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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ftdoajarticles:oai:doaj.org/article:d6f466b025c64efeb618300ab9722e66 2023-09-05T13:20:06+02:00 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 2023-08-01T00:00:00Z https://doi.org/10.3390/rs15153913 https://doaj.org/article/d6f466b025c64efeb618300ab9722e66 EN eng MDPI AG https://www.mdpi.com/2072-4292/15/15/3913 https://doaj.org/toc/2072-4292 doi:10.3390/rs15153913 2072-4292 https://doaj.org/article/d6f466b025c64efeb618300ab9722e66 Remote Sensing, Vol 15, Iss 3913, p 3913 (2023) Qinghai–Tibet engineering corridor permafrost subgrade thaw settlement risk characteristics RBF neural network Science Q article 2023 ftdoajarticles https://doi.org/10.3390/rs15153913 2023-08-13T00:34:32Z 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 km 2 , 1594.21 km 2 , 2456.10 km 2 and 558.78 km 2 , 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. Article in Journal/Newspaper Ice permafrost Directory of Open Access Journals: DOAJ Articles Remote Sensing 15 15 3913 |
institution |
Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Qinghai–Tibet engineering corridor permafrost subgrade thaw settlement risk characteristics RBF neural network Science Q |
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Qinghai–Tibet engineering corridor permafrost subgrade thaw settlement risk characteristics RBF neural network Science Q 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 |
topic_facet |
Qinghai–Tibet engineering corridor permafrost subgrade thaw settlement risk characteristics RBF neural network Science Q |
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 km 2 , 1594.21 km 2 , 2456.10 km 2 and 558.78 km 2 , 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. |
format |
Article in Journal/Newspaper |
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 |
title |
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_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_sort |
risk zoning of permafrost thaw settlement in the qinghai–tibet engineering corridor |
publisher |
MDPI AG |
publishDate |
2023 |
url |
https://doi.org/10.3390/rs15153913 https://doaj.org/article/d6f466b025c64efeb618300ab9722e66 |
genre |
Ice permafrost |
genre_facet |
Ice permafrost |
op_source |
Remote Sensing, Vol 15, Iss 3913, p 3913 (2023) |
op_relation |
https://www.mdpi.com/2072-4292/15/15/3913 https://doaj.org/toc/2072-4292 doi:10.3390/rs15153913 2072-4292 https://doaj.org/article/d6f466b025c64efeb618300ab9722e66 |
op_doi |
https://doi.org/10.3390/rs15153913 |
container_title |
Remote Sensing |
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15 |
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15 |
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3913 |
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1776200834268266496 |