A knowledge model-based BIM framework for automatic code-compliant quantity take-off
File(s)
Accepted version
Author(s)
Liu, H
Cheng, JCP
Gan, VJL
Zhou, S
Type
Journal Article
Abstract
The results of quantity take-off (QTO) based on building information modeling (BIM) technology rely heavily on the geometry and semantics of 3D objects that may vary among BIM model creation methods. Furthermore, conventional BIM models do not contain all the required information for automatic QTO and the results do not follow the descriptive rules in the standard method of measurement (SMM). This paper presents a new knowledge model-based framework that incorporates the semantic information and SMM rules in BIM for automatic code-compliant QTO. It begins with domain knowledge modeling, taking into consideration QTO-related information, semantic QTO entities and relationships, and SMM logic formulation. Subsequently, linguistic-based approaches are developed to automatically audit the BIM model integrity for QTO purposes, with QTO algorithms developed and used in a case study for demonstration. The results indicate that the proposed new framework automatically identifies the semantic errors in BIM models and obtains code-compliant quantities.
Date Issued
2022-01-01
Date Acceptance
2021-11-01
Citation
Automation in Construction, 2022, 133
ISSN
0926-5805
Journal / Book Title
Automation in Construction
Volume
133
Identifier
https://www.sciencedirect.com/science/article/pii/S0926580521004751
Subjects
Automatic semantic auditing
Building information modeling
Code-compliant
Data model
Knowledge model-based framework
Quantity take-off
Semantic representation
Notes
The results of quantity take-off (QTO) based on building information modeling (BIM) technology rely heavily on the geometry and semantics of 3D objects that may vary among BIM model creation methods. Furthermore, conventional BIM models do not contain all the required information for automatic QTO and the results do not follow the descriptive rules in the standard method of measurement (SMM). This paper presents a new knowledge model-based framework that incorporates the semantic information and SMM rules in BIM for automatic code-compliant QTO. It begins with domain knowledge modeling, taking into consideration QTO-related information, semantic QTO entities and relationships, and SMM logic formulation. Subsequently, linguistic-based approaches are developed to automatically audit the BIM model integrity for QTO purposes, with QTO algorithms developed and used in a case study for demonstration. The results indicate that the proposed new framework automatically identifies the semantic errors in BIM models and obtains code-compliant quantities.
Publication Status
Published
Article Number
ARTN 104024
Date Publish Online
2021-10-26