The BIMbot: A Cognitive Assistant in the BIM Room

Mutis, Iván; Ramachandran, Adithya; Martinez, Marc Gil

doi:10.1007/978-3-030-00220-6_19

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…Data ordered with IFC can be reviewed and studied with BIM model checking software tools, such as Solibri. Then, it can be suitably mined manually, with semantic and/or latent techniques ( [53,54,55,56,57,58]), or with dedicated data parsers [59], and exported into file formats as input for ML suites; examples of such formats are .arff files for the Waikato Environment for Knowledge Analysis (WEKA), or structured .csv files to be incorporated in ML libraries of the Surprise Scikit, a Python-powered scientific toolkit for recommender systems. But for this data to be translated into meaningful independent input variables, and then connected with meaningful dependent output variables as part of a ML modelling (and especially SML) addressing the research gap mentioned in the previous section (namely, the absence of BIM data utilization for the prediction of a building project's performance, and especially its delivery cost and time overheads), it needs to be incorporated in a suitable theoretical and conceptual framework.…”

Section: Data In Ifcs and Constructability For Machine Learning Predimentioning

confidence: 99%

Building Information Models’ Data for Machine Learning Systems in Construction Management

Kifokeris¹,

Roupé²,

Johansson³

et al. 2019

Proceedings of the Creative Construction Conference 2019

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Qualitative and quantitative data are important in construction management. However, despite the capabilities of construction informatics, such data and its sources have scarcely been fully and systematically utilized for predictive purposes. Building Information Models (BIM) are such a data source. Within BIM, information structures enabling interoperability and providing utilizable data throughout the various Levels of Development (LODs) of a buildingfor example, Industry Foundation Classes (IFCs)can be fully and meaningfully exploited through data mining, and more particularly, via machine learning. In this paper, the capabilities of the information structures found in IFCs to be used as data sources for developing machine learning predictive models, will be examined. In addition, and by conceptually tying such data with constructability, their suitability for predictingthrough such machine learning modelsthe delivery cost and time overheads of a construction project, will be considered.

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