A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

Líu, Hao; Cheng, Jackie; Gan, Vincent J.L.; Zhou, Shanjing

doi:10.1016/j.aei.2022.101757

Cited by 14 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various techniques for extracting graphs from IFC have been reported in the literature [32][33][34]. These graphs are used for rule checking [35], for data merging [36], for generative design of modular buildings [37], and for model auditing and quantity takeoffs [38].…”

Section: Buildings As Graphsmentioning

confidence: 99%

Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification

Austern,

Bloch,

Abulafia

2024

Buildings

View full text Add to dashboard Cite

The application of machine learning (ML) for the automatic classification of building elements is a powerful technique for ensuring information integrity in building information models (BIMs). Previous work has demonstrated the favorable performance of such models on classification tasks using geometric information. This research explores the hypothesis that incorporating contextual information into the ML models can improve classification accuracy. To test this, we created a graph data structure where each building element is represented as a node assigned with basic geometric information. The connections between the graph nodes (edges) represent the immediate neighbors of that node, capturing the contextual information expressed in the BIM model. We devised a process for extracting graphs from BIM files and used it to construct a graph dataset of over 42,000 building elements and used the data to train several types of ML models. We compared the classification results of models that rely only on geometry, to graph neural networks (GNNs) that leverage contextual information. This work demonstrates that graph-based models for building element classification generally outperform classic ML models. Furthermore, dividing the graphs that represent complete buildings into smaller subgraphs further improves classification accuracy. These results underscore the potential of leveraging contextual information via graphs for advancing ML capabilities in the BIM environment.

show abstract

Section: Buildings As Graphsmentioning

confidence: 99%

Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification

Austern,

Bloch,

Abulafia

2024

Buildings

View full text Add to dashboard Cite

show abstract

“…Lastly, the incompleteness and uncertainty of data in knowledge graphs may affect auditors' judgment and decision-making regarding risks. Therefore, addressing these challenges requires a comprehensive consideration of issues such as data quality, update mechanisms, query performance, and uncertain reasoning [90].…”

Section: Knowledge Graphs and Intelligent Auditingmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

show abstract

“…They also report that significant additional work during the modeling phase was required in the design process in ArchiCad to increase the IQ for quantity takeoffs. Liu et al (2022) also highlights the importance of modeling practices for IQ in BIM and proposes a data-driven framework based on BIM and knowledge graphs to represent unstructured BIM data for automatic inferences of auditing results of BIM model mistakes. Modeling practices are a subject of concern in other industries that also use parametric CAD (Computer Aided Design) (Bodein et al, 2014;Camba et al, 2016).…”

Section: Iq In Bim For Fmmentioning

confidence: 99%

BIM for FM: understanding information quality issues in terms of compliance with owner’s Building Information Modeling Requirements

Tsay

Staub‐French

Poirier

et al. 2023

Front. Built Environ.

View full text Add to dashboard Cite

The promise of Building Information Modeling (BIM) for Facilities Management (FM) is based upon building information models as reliable sources of information for decisions during a facility’s life cycle, from the planning to end of life. However, the premise of BIM as an enabler for the delivery of reliable information for FM has numerous challenges. Previous studies have shown that the quality of information provided through current design practices with BIM is inadequate for FM. These information quality (IQ) issues are mostly related to incomplete, inaccurate, inconsistent, and unintelligible facility information that ultimately reduce the usefulness of BIM-based information for FM purposes. In order to support BIM-enabled delivery of useful asset information for FM, certain IQ criteria must be met. Based on three ethnographic case studies, including the analysis of more than two thousand documented BIM for FM-related compliance issues, this research identifies ten key IQ criteria in design BIMs that must be considered to reliably support BIM use for FM, correlates these IQ criteria with key IQ dimensions identified in the literature to reflect their frequency of occurrence, and identifies sources of IQ issues in BIM for FM within design practice. A mixed-method approach for data collection from the case studies is adopted, including document analysis, semi-structured interviews, meeting observation, and a survey. The data collected are analyzed through an iterative coding process, in which the themes emerged are refined and tested as part of a grounded theory approach. This study contributes to the development of the theoretical concept of IQ in BIM for FM that is grounded in data from actual projects with stringent BIM requirements for FM and thorough compliance processes. As a practical contribution, the findings in this study should enable owners and designers to develop a more optimized asset information delivery process, increasing the value of the information in design BIMs for operations with minimal impact on current modeling practices.

show abstract

A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

Cited by 14 publications

References 29 publications

Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification

Incorporating Context into BIM-Derived Data—Leveraging Graph Neural Networks for Building Element Classification

From data to insights: the application and challenges of knowledge graphs in intelligent audit

BIM for FM: understanding information quality issues in terms of compliance with owner’s Building Information Modeling Requirements

Contact Info

Product

Resources

About