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Risk Assessment of Green Intelligent Building Based on Artificial Intelligence

Green smart building is the development direction of future architecture. It is of great significance to carry out risk assessment. Fire risk is the key content of building risk, so this paper takes fire risk as the research object, with the help of artificial intelligence technology, to carry out t...

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Detalles Bibliográficos
Autor principal: Xu, Binjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499760/
https://www.ncbi.nlm.nih.gov/pubmed/36156975
http://dx.doi.org/10.1155/2022/7584853
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author Xu, Binjie
author_facet Xu, Binjie
author_sort Xu, Binjie
collection PubMed
description Green smart building is the development direction of future architecture. It is of great significance to carry out risk assessment. Fire risk is the key content of building risk, so this paper takes fire risk as the research object, with the help of artificial intelligence technology, to carry out the risk assessment research of green smart buildings. With the rapid development of the economy, urban fire risk factors are increasing, and the fire situation is becoming more and more serious. Building fire risk assessment is an important measure to effectively prevent and control urban building fires. This paper uses Internet of Things data to carry out fire risk assessment and realize Internet of Things data mining. Collect a large number of expert samples to build training samples, train the green intelligent building monomer fire risk assessment and prediction model based on deep neural network, constantly adjust the model parameters to optimize the model, and finally verify and modify the model.
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spelling pubmed-94997602022-09-23 Risk Assessment of Green Intelligent Building Based on Artificial Intelligence Xu, Binjie Comput Intell Neurosci Research Article Green smart building is the development direction of future architecture. It is of great significance to carry out risk assessment. Fire risk is the key content of building risk, so this paper takes fire risk as the research object, with the help of artificial intelligence technology, to carry out the risk assessment research of green smart buildings. With the rapid development of the economy, urban fire risk factors are increasing, and the fire situation is becoming more and more serious. Building fire risk assessment is an important measure to effectively prevent and control urban building fires. This paper uses Internet of Things data to carry out fire risk assessment and realize Internet of Things data mining. Collect a large number of expert samples to build training samples, train the green intelligent building monomer fire risk assessment and prediction model based on deep neural network, constantly adjust the model parameters to optimize the model, and finally verify and modify the model. Hindawi 2022-09-15 /pmc/articles/PMC9499760/ /pubmed/36156975 http://dx.doi.org/10.1155/2022/7584853 Text en Copyright © 2022 Binjie Xu. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xu, Binjie
Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title_full Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title_fullStr Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title_full_unstemmed Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title_short Risk Assessment of Green Intelligent Building Based on Artificial Intelligence
title_sort risk assessment of green intelligent building based on artificial intelligence
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9499760/
https://www.ncbi.nlm.nih.gov/pubmed/36156975
http://dx.doi.org/10.1155/2022/7584853
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