Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil
The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the phy...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Geotechnics |
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| Online Access: | https://www.mdpi.com/2673-7094/3/2/26 |
| _version_ | 1797594625499725824 |
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| author | Adel Abdallah Giacomo Russo Olivier Cuisinier |
| author_facet | Adel Abdallah Giacomo Russo Olivier Cuisinier |
| author_sort | Adel Abdallah |
| collection | DOAJ |
| description | The mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m<sup>3</sup>) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R<sup>2</sup> of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions. |
| first_indexed | 2024-03-11T02:25:57Z |
| format | Article |
| id | doaj.art-f6e4e29760a34d20aa5cd5a731ab311c |
| institution | Directory Open Access Journal |
| issn | 2673-7094 |
| language | English |
| last_indexed | 2024-03-11T02:25:57Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Geotechnics |
| spelling | doaj.art-f6e4e29760a34d20aa5cd5a731ab311c2023-11-18T10:37:02ZengMDPI AGGeotechnics2673-70942023-06-013246547910.3390/geotechnics3020026Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey SoilAdel Abdallah0Giacomo Russo1Olivier Cuisinier2LEMTA, Université de Lorraine, CNRS, F-54000 Nancy, FranceDepartment of Earth Science, Environment and Resources, University of Napoli Federico II, 80126 Napoli, ItalyLEMTA, Université de Lorraine, CNRS, F-54000 Nancy, FranceThe mechanical efficiency of soil stabilization with cement is mainly controlled by various parameters, namely, the amount of binder, the compaction soil state and the curing conditions. The strength of the treated soil is the result of a complex combination of several factors that condition the physicochemical processes involved in cement hydration, which are difficult to monitor. The objective of this study is to identify the relevant parameters governing the bonding in cement-treated soil and suggest a predictive model for strength evolution using these parameters as input. To this purpose, an extensive testing program is presented to assess the impact of the initial water content (11–18%) and dry density (1.6–1.87 Mg/m<sup>3</sup>) as well as cement dosage (3 and 6%) in sealed curing conditions for 0, 7, 28 and 90 days. The water content variation, the total suction and the compressive strength were measured after different curing durations. The experimental results are first discussed in the parameters’ space, and then through a principal components analysis to overcome the complexity due to the parameters’ interdependency. The PCA revealed that the cement dosage explained 40% of the dataset variance, the remaining 60% being related to a combination of the initial state and curing time. Finally, a predictive model based on an artificial neural network was developed and tested. The predicted results were excellent, with an R<sup>2</sup> of +0.99 with the training data and +0.93 with the testing data. These results should be improved by extending the dataset to include different soils and additional compaction conditions.https://www.mdpi.com/2673-7094/3/2/26soil cement stabilizationstatistical analysisartificial neural networksunconfined compressive strength |
| spellingShingle | Adel Abdallah Giacomo Russo Olivier Cuisinier Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil Geotechnics soil cement stabilization statistical analysis artificial neural networks unconfined compressive strength |
| title | Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil |
| title_full | Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil |
| title_fullStr | Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil |
| title_full_unstemmed | Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil |
| title_short | Statistical and Predictive Analyses of the Strength Development of a Cement-Treated Clayey Soil |
| title_sort | statistical and predictive analyses of the strength development of a cement treated clayey soil |
| topic | soil cement stabilization statistical analysis artificial neural networks unconfined compressive strength |
| url | https://www.mdpi.com/2673-7094/3/2/26 |
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