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Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP
Land subsidence caused serious damages of mage-city infrastructures. This data in brief presents a new questionnaire to establish judgment matrix during the risk assessment of land subsidence. The data source of the assessment factors is provided. The analytical hierarchy process (AHP) and interval...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6939062/ https://www.ncbi.nlm.nih.gov/pubmed/31909110 http://dx.doi.org/10.1016/j.dib.2019.105007 |
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author | Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yang, Jun |
author_facet | Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yang, Jun |
author_sort | Lyu, Hai-Min |
collection | PubMed |
description | Land subsidence caused serious damages of mage-city infrastructures. This data in brief presents a new questionnaire to establish judgment matrix during the risk assessment of land subsidence. The data source of the assessment factors is provided. The analytical hierarchy process (AHP) and interval fuzzy AHP (FAHP) are used to calibrate the weights of assessment factors. The new questionnaire is used to collect the viewpoints from experts. Based on the viewpoints of experts, the judgment matrix can be established using pairwise comparison. The data presented herein was used for the article, titled “Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP” Lyu et al. (2019) [1]. |
format | Online Article Text |
id | pubmed-6939062 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-69390622020-01-06 Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yang, Jun Data Brief Engineering Land subsidence caused serious damages of mage-city infrastructures. This data in brief presents a new questionnaire to establish judgment matrix during the risk assessment of land subsidence. The data source of the assessment factors is provided. The analytical hierarchy process (AHP) and interval fuzzy AHP (FAHP) are used to calibrate the weights of assessment factors. The new questionnaire is used to collect the viewpoints from experts. Based on the viewpoints of experts, the judgment matrix can be established using pairwise comparison. The data presented herein was used for the article, titled “Risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP” Lyu et al. (2019) [1]. Elsevier 2019-12-17 /pmc/articles/PMC6939062/ /pubmed/31909110 http://dx.doi.org/10.1016/j.dib.2019.105007 Text en © 2019 The Author(s) http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Engineering Lyu, Hai-Min Shen, Shui-Long Zhou, Annan Yang, Jun Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title | Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title_full | Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title_fullStr | Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title_full_unstemmed | Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title_short | Data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal FAHP |
title_sort | data in risk assessment of mega-city infrastructures related to land subsidence using improved trapezoidal fahp |
topic | Engineering |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6939062/ https://www.ncbi.nlm.nih.gov/pubmed/31909110 http://dx.doi.org/10.1016/j.dib.2019.105007 |
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