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2022 | OriginalPaper | Chapter

Integrating Artificial Intelligence Approaches for Quantitative and Qualitative Analysis in H-BIM

Authors : David Bienvenido-Huertas, Blanca Tejedor, Manuel J. Carretero-Ayuso, Carlos E. Rodríguez Jiménez, Marta Torres-González

Published in: New Technologies in Building and Construction

Publisher: Springer Nature Singapore

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Abstract

Managing historic buildings is a process in which workers responsible for this task require many time resources. Its optimization through several techniques, such as artificial intelligence, reduces the time related to decision-making. This chapter develops a procedure to generate intelligent GDL objects to predict or estimate the responses required to manage heritage elements in historic buildings. For this purpose, the models developed through data mining procedures in GDL objects in Building Information Modelling (BIM) platforms are combined with their application to historic buildings: Heritage Building Information Modelling (H-BIM). Thus, intelligent BIM models are developed to meet the needs of the technicians responsible for maintaining historic buildings. The responses given by the intelligent objects could be qualitative or quantitative. This methodology would be useful to reduce both the time of decision-making and the data analysis by visualizing them in a three-dimensional model of the historic building. Thus, this is a technique designed to optimize the management of the heritage elements in historic buildings.

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previous chapter The Semantic Discretization of Architectural Heritage as the Basis of a HBIM Restoration and Conservation Project

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Title: Integrating Artificial Intelligence Approaches for Quantitative and Qualitative Analysis in H-BIM
Authors: David Bienvenido-Huertas
Blanca Tejedor
Manuel J. Carretero-Ayuso
Carlos E. Rodríguez Jiménez
Marta Torres-González
Publisher: Springer Nature Singapore
Book: New Technologies in Building and Construction
Print ISBN: 978-981-19-1893-3

Electronic ISBN: 978-981-19-1894-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-981-19-1894-0_14

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