OutDet: an algorithm for extracting the outer surfaces of building information models for integration with geographic information systems

Zhou, Xiaoping; Zhao, Jichao; Wang, Jia; Su, Dingding; Zhang, Haiyan; Guo, Ming; Guo, Maozu; Li, Zhi

doi:10.1080/13658816.2019.1572894

Cited by 21 publications

(13 citation statements)

References 33 publications

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“…Currently, some efforts have been made on BIM visualization, such as the online BIM visualization system based on IFC and WebGL (Zhou et al, 2018). The OutDet algorithm was proposed to select the representative geometric features of BIM models, to eliminate most unnecessary features, and to reduce the computational burden of model visualization (Zhou et al, 2019). To greatly reduce the calculation time and space the IFC file comparison process, Shi proposed a content-based automatic comparison approach, named IFCdiff (Shi et al, 2018).…”

Section: Related Researches 21 Extensive Work In Bimmentioning

confidence: 99%

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Wang¹,

Gao²,

Zhou³

et al. 2021

ITcon

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Building Information Modelling (BIM) captures numerous information the life cycle of buildings. Information retrieval is one of fundamental tasks for BIM decision support systems. Currently, most of the BIM retrieval systems focused on querying existing BIM models from a BIM database, seldom studies explore the multi-scale information retrieval from a BIM model. This study proposes a multi-scale information retrieval scheme for BIM jointly using the hierarchical structure of BIM and Natural Language Processing (NLP). Firstly, a BIM Hierarchy Tree (BIH-Tree) model is constructed to interpret the hierarchical structure relations among BIM data according to Industry Foundation Class (IFC) specification. Secondly, technologies of NLP and International Framework for Dictionaries (IFD) are employed to parse and unify the queries. Thirdly, a novel information retrieval scheme is developed to find the multi-scale information associated with the unified queries. Finally, the retrieval method proposed in this study is applied to an engineering case, and the practical results show that the proposed method is effective.

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Section: Related Researches 21 Extensive Work In Bimmentioning

confidence: 99%

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Wang¹,

Gao²,

Zhou³

et al. 2021

ITcon

Self Cite

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“…As shown in Figure 3, exterior building entities (ExteriorBI M) first are obtained from each BIM model using previous algorithms that aim to reduce the computing burden [18,19]. Then, similar to other multi-scale GIS data sources (e.g., terrain, satellite imagery, and oblique aerial images) in virtual globes, we employ the LLG-based tile pyramid of the discrete global grid to organize the ExteriorBI Ms [12,34].…”

Section: Semantic-based Data Organization For Outdoor Scenes In Bim Modelsmentioning

confidence: 99%

“…Previous studies have generally reduced geometric data by mesh simplification or by conversion to lower level of detail (LOD) models [16,17]. However, these approaches can change topological relationships between indoor spaces and the geometric accuracy of the building [18]. To address this issue, some studies have extracted exterior entities from BIM models, which can dynamically reduce geometric information by removing non-exterior entities from outdoor views.…”

Section: Introductionmentioning

confidence: 99%

“…To address this issue, some studies have extracted exterior entities from BIM models, which can dynamically reduce geometric information by removing non-exterior entities from outdoor views. Moreover, when entering into the indoor space, it is possible to load indoor entities and even remove other BIM models from outdoor scenes [18,19]. However, previous algorithms face the following two problems.…”

Section: Introductionmentioning

confidence: 99%

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Visualizing Large-Scale Building Information Modeling Models within Indoor and Outdoor Environments Using a Semantics-Based Method

Chen

Huang

2021

IJGI

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Building information modeling (BIM), with detailed geometry and semantics of the indoor environment, has become an essential part of smart city development and city information modeling (CIM). However, visualizing large-scale BIM models within geographic information systems (GIS), such as virtual globes, remains a technological challenge with limited hardware resources. Previous methods generally removed indoor features in a single-source (BIM) scene to reduce the computational burden from outdoor views, which have not been applied to the multi-source and -scale geographic environment (e.g., virtual globes). This approach neglected special BIM semantics (e.g., transparent windows), which may miss a part of geographic features or buildings and cause unreasonable visualization. Besides, the method overlooked indoor visualization optimization, which may burden computing resources when visualizing big and complex buildings from indoor views. To address these problems, we propose a semantics-based method for visualizing large-scale BIM models within indoor and outdoor environments. First, we organize large-scale BIM models based on a latitude-longitude grid (LLG) in the outdoor environment; a multilayer cell-and-portal graph is used to index the structure of the BIM model and building entities. Second, we propose a scheduling algorithm to achieve the integrated visualization in indoor and outdoor environments considering BIM semantics. The application of the proposed method to a multi-scale and -source environment confirmed that it can achieve an effective and efficient visualization for huge BIM models in indoor-outdoor scenes. Compared with the previous study, the proposed method considers the BIM semantics and thus can visualize more complete features from outdoor and indoor views of BIM models in the virtual globe. Besides, the study only loads as visible data as possible, which can retain lower the volume of increased geometry, and thus keep a higher frame rate for the tested areas.

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“…Building Information Modeling (BIM) is a digital representation of physical and functional characteristics of a facility [1]. A BIM records reliable information of a building throughout its life-cycle, including planning, design, construction, operations and facility management phases [2]. By providing interoperable construction data, BIM enables collaboration among stakeholders [3], and provides a reliable basis for decisions during the building life cycle [4].…”

Section: Introductionmentioning

confidence: 99%

Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing

et al. 2019

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Building Information Modeling (BIM) is a promising technology for building informatics. Currently, an increasing number of applications adopt BIM to improve the building operations and facility management. In these applications, matching real-world facilities to the corresponding BIM items is a fundamental yet challenging task. This study addresses this issue using Natural Language Processing. Firstly, a novel BIM hierarchy tree (HiTree) is proposed to model the original spatial structure relationships of a BIM. Then, the locations of facilities are extracted from natural language through processes of word segmentation, keyword extraction, and semantic disambiguation. Thirdly, an algorithm that matches real-world facilities to the BIM data is developed using the HiTree and the extracted locations. Finally, a concrete case for a 35,000 m 2 library is presented to verify the effectiveness of the proposed solution. BIM has become a common paradigm in the construction industry, and our scheme can facilitate more applications of BIM in building operations and facility management. One of the most representative applications is integrating the BIM data and information within IoT (Internet of Things) system intelligently by matching the BIM data to real-world facilities.INDEX TERMS Building information modeling (BIM), facility, natural language processing (NLP), facility management.

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OutDet: an algorithm for extracting the outer surfaces of building information models for integration with geographic information systems

Cited by 21 publications

References 33 publications

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Multi-scale Information Retrieval for BIM using Hierarchical Structure Modelling and Natural Language Processing

Visualizing Large-Scale Building Information Modeling Models within Indoor and Outdoor Environments Using a Semantics-Based Method

Matching Real-World Facilities to Building Information Modeling Data Using Natural Language Processing

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