Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing

Wang, Hao; Meng, Xin; Zhu, Xingyu

doi:10.1016/j.autcon.2022.104317

Cited by 28 publications

(5 citation statements)

References 93 publications

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“…Case-based reasoning (CBR) has been used extensively in construction project management-after all construction is an experience-based industry (see Fong & Choi 2009;Dogra 2020). To enhance the formalized capture and retrieval of cases, recently, researchers combined CBR with several tools, including ontologies (Yurchyshyna and Zarli 2009;) and natural language processing (Wang et al 2022). While the proposed approach is precedence-based, it differs from CBR in several ways.…”

Section: Experience-based Reasoning and Prototypingmentioning

confidence: 99%

How typical is your project? The need for a no-model approach for information management in AEC

El-Diraby¹

2023

ITcon

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This paper discusses the merit of using a no-model approach (no common product models or ontologies, etc.) for managing information in the AEC. It proposes an option for such an approach through the generation and analysis of semantic and social networks of communication between project stakeholders. The proposed approach advocates for a bottom-up discovery of knowledge constructs from stakeholder communication. Knowledge constructs are mini two-mode networks containing, on the one hand, clusters of concepts that appear frequently in the semantic networks of stakeholder communication; and, on the other hand, the social networks of stakeholders discussing these concepts. Using common models (such as IFC) has several limitations, including inflexibility to recognize and accommodate project contexts (which vary constantly), inability to timely capture the emergence of knowledge, and the scope creep problem (the ever-existing need to add more concepts to the common model from within and outside ACE domain). The no-model approach presented here is meant to complement and not replace the established model-based approach. This approach is built on the belief in the ontological agency of project stakeholders: knowledge is a social phenomenon that emerges through interactions between people. It advocates a shift from a top-down format where experts or standards clearinghouses tell (force) practitioners what should be true about their project. In every project, stakeholders customize (the structure of) established knowledge and adopt elements from emerging knowledge to address project-specific needs. They use the more superior intelligence (the human one) to innovate a ‘model of what they know’ to guide the management of the project in a manner specific to its context. By studying projects’ communication, we tell (inform) project stakeholders what knowledge constructs can be found in their communication. Unlike generic/static models, the resulting knowledge constructs are by default sensitive to project conditions. We should re-design our information management systems to be able to recognize and adaptively use the constructs established by project teams to facilitate their sharing of data (along with the established scheme, such as IFC). Relatedly used constructs can be nominated as AEC-wide prototype constructs, representing what we know about a typical project. At the initiation of a new project, these can be the starting scheme used by information and communication systems. As the project evolves and the project's own constructs are generated, the project-specific constructs should guide the flow of information. Contrasting project constructs against prototypes should inform the stakeholders of not only what is factual about their view/model of knowledge, but also how unique are they (from generic/base knowledge). This approach to no-model thinking is advantageous for several reasons. First, addressing the model rigidity problem. Because of the increasing complexity of projects, no single/standardized model can capture all contexts. Second, the increasing need for handling project unstructured data. The proposed approach helps formalize knowledge constructs from such data using network science. Third, recognizing and tracking the evolutionary nature of knowledge. Fourth, supporting innovation: instead of forcing knowers (people) to comply with a static model of reality, the new approach encourages them to imagine new possible futures/ worlds—after all, the true essence of digital twinning is to virtualize futures not just to digitize the present.

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Section: Experience-based Reasoning and Prototypingmentioning

confidence: 99%

How typical is your project? The need for a no-model approach for information management in AEC

El-Diraby¹

2023

ITcon

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“…For the color features, the color correlation matrix and color histogram are used to represent the colors at the pixel level and their neighboring areas. The value of mean, variance, skewness, and kurtosis can be calculated by Equations ( 5)- (8). For the color histogram, in the LAB color space, the mean, variance, and the first, second, and third quantile and entropy are calculated.…”

Section: Deep and Design Feature Computationmentioning

confidence: 99%

“…The most pertinent models can assist users in the areas of e-commerce [5], software engineering [6], smart cities [7], etc. Some studies [8,9] explore the text-based BIM retrieval scheme and case-based reasoning (CBR) [10] strategy to stimulate manual BIM product selection. Semantic information relating to shape and apparencies is used to retrieve BIM models.…”

Section: Introductionmentioning

confidence: 99%

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

Cui,

Zang,

Liu

et al. 2023

Buildings

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The rapid increase in building components on the building information model (BIM) object database has created new demand for BIM product recommendations to improve design efficiency. Current efforts mainly focus on the shape and contents of the products, instead of stylistic consistency, which is a crucial factor during the practical design process. To tackle such a problem, this paper proposes a novel framework to capture stylistic features based on long-range design dependencies with structural preservation, of which the snapshots of BIM products have been used to extract the stylistic features; core patches with strong style, generated by the pre-trained saliency model, are the root nodes; stylistic correlations are calculated as the hyperedges by tree-based operations; deep features and design features are proposed to represent the low-level and style distribution based on the study of design theory; and an ensemble learning strategy is introduced to solve the unbalanced classifier performance. An ablation study is conducted to validate the effectiveness of the proposed framework, in which comparative experiments with state-of-the-art baselines demonstrate the advantages of the proposed method.

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“…Artificial Intelligence (AI), knowledge engineering and informatic technologies offer promising solutions to the information sharing challenges of the construction industry; and several approaches employing NLP, and semantic techniques have been suggested in the literature [10], [11], [12], [13] [14]. Intelligent question answering systems such as OpenAI's Chat Generative Pre-Trained Transformer (ChatGPT) are at the forefront of a new paradigm in the Information Retrieval (IR) field, and related approaches leveraging deep learning [15] and chatbots [16] have emerged for sharing safety hazard knowledge in the construction domain.…”

Section: Introductionmentioning

confidence: 99%

A Linked Data and Ontology-Based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry

Pedro,

Baik,

et al. 2023

IEEE Access

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The provision of suitable training materials is crucial in preparing construction workers for the safety risks inherent in their work environments. However, retrieving relevant training resources for specific work tasks and worksites poses challenges due to the dispersed, unstructured, and multifarious nature of available content. This paper proposes a linked data and ontology-based system framework for effectively sharing and accessing educational safety contents from heterogeneous sources in a standardized way. The main technical solutions in the framework include an ontology that leverages the Resource Description Framework and Ontology Web Language to structure and publish educational safety training contents based on the linked data principles, and a content retrieval method which leverages the SPARQL protocol and RDF query language. Experimental results confirm that the proposed approach outperforms existing repositories in terms of search time, search result precision, and accuracy. It is envisaged that the framework would aid safety trainers in sharing, searching, and retrieving didactically appropriate materials to prepare workers for the unique safety challenges of their jobsite environments.

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Improving knowledge capture and retrieval in the BIM environment: Combining case-based reasoning and natural language processing

Cited by 28 publications

References 93 publications

How typical is your project? The need for a no-model approach for information management in AEC

How typical is your project? The need for a no-model approach for information management in AEC

BIM Product Style Classification and Retrieval Based on Long-Range Style Dependencies

A Linked Data and Ontology-Based Framework for Enhanced Sharing of Safety Training Materials in the Construction Industry

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