Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection
The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to predict the experimental results as supporting informati...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Machine Learning and Knowledge Extraction |
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| Online Access: | https://www.mdpi.com/2504-4990/6/1/20 |
| _version_ | 1797240196251516928 |
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| author | Yuxin Cong Toshiyuki Motohashi Koki Nakao Shinya Inazumi |
| author_facet | Yuxin Cong Toshiyuki Motohashi Koki Nakao Shinya Inazumi |
| author_sort | Yuxin Cong |
| collection | DOAJ |
| description | The objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to predict the experimental results as supporting information before performing the physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all the variables, as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data. |
| first_indexed | 2024-04-24T18:03:35Z |
| format | Article |
| id | doaj.art-07b626a31433497a83aba7694437234b |
| institution | Directory Open Access Journal |
| issn | 2504-4990 |
| language | English |
| last_indexed | 2024-04-24T18:03:35Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj.art-07b626a31433497a83aba7694437234b2024-03-27T13:52:05ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902024-02-016140241910.3390/make6010020Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical InjectionYuxin Cong0Toshiyuki Motohashi1Koki Nakao2Shinya Inazumi3Graduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, JapanOsaka Bousui Construction Co., Ltd., Osaka 543-0016, JapanGraduate School of Engineering and Science, Shibaura Institute of Technology, Tokyo 135-8548, JapanCollege of Engineering, Shibaura Institute of Technology, Tokyo 135-8548, JapanThe objective of this study was to investigate the liquefaction resistance of chemically improved sandy soils in a straightforward and accurate manner. Using only the existing experimental databases and artificial intelligence, the goal was to predict the experimental results as supporting information before performing the physical experiments. Emphasis was placed on the significance of data from 20 loading cycles of cyclic undrained triaxial tests to determine the liquefaction resistance and the contribution of each explanatory variable. Different combinations of explanatory variables were considered. Regarding the predictive model, it was observed that a case with the liquefaction resistance ratio as the dependent variable and other parameters as explanatory variables yielded favorable results. In terms of exploring combinations of explanatory variables, it was found advantageous to include all the variables, as doing so consistently resulted in a high coefficient of determination. The inclusion of the liquefaction resistance ratio in the training data was found to improve the predictive accuracy. In addition, the results obtained when using a linear model for the prediction suggested the potential to accurately predict the liquefaction resistance using historical data.https://www.mdpi.com/2504-4990/6/1/20artificial intelligencechemical injectioncyclic undrained triaxial testliquefactionmachine learningsandy soil |
| spellingShingle | Yuxin Cong Toshiyuki Motohashi Koki Nakao Shinya Inazumi Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection Machine Learning and Knowledge Extraction artificial intelligence chemical injection cyclic undrained triaxial test liquefaction machine learning sandy soil |
| title | Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection |
| title_full | Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection |
| title_fullStr | Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection |
| title_full_unstemmed | Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection |
| title_short | Machine Learning Predictive Analysis of Liquefaction Resistance for Sandy Soils Enhanced by Chemical Injection |
| title_sort | machine learning predictive analysis of liquefaction resistance for sandy soils enhanced by chemical injection |
| topic | artificial intelligence chemical injection cyclic undrained triaxial test liquefaction machine learning sandy soil |
| url | https://www.mdpi.com/2504-4990/6/1/20 |
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