Automated generation of steel connections of BIM by machine learning
Helminen, Joonas (2019)
2019
Rakennustekniikan DI-tutkinto-ohjelma
Rakennetun ympäristön tiedekunta - Faculty of Built Environment
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Hyväksymispäivämäärä
2019-08-28
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-201906252205
https://urn.fi/URN:NBN:fi:tuni-201906252205
Tiivistelmä
In the last decades, building information modeling (BIM) has increased significantly and it has widely accepted in the construction industry. This has made available a significant amount of digital data that makes possible the use of machine learning techniques in the BIM field. However, machine learning techniques are yet to utilize and current approaches for automation are yet to take full advantage of the information gathered in previously engineered BIM models.
In this study, it was investigated improving modelling efficiency by developing a new toolkit for automated generation of steel connections in BIM models by machine learning techniques. The toolkit had three objectives: generate a training dataset, predict connections between structural members based on the dataset, and automatically model them. The toolkit consists of modules developed in C# and Python, with the machine learning module being implemented using the latter. For this module, the k-nearest neighbors (k-NN) algorithm was used for prediction.
The toolkit was tested on 13 industrial steel structures. Connections were searched and automatically created to three models and a training dataset contained connections from 10 models. The results were positive, even being limited by using only a 10-model database. By creating a training set from finished models, it was found that it is possible to predict and automatically insert valid structural connections in new BIM models. Overall, our findings suggest that our methodology promises to be of significant assistance in improving present methods of generating steel connections in building design.
In this study, it was investigated improving modelling efficiency by developing a new toolkit for automated generation of steel connections in BIM models by machine learning techniques. The toolkit had three objectives: generate a training dataset, predict connections between structural members based on the dataset, and automatically model them. The toolkit consists of modules developed in C# and Python, with the machine learning module being implemented using the latter. For this module, the k-nearest neighbors (k-NN) algorithm was used for prediction.
The toolkit was tested on 13 industrial steel structures. Connections were searched and automatically created to three models and a training dataset contained connections from 10 models. The results were positive, even being limited by using only a 10-model database. By creating a training set from finished models, it was found that it is possible to predict and automatically insert valid structural connections in new BIM models. Overall, our findings suggest that our methodology promises to be of significant assistance in improving present methods of generating steel connections in building design.