AI based Semantic Extensibility and Querying Techniques for Building Information Model

Raghavi,; Gowtham, R.

doi:10.1109/iccs45141.2019.9065840

Cited by 6 publications

(3 citation statements)

References 3 publications

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“…Another study employed the IFC model to convert building data into a node2vec graph model designed to capture semantic similarities among different building components [34]. Raghavi and Gowtham summarized current efforts to integrate the BIM (building information model) with external domain querying and reasoning systems to retrieve details of a building from the underlying building model [35]. The goal of the work was to make the IFC model available to domains other than architecture, construction and engineering.…”

Section: State-of-the-art Building Ontologiesmentioning

confidence: 99%

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

2022

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Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

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Section: State-of-the-art Building Ontologiesmentioning

confidence: 99%

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

2022

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“…[26] presents a semantic reasoning framework for the integration of BIM data sources, which allow to detect errors of included 3D models. In [27], querying different BIM sources by using linked building data of different ontologies has been realized successfully. Despite promising results of using ontologies for semantic interoperability in both domains, the possibility of their connection has still not got proper attention.…”

Section: Semantic Web As Integration Technologymentioning

confidence: 99%

Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Badenko

Bolshakov

Fedotov

et al. 2021

Preprint

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Industrial objects nowadays rapidly transform due to the development of digital technologies. The concept of the Factory of the Future (FoF) involves digitization of all parts of the factory. In this paper two technologies are motivated and considered as the basic technologies that should be used in FoF: Digital Shadow (DS) and Building Information Modelling (BIM). Basic theory on these issues is given and potentials of BIM and DS integration is formulated. Based on the ability of digital technologies, their integration and convergence to generate value, definition Digital Asset is introduced from the economic point of view as a digital resource which brings economic benefit. A concept of integrating BIM and DS technologies for decision support in factory planning is formulated, including Life Cycle Assessment (LCA) and semantic modeling. The concept includes a description of the aggregate of technologies and their interconnections as a Digital Asset of the FoF. Further research objectives are focused on integration of BIM and DS which requires their interoperability ensured by an Ontology-Based Data Access (OBDA) approach, based on the Semantic web.

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“…A centralized database of relevant information comprising of regional interests and metrics would help formulate better inferences for design selection than just relying on individual expertise. However, the main challenge in this process lies in the data representation from sources like Computer Aided Design (CAD) tools, Building Information Modeling (BIM) software, etc [1]. For sketch-type data, data representation becomes very cumbersome as the information contains vector objects and geometric entities; for floor plan data, this is a big hurdle recently realized by Kalevro et al resulting in the creation of the Cu-biCasa5k corpus [2].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Object Detection in Floor-plan through Super Resolution

Khare¹,

Kamal²,

HB³

et al. 2021

Preprint

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Building Information Modelling (BIM) software use scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format. The conversion of floor plan images to fully annotated vector images is a process that can now be realized by computer vision. Novel datasets in this field have been used to train Convolutional Neural Network (CNN) architectures for object detection. Image enhancement through Super-Resolution (SR) is also an established CNN based network in computer vision that is used for converting low resolution images to high resolution ones. This work focuses on creating a multi-component module that stacks a SR model on a floor plan object detection model. The proposed stacked model shows greater performance than the corresponding vanilla object detection model. For the best case, the the inclusion of SR showed an improvement of 39.47% in object detection over the vanilla network. Data and code are made publicly available at https://github.com/rbg-research/Floor-Plan-Detection.

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AI based Semantic Extensibility and Querying Techniques for Building Information Model

Cited by 6 publications

References 3 publications

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

Integration concept of Digital Shadows and BIM for a long-term optimal decision support in factory planning

Enhanced Object Detection in Floor-plan through Super Resolution

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