File Download
Supplementary

Conference Paper: Development of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory

TitleDevelopment of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory
Authors
KeywordsFuzzy-MAUT
Fuzzy set theory
As-is BIM object
Image-based object recognition
Issue Date2016
Citation
The 2016 CIB W78 Conference, Brisbane, Australia, 31 October-2 November 2016. How to Cite?
AbstractBuilding Information Modeling (BIM) could support different activities throughout the life cycle of a building and has been widely applied in design and construction phases nowadays. However, BIM has not been widely implemented in the operation and maintenance (O&M) phase. As-is information for the majority of existing buildings is not complete and even outdated or incorrect. Lack of accurate and complete as-is information is still one of the key reasons leading to the low-level efficiency in O&M. BIM performs as an intelligent platform and a database that stores, links, extracts and exchanges information in construction projects. It has shown promising opportunities and advantages in BIM applications for the improvement in O&M. Hence, an effective and convenient approach to record as-is conditions of the existing buildings and create as-is BIM objects would be the essential step for improving efficiency and effectiveness of O&M, and furthermore possibly refurbishment of the building. Many researchers have paid attention to different systems and approaches for automated and real-time object recognition in past decades. This paper summarizes state-of-the-art statistical matching-based object recognition methods and then presents our image-based building object recognition application, which extracts object information by simply conducting point-and-click operations. Furthermore, the object recognition research system is introduced, including recognizing structure object types and their corresponding materials. In this paper, we combine the Multi-Attribute Utility Theory (MAUT) with the fuzzy set theory to be Fuzzy-MAUT, since the MAUT allows complex and powerful combinations of various criteria and fuzzy set theory assists improving the performance of this system. With the goal of creating as-is BIM objects equipped with the semi-automatic object recognition system, our image-based object recognition system and its recognition process are validated and tested. Key challenges and promising opportunities are also addressed.
DescriptionPaper no. 046
Persistent Identifierhttp://hdl.handle.net/10722/241034

 

DC FieldValueLanguage
dc.contributor.authorLu, Q-
dc.contributor.authorLee, SH-
dc.date.accessioned2017-05-22T09:21:29Z-
dc.date.available2017-05-22T09:21:29Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 CIB W78 Conference, Brisbane, Australia, 31 October-2 November 2016.-
dc.identifier.urihttp://hdl.handle.net/10722/241034-
dc.descriptionPaper no. 046-
dc.description.abstractBuilding Information Modeling (BIM) could support different activities throughout the life cycle of a building and has been widely applied in design and construction phases nowadays. However, BIM has not been widely implemented in the operation and maintenance (O&M) phase. As-is information for the majority of existing buildings is not complete and even outdated or incorrect. Lack of accurate and complete as-is information is still one of the key reasons leading to the low-level efficiency in O&M. BIM performs as an intelligent platform and a database that stores, links, extracts and exchanges information in construction projects. It has shown promising opportunities and advantages in BIM applications for the improvement in O&M. Hence, an effective and convenient approach to record as-is conditions of the existing buildings and create as-is BIM objects would be the essential step for improving efficiency and effectiveness of O&M, and furthermore possibly refurbishment of the building. Many researchers have paid attention to different systems and approaches for automated and real-time object recognition in past decades. This paper summarizes state-of-the-art statistical matching-based object recognition methods and then presents our image-based building object recognition application, which extracts object information by simply conducting point-and-click operations. Furthermore, the object recognition research system is introduced, including recognizing structure object types and their corresponding materials. In this paper, we combine the Multi-Attribute Utility Theory (MAUT) with the fuzzy set theory to be Fuzzy-MAUT, since the MAUT allows complex and powerful combinations of various criteria and fuzzy set theory assists improving the performance of this system. With the goal of creating as-is BIM objects equipped with the semi-automatic object recognition system, our image-based object recognition system and its recognition process are validated and tested. Key challenges and promising opportunities are also addressed.-
dc.languageeng-
dc.relation.ispartofCIB W78 2016 Conference-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectFuzzy-MAUT-
dc.subjectFuzzy set theory-
dc.subjectAs-is BIM object-
dc.subjectImage-based object recognition-
dc.titleDevelopment of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory-
dc.typeConference_Paper-
dc.identifier.emailLee, SH: shlee1@hku.hk-
dc.identifier.authorityLee, SH=rp01910-
dc.description.naturepostprint-
dc.identifier.hkuros272405-
dc.customcontrol.immutablesml 170525-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats