutumluer, E.; Wayne, M.: Characterization of unbound aggregate materials considering physical and morphological properties. Int. J. Pavement Eng. 18(4), 303–308 (2017)17. Mendoza, C.; Caicedo, B.: Elastoplastic framework of relationships between CBR and Young’s modulus for granular material. Road Mater. Pavement Des. 19(8), 1796–1815 (2018)18. Sreelekshmypillai, G.; Vinod, P.: Prediction of CBR value of fine grained soils at any rational compactive effort. Int. J. Geotech. Eng. 13(6), 558–563 (2019)19. Sivrikaya, O.; Togrol, E.; Kayadelen, C.: Estimating compaction behavior of fine-grained soils based on compaction energy. Can. Geotech. J. 45(6), 877–887 (2008)20. Matteo, L.; Bigotti, F.; Ricco, R.: Best-fit model to estimate proctor properties of compacted soils. J. Geotech. Geoenviron. Eng. 135(7), 992–996 (2009)21. Mutjaba, H.; Farooq, K.; Sivakugan, N.; Das, B.: Correlation between gradational parameters and compaction characteristics of sandy soils. Int. J. Geotech. Eng. 7(4), 395–401 (2013)22. Araujo, W.; Ruiz, G.: Correlation equations of CBR with index properties of soil in the city of Piura. In: 14th LACCEI International Multi Conference for Engineering, Education and Technology: “Engineering Innovations for Global Sustainability‬”, San Jose, Costa Rica, 20–22 July, pp. 1–7 (2016)23. Janjua, Z.; Chand, J.: Correlation of CBR with index properties of soil. Int. J. Civ. Eng. Technol. 7(5), 57–62 (2016)24. Aderinola, O.; Emmanuel, O.; Ajibola, Q.: Correlation of California bearing ratio value of clays with soil index and compaction characteristics. Int. J. Sci. Res. Innov. Technol. 4(4), 12–22 (2017)25. Bekele, A.: Correlation of CBR with index properties of soils in Sulsulta town. Master Thesis, Addis Ababa Institute of Technology (2017)26. Ardakani, A.; Kordnaeji, A.: Soil compaction parameters prediction using GMDH-type neural network and genetic algorithm. Eur. J. Environ. Civ. Eng. 23(4), 449–462 (2019)27. Black, W.: A method of estimating the California bearing ratio of cohesive soils from plasticity data. Géotechnique 12(4), 271–282 (1962)28. Korfiatis, G.; Manikopoulos, C.: Correlation of maximum dry density and grain size. J. Geotech. Eng. 108(9), 1171–1176 (1982)29. Brown, S.: Soil mechanics in pavement engineering. Géotechnique 46(3), 383–426 (1996)30. Breytenbach, J.; Green, P.; Rooy, J.: The relationship between index testing and California bearing ratio values for natural road construction materials in South Africa. J. S. Afr. Inst. Civ. Eng. 52(2), 65–69 (2010)31. Ferede, Z.: Prediction of California bearing ratio (CBR) value from index properties of soil. Master Thesis, Addis Ababa Institute of Technology (2012)30. Breytenbach, J.; Green, P.; Rooy, J.: The relationship between index testing and California bearing ratio values for natural road construction materials in South Africa. J. S. Afr. Inst. Civ. Eng. 52(2), 65–69 (2010)31. Ferede, Z.: Prediction of California bearing ratio (CBR) value from index properties of soil. Master Thesis, Addis Ababa Institute of Technology (2012)32. Talukdar, D.: A study of correlation between California Bearing Ratio (CBR) value with other properties of soil. Int. J. Emerg. Technol. Adv. Eng. 4(1), 559–562 (2014)33. Look, B.: Spatial and statistical distribution models using the CBR test. Aust. Geomech. 44(1), 37–41 (2009)34. McGough, P.: A method for the prediction of soaked CBR of remolded samples from standard classification tests. Aust. Geomech. 45(4), 75–86 (2010)35. Patra, C.; Sivakugan, N.; Das, B.; Rout, S.: Correlations for relative density of clean sand with median grain size and compaction energy. Int. J. Geotech. Eng. 4(2), 195–203 (2010)36. Saklecha, P.; Katpatal, Y.; Rathore, S.; Agarawal, D.: Spatial correlation of mechanical properties of subgrade soil for foundation characterization. Int. J. Comput. Appl. 36(11), 20–25 (2011)37. Singh, D.; Reddy, K.; Yadu, L.: Moisture and compaction based statistical model for estimating CBR of fine-grained subgrade soils. Int. J. Earth Sci. Eng. 4(6), 100–103 (2011)38. Leliso, Y.: Correlation of CBR value with soil index properties for Addis Abba subgrade soils. Master Thesis, Addis Ababa University (2013)39. Gratchev, I.; Pitawala, S.; Gurung, N.; Monteiro, E.: A chart to estimate CBR of plastic soils. Aust. Geomech. J. 53(1), 1–5 (2018)40. Verma, G.; Kumar, B.: Prediction of compaction parameters for fine-grained and coarse-grained soils: a review. Int. J. Geotech. Eng. (2019).41. Moreno, N.: Zonificación geotécnica de los suelos en Barranquilla. 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