Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis
Soils compaction with experimental tests is a pivotal facet in the selection of materials for earth constructions. Due to the time limitations and concerns of finishing resources, it is obligate to develop some relationships for predicting compaction parameters such as maximum dry unit weight (γdmax...
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2023-03-01
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| Series: | Advances in Engineering and Intelligence Systems |
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| author | Yufeng Qian |
| author_facet | Yufeng Qian |
| author_sort | Yufeng Qian |
| collection | DOAJ |
| description | Soils compaction with experimental tests is a pivotal facet in the selection of materials for earth constructions. Due to the time limitations and concerns of finishing resources, it is obligate to develop some relationships for predicting compaction parameters such as maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily estimated index properties. The purpose is to evaluate the applicability of multivariate adaptive regression splines (MARS) for estimating γdmax and ωopt of lateritic soils. Furthermore, different degrees of interactions of models are employed to have comprehensive, precise, and trustable outputs. The outputs of suggested equations to estimate γdmax related to modified proctor compaction test provide proper capability in the modeling procedure. In the training dataset, the value of all criteria for MARS-OI-3 is proper, with the value of 0.9365, 0.4146, and 93.647 for R2, RMSE, and VAF, respectively. But testing phase’s results are roughly complicated, where scores of MARS-OI-3 equal to 21, bigger than MARS-OI-2 (10) and MARS-OI-4 (17). In summary, MARS-OI-3 outperforms others, where can be known as the suggested equation. The outputs of suggested equations to estimate ω_opt also provide great ability in the modeling. In both phases, the value of all criteria for MARS-OI-2 is proper than MARS-OI-1. Also, scores depict that the score of MARS-OI-2 (15) is about double of MARS-OI-2 (9). So, in spite MARS-OI-1 has justifiable usefulness in the forecasting outline, MARS-OI-2 outperforms it. |
| format | Article |
| id | doaj-art-e0860dfef55d4964a8f5c1a6544f93ed |
| institution | Kabale University |
| issn | 2821-0263 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Bilijipub publisher |
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| series | Advances in Engineering and Intelligence Systems |
| spelling | doaj-art-e0860dfef55d4964a8f5c1a6544f93ed2025-02-12T08:47:02ZengBilijipub publisherAdvances in Engineering and Intelligence Systems2821-02632023-03-0100201132310.22034/aeis.2023.374474.1059169077Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression AnalysisYufeng Qian0School of Science, Hubei University of Technology, Wuhan, 430068, China.Soils compaction with experimental tests is a pivotal facet in the selection of materials for earth constructions. Due to the time limitations and concerns of finishing resources, it is obligate to develop some relationships for predicting compaction parameters such as maximum dry unit weight (γdmax) and optimum moisture content (ωopt) from easily estimated index properties. The purpose is to evaluate the applicability of multivariate adaptive regression splines (MARS) for estimating γdmax and ωopt of lateritic soils. Furthermore, different degrees of interactions of models are employed to have comprehensive, precise, and trustable outputs. The outputs of suggested equations to estimate γdmax related to modified proctor compaction test provide proper capability in the modeling procedure. In the training dataset, the value of all criteria for MARS-OI-3 is proper, with the value of 0.9365, 0.4146, and 93.647 for R2, RMSE, and VAF, respectively. But testing phase’s results are roughly complicated, where scores of MARS-OI-3 equal to 21, bigger than MARS-OI-2 (10) and MARS-OI-4 (17). In summary, MARS-OI-3 outperforms others, where can be known as the suggested equation. The outputs of suggested equations to estimate ω_opt also provide great ability in the modeling. In both phases, the value of all criteria for MARS-OI-2 is proper than MARS-OI-1. Also, scores depict that the score of MARS-OI-2 (15) is about double of MARS-OI-2 (9). So, in spite MARS-OI-1 has justifiable usefulness in the forecasting outline, MARS-OI-2 outperforms it.https://aeis.bilijipub.com/article_169077_c01c30cc980dd3049dcad51869e63180.pdflateritic soilsproctor compaction testmodified testcompaction propertiesmultivariate adaptive regression splines |
| spellingShingle | Yufeng Qian Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis Advances in Engineering and Intelligence Systems lateritic soils proctor compaction test modified test compaction properties multivariate adaptive regression splines |
| title | Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis |
| title_full | Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis |
| title_fullStr | Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis |
| title_full_unstemmed | Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis |
| title_short | Maximum Dry Unit Weight and Optimum Moisture Content Prediction of Lateritic Soils Using Regression Analysis |
| title_sort | maximum dry unit weight and optimum moisture content prediction of lateritic soils using regression analysis |
| topic | lateritic soils proctor compaction test modified test compaction properties multivariate adaptive regression splines |
| url | https://aeis.bilijipub.com/article_169077_c01c30cc980dd3049dcad51869e63180.pdf |
| work_keys_str_mv | AT yufengqian maximumdryunitweightandoptimummoisturecontentpredictionoflateriticsoilsusingregressionanalysis |