Correlating CBR values with basic soil parameters (by using Neuroxl Predictor)
| dc.contributor.advisor | Henok Fikre (PhD) | |
| dc.contributor.author | Airmeyas Aychew | |
| dc.date.accessioned | 2024-05-27T08:35:31Z | |
| dc.date.available | 2024-05-27T08:35:31Z | |
| dc.date.issued | 2024-01 | |
| dc.description.abstract | The California bearing ratio (CBR) is an essential design parameter for soils and an indirect measure of soil strength. This is broadly used for the design of sub-grade, sub-base and base course materials for road, railway, and airfield projects. It is also used as a direct relationship to determine the response of the base or sub-base soil. This research studies the relationship between CBR values and other parameters of soil properties, although the samples include coarse and fine-grained soils, using advanced neural network programs to help us obtain an accurate predicted value. Most of the previous studies were conducted on fine-grained soil types and also used conventional multiple linear regression analysis methods. To satisfy the objective of this study, one hundred and ninety-eight soil sample test results were collected. I participated as a material engineer in the testing and reporting process. Laboratory testing was performed in accordance with AASHTO standard test methods. Modified compaction, soak three-point modified CBR, wet sieve analysis, and Atterberg limit tests were performed on a total of one hundred and ninety-eight soil samples. Statistical analyses were performed to validate the new model using 30 percent of the total sample size. Two types of analysis programs—Microsoft Excel software (ANOVA) for multiple non-linear regression relationships and the advanced NeuroXL predictive neural network program—were used to predict CBR values. Independent soil property parameters were liquid limit, plastic limit, plastic index, amount of particle size less than 0.075 mm, amount of particle size less than 0.425 mm, amount of particle size less than 2.00 mm, amount of particle size less than 4.75 mm, optimum moisture content, and maximum dry density. This study provided two alternative models. The first alternative model included compaction test parameters (OMC and MDD), particle size distribution parameters (4.75 mm PP, 2 mm PP, 0.425 mm PP, and 0.075 mm PP), and plasticity parameters (LL, PL, and PI). They were taken as independent parameters. The second alternative model excludes the compaction test parameters (OMC and MDD) as independent parameters when compared to the alternative one. This study used two alternative analysis techniques: the first group of analysis techniques developed model equations for each classified data set (sub-grade, sub-base, and sub-base), and the second technique developed model equations for the unclassified data set group. The predicted CBR values of both the NeuroXL prediction and multiple nonlinear ANOVA regression models were compared with the actual CBR values, which confirmed that there was an acceptable difference between the actual and predicted CBR values between both analysis methods. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3034 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | CBR | |
| dc.subject | neuroxl predictor | |
| dc.subject | correlation | |
| dc.subject | regression | |
| dc.subject | ANOVA | |
| dc.subject | AASHTO. | |
| dc.title | Correlating CBR values with basic soil parameters (by using Neuroxl Predictor) | |
| dc.type | Thesis |