Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods
المؤلفون المشاركون
Cui, Fu-Qing
Zhang, Wei
Liu, Zhi-Yun
Wang, Wei
Chen, Jian-bing
Jin, Long
Peng, Hui
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-28
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions.
Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable.
In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed.
Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method.
Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized.
Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed.
To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed.
The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%).
The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%.
For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils.
This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Cui, Fu-Qing& Zhang, Wei& Liu, Zhi-Yun& Wang, Wei& Chen, Jian-bing& Jin, Long…[et al.]. 2020. Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods. Advances in Civil Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1125647
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Cui, Fu-Qing…[et al.]. Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods. Advances in Civil Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1125647
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Cui, Fu-Qing& Zhang, Wei& Liu, Zhi-Yun& Wang, Wei& Chen, Jian-bing& Jin, Long…[et al.]. Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods. Advances in Civil Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1125647
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1125647
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر