BIMASR: Framework for Voice-Based BIM Information Retrieval

Shin, Sang-Yun; Issa, Raja R. A.

doi:10.1061/(asce)co.1943-7862.0002138

Cited by 20 publications

(4 citation statements)

References 39 publications

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“…Compliance checking; model optimization IFC file; BIM model; design specification [24,28,32,40,57,68,76,77,[83][84][85][86][87]92,94,97,100,115,117,118,120,140,147,151,152,157]…”

Section: Design Phasementioning

confidence: 99%

Integration of Industry Foundation Classes and Ontology: Data, Applications, Modes, Challenges, and Opportunities

Jia,

Ma,

Zhang

2024

Buildings

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Industry Foundation Classes (IFCs), as the most recognized data schema for Building Information Modeling (BIM), are increasingly combined with ontology to facilitate data interoperability across the whole lifecycle in the Architecture, Engineering, Construction, and Facility Management (AEC/FM). This paper conducts a bibliometric analysis of 122 papers from the perspective of data, model, and application to summarize the modes of IFC and ontology integration (IFCOI). This paper first analyzes the data and models of the integration from IFC data formats and ontology development models to the IfcOWL data model. Next, the application status is summed up from objective and phase dimensions, and four frequent applications with maturity are identified. Based on the aforementioned multi-dimensional analysis, three integration modes are summarized, taking into account various data interoperability requirements. Accordingly, ontology behaves as the representation of domain knowledge, an enrichment tool for IFC model semantics, and a linkage between IFC data and other heterogeneous data. Finally, this paper points out the challenges and opportunities for IFCOI in the data, domain ontology, and integration process and proposes a building lifecycle management model based on IFCOI.

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“…Compliance checking; model optimization IFC file; BIM model; design specification [24,28,32,40,57,68,76,77,[83][84][85][86][87]92,94,97,100,115,117,118,120,140,147,151,152,157]…”

Section: Design Phasementioning

confidence: 99%

Integration of Industry Foundation Classes and Ontology: Data, Applications, Modes, Challenges, and Opportunities

Jia,

Ma,

Zhang

2024

Buildings

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“…Gao et al [14] proposed a BIM prototype semantic search engine by combing the usability of a keyword-based interface with automatic query expansion techniques. Despite the convenient usage, there were irrelevant results, so a large amount of time had to be spent on searching useful and professional text keywords that meet the design requirements for users [15][16][17]. For better retrieval performance, the stylistic consistency of BIM products is a pivotal indicator of design quality and an essential supplement during the BIM design process.…”

Section: Bim Product Recommendationmentioning

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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“…The framework achieved a maximum accuracy of 94.3%. Speech recognition has also been used to retrieve BIM data from BIM software (Shin & Issa, 2021) and to identify heavy construction equipment operating at a construction site (Cheng et al, 2017). To the authors' knowledge, though, the use of speech recognition systems to identify keywords in speech communication on construction sites has yet to be explored.…”

Section: Related Studiesmentioning

confidence: 99%

Keyword identification framework for speech communication on construction sites

Mansoor

Liu

Ali

et al. 2022

mocs

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Worksite communication is a key to boosting teamwork and improving worker performance on the construction worksite. Communication among workers on the construction site mostly consists of speech communication. However, construction sites are typically noisy due to construction tasks like drilling and operation of heavy equipment. Meanwhile, workers on construction sites typically represent a range of different ethnic and linguistic backgrounds and have different speaking accents. This can make it difficult for the listener to understand the speaker clearly, leading to miscommunication and errors in decision making on the construction site. Technological advancements in recent years can be leveraged to mitigate this problem. In this paper, a keyword identification framework is developed for speech communication on the construction site. For this framework, 12 hours of raw audio data containing 18 crane signalman speech commands (referred to as “keywords”) are collected. The crane signalman uses specific keywords to communicate with the crane operator and guide the crane operator in the crane operations. The 2-second audio clips (this being the approximate duration of each keyword) are extracted from the raw audio dataset, and construction site noise is added. Moreover, mel-frequency cepstral coefficients are extracted from the waveform audio dataset. The extracted mel-frequency cepstral coefficients, in turn, are used to train the 1-dimensional convolutional neural network. After training, the model is found to achieve a training accuracy of 97.3%, a validation accuracy of 96.1%, and a testing accuracy of 93.8%. The model is further deployed for real-time identification of keywords in speech, with the model achieving an accuracy of 95.3%. In light of these findings, it can be concluded that the developed framework is suitable for real-time application in noisy construction sites for identifying specific keywords in speech.

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BIMASR: Framework for Voice-Based BIM Information Retrieval

Cited by 20 publications

References 39 publications

Integration of Industry Foundation Classes and Ontology: Data, Applications, Modes, Challenges, and Opportunities

Integration of Industry Foundation Classes and Ontology: Data, Applications, Modes, Challenges, and Opportunities

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

Keyword identification framework for speech communication on construction sites

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