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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
Title | Development of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory |
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Authors | |
Keywords | Fuzzy-MAUT Fuzzy set theory As-is BIM object Image-based object recognition |
Issue Date | 2016 |
Citation | Proceedings of the 33rd CIB W78 Conference, Brisbane, Australia, 31 October-2 November 2016. How to Cite? |
Abstract | Building 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 As-is information for the majority of existing buildings is not complete and even outdated or incorrect.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. |
Description | Paper no. 046 |
Persistent Identifier | http://hdl.handle.net/10722/241034 |
DC Field | Value | Language |
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dc.contributor.author | Lu, Q | - |
dc.contributor.author | Lee, SH | - |
dc.date.accessioned | 2017-05-22T09:21:29Z | - |
dc.date.available | 2017-05-22T09:21:29Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | Proceedings of the 33rd CIB W78 Conference, Brisbane, Australia, 31 October-2 November 2016. | - |
dc.identifier.uri | http://hdl.handle.net/10722/241034 | - |
dc.description | Paper no. 046 | - |
dc.description.abstract | Building 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.language | eng | - |
dc.relation.ispartof | CIB W78 2016 Conference | - |
dc.subject | Fuzzy-MAUT | - |
dc.subject | Fuzzy set theory | - |
dc.subject | As-is BIM object | - |
dc.subject | Image-based object recognition | - |
dc.title | Development of a Semi-Automatic Image-based Object Recognition System for Reconstructing As-is BIM Objects based on Fuzzy Multi-Attribute Utility Theory | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Lee, SH: shlee1@hku.hk | - |
dc.identifier.authority | Lee, SH=rp01910 | - |
dc.description.nature | postprint | - |
dc.identifier.hkuros | 272405 | - |
dc.customcontrol.immutable | sml 170525 | - |