Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor

The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement def...

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Published in:Remote Sensing
Main Authors: Zhiyun Liu, Yu Zhu, Jianbing Chen, Fuqing Cui, Wu Zhu, Jine Liu, Hui Yu
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2023
Subjects:
Online Access:https://doi.org/10.3390/rs15153913
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author Zhiyun Liu
Yu Zhu
Jianbing Chen
Fuqing Cui
Wu Zhu
Jine Liu
Hui Yu
author_facet Zhiyun Liu
Yu Zhu
Jianbing Chen
Fuqing Cui
Wu Zhu
Jine Liu
Hui Yu
author_sort Zhiyun Liu
collection MDPI Open Access Publishing
container_issue 15
container_start_page 3913
container_title Remote Sensing
container_volume 15
description The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased.
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genre Ice
permafrost
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permafrost
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op_doi https://doi.org/10.3390/rs15153913
op_relation Ecological Remote Sensing
https://dx.doi.org/10.3390/rs15153913
op_rights https://creativecommons.org/licenses/by/4.0/
op_source Remote Sensing; Volume 15; Issue 15; Pages: 3913
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spelling ftmdpi:oai:mdpi.com:/2072-4292/15/15/3913/ 2025-01-16T22:21:23+00:00 Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor Zhiyun Liu Yu Zhu Jianbing Chen Fuqing Cui Wu Zhu Jine Liu Hui Yu agris 2023-08-07 application/pdf https://doi.org/10.3390/rs15153913 EN eng Multidisciplinary Digital Publishing Institute Ecological Remote Sensing https://dx.doi.org/10.3390/rs15153913 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 15; Issue 15; Pages: 3913 Qinghai–Tibet engineering corridor permafrost subgrade thaw settlement risk characteristics RBF neural network Text 2023 ftmdpi https://doi.org/10.3390/rs15153913 2023-08-13T23:51:55Z The Qinghai–Tibet Plateau is the highest and largest permafrost area in the middle and low latitudes of China. In this region, permafrost thaw settlement is the main form of expressway subgrade disaster. Therefore, the quantitative analysis and regionalization study of permafrost thaw settlement deformation are of great significance for expressway construction and maintenance in the Qinghai–Tibet region. This paper establishes a thaw settlement prediction model using the thaw settlement coefficient and thaw depth. The thaw depth was predicted by the mean annual ground temperatures and active-layer thicknesses using the Radial Basis Function (RBF) neural network model, and the thaw settlement coefficient was determined according to the type of ice content. Further, the distribution characteristics of thaw settlement risk of the permafrost subgrade in the study region were mapped and analyzed. The results showed that the thaw settlement risk was able to be divided into four risk levels, namely significant risk, high risk, medium risk and low risk levels, with the areas of these four risk levels covering 3868.67 km2, 1594.21 km2, 2456.10 km2 and 558.78 km2, respectively, of the total study region. The significant risk level had the highest proportion among all the risk levels and was mainly distributed across the Chumar River Basin, Beiluhe River Basin and Gaerqu River Basin regions. Moreover, ice content was found to be the main factor affecting thaw settlement, with thaw settlement found to increase as the ice content increased. Text Ice permafrost MDPI Open Access Publishing Remote Sensing 15 15 3913
spellingShingle Qinghai–Tibet engineering corridor
permafrost subgrade
thaw settlement
risk characteristics
RBF neural network
Zhiyun Liu
Yu Zhu
Jianbing Chen
Fuqing Cui
Wu Zhu
Jine Liu
Hui Yu
Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title_full Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title_fullStr Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title_full_unstemmed Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title_short Risk Zoning of Permafrost Thaw Settlement in the Qinghai–Tibet Engineering Corridor
title_sort risk zoning of permafrost thaw settlement in the qinghai–tibet engineering corridor
topic Qinghai–Tibet engineering corridor
permafrost subgrade
thaw settlement
risk characteristics
RBF neural network
topic_facet Qinghai–Tibet engineering corridor
permafrost subgrade
thaw settlement
risk characteristics
RBF neural network
url https://doi.org/10.3390/rs15153913