Long Jin

Dr. Long Jin


Research Focus & Metrics

Academic Career Length

12Years (2015-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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14 February 2021 in Bulletin of Engineering Geology and the Environment
Verified
Journal Article
Closed access
PDF file
LTLiyun Tang
Liyun Tang
LGLi Gang
Li Gang
ZLZhen Li
Zhen Li
LJLong Jin
Long Jin
GYGengshe Yang
Gengshe Yang


12 April 2018 in Journal of Mountain Science
Verified
Journal Article
Closed access
PDF file
Jian XuJian Xu
Jian Xu
ZWZhang-Quan Wang
Zhang-Quan Wang
JRJian-Wei Ren
Jian-Wei Ren
Songhe WangSonghe Wang
Songhe Wang
LJLong Jin
Long Jin

1 October 2020 in Land Degradation & Development
Verified
Journal Article
Closed access
PDF file
Yi SongYi Song
Yi Song
Jagannath AryalJagannath Aryal
Jagannath Aryal
LTLiangcheng Tan
Liangcheng Tan
LJLong Jin
Long Jin
ZGZhihua Gao
Zhihua Gao
YWYunqiang Wang
Yunqiang Wang

10 December 2020 in Construction and Building Materials
Verified
Journal Article
Closed access
DWDi Wu
Di Wu
LJLong Jin
Long Jin
YLYanqiu Leng
Yanqiu Leng
MHMengling Hu
Mengling Hu
RBRan Bi
Ran Bi
GYGengshe Yang
Gengshe Yang

1 January 2020 in Advances in Civil Engineering
Verified
Journal Article
Open access
PDF file
FCFuqing Cui
Fuqing Cui
WZWei Zhang
Wei Zhang
ZLZhiyun Liu
Zhiyun Liu
WWWei Wang
Wei Wang
JCJian-Bing Chen
Jian-Bing Chen
HPhui peng
hui peng
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.