Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China
The collapsibility of aeolian sand has hindered the development of oil and gas resources and the construction of oil and gas stations in the Mu Us Desert. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on a water immersion load test, standar...
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MDPI AG
2024-12-01
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| author | Zhixu Li Yahong Deng Huandong Mu Yanxun Song |
| author_facet | Zhixu Li Yahong Deng Huandong Mu Yanxun Song |
| author_sort | Zhixu Li |
| collection | DOAJ |
| description | The collapsibility of aeolian sand has hindered the development of oil and gas resources and the construction of oil and gas stations in the Mu Us Desert. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on a water immersion load test, standard penetration test, and indoor geotechnical test, four evaluation indicators were selected, the water content, dry density, void ratio, and saturation. Combined with the support vector machine method, we established a method for evaluating the collapsibility of aeolian sand based on basic physical indicators. The results showed the following: (1) The degree of collapsibility was slight, with a small portion showing no collapsibility. And the load-settlement curve (P-s) was divided into three stages: the linear elastic deformation stage, the elastic–plastic deformation stage, and the collapsible deformation stage. (2) There was a strong relationship between the collapsibility coefficient and the four evaluation indicators for aeolian sand. Based on these indicators, we could accurately predict and evaluate the collapsibility coefficient. (3) Machine learning methods, such as the support vector machine, can effectively solve prediction and evaluation problems between variables when there is no clear mathematical relationship between multiple independent variables and a single dependent variable. |
| format | Article |
| id | doaj-art-6a46c9cdb1374e5486eeac6d42b18ad1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-6a46c9cdb1374e5486eeac6d42b18ad12024-12-13T16:23:15ZengMDPI AGApplied Sciences2076-34172024-12-0114231123810.3390/app142311238Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, ChinaZhixu Li0Yahong Deng1Huandong Mu2Yanxun Song3School of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaInstitute of Geotechnical Engineering, Xi’an University of Technology, Xi’an 710048, ChinaSchool of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaThe collapsibility of aeolian sand has hindered the development of oil and gas resources and the construction of oil and gas stations in the Mu Us Desert. This study considered aeolian sand on the southern edge of the Mu Us Desert as the research object. Based on a water immersion load test, standard penetration test, and indoor geotechnical test, four evaluation indicators were selected, the water content, dry density, void ratio, and saturation. Combined with the support vector machine method, we established a method for evaluating the collapsibility of aeolian sand based on basic physical indicators. The results showed the following: (1) The degree of collapsibility was slight, with a small portion showing no collapsibility. And the load-settlement curve (P-s) was divided into three stages: the linear elastic deformation stage, the elastic–plastic deformation stage, and the collapsible deformation stage. (2) There was a strong relationship between the collapsibility coefficient and the four evaluation indicators for aeolian sand. Based on these indicators, we could accurately predict and evaluate the collapsibility coefficient. (3) Machine learning methods, such as the support vector machine, can effectively solve prediction and evaluation problems between variables when there is no clear mathematical relationship between multiple independent variables and a single dependent variable.https://www.mdpi.com/2076-3417/14/23/11238Mu Us Desertaeolian sandcollapsibility evaluationbasic physical indicatorssupport vector machine |
| spellingShingle | Zhixu Li Yahong Deng Huandong Mu Yanxun Song Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China Applied Sciences Mu Us Desert aeolian sand collapsibility evaluation basic physical indicators support vector machine |
| title | Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China |
| title_full | Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China |
| title_fullStr | Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China |
| title_full_unstemmed | Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China |
| title_short | Evaluation of Aeolian Sand Collapsibility Based on Physical Indicators in the Mu Us Desert, China |
| title_sort | evaluation of aeolian sand collapsibility based on physical indicators in the mu us desert china |
| topic | Mu Us Desert aeolian sand collapsibility evaluation basic physical indicators support vector machine |
| url | https://www.mdpi.com/2076-3417/14/23/11238 |
| work_keys_str_mv | AT zhixuli evaluationofaeoliansandcollapsibilitybasedonphysicalindicatorsinthemuusdesertchina AT yahongdeng evaluationofaeoliansandcollapsibilitybasedonphysicalindicatorsinthemuusdesertchina AT huandongmu evaluationofaeoliansandcollapsibilitybasedonphysicalindicatorsinthemuusdesertchina AT yanxunsong evaluationofaeoliansandcollapsibilitybasedonphysicalindicatorsinthemuusdesertchina |