| id |
ecaade2023_89 |
| authors |
Ahmadpanah, Hooshiar, Haidar, Adonis and Latifi, Seyed Mostafa |
| year |
2023 |
| title |
BIM and Machine Learning (ML) Integration in Design Coordination: Using ML to automate object classification for clash detection |
| doi |
https://doi.org/10.52842/conf.ecaade.2023.2.619
|
| source |
Dokonal, W, Hirschberg, U and Wurzer, G (eds.), Digital Design Reconsidered - Proceedings of the 41st Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2023) - Volume 2, Graz, 20-22 September 2023, pp. 619–628 |
| summary |
Amongst the countless benefits of BIM, clash detection appears to be one of the most recognized ones. This is due to the automated manner in which clashes can be detected in the design stage in comparison to the cumbersome drawing-based clash detection applied in traditional design coordination. When BIM clash detection software, such as Navisworks or Solibri, is used, thousands of clashes can be detected automatically, and a report is generated containing a list of all the clashes with an image of each clash. In most cases, a large number of irrelevant/ignorable clashes can be found, making it extremely difficult and time-consuming to classify those clashes in order to assign responsibilities to manage those clashes, and more importantly specifying which clashes are relevant and which are not. Therefore, finding an automated machine-enabled method to classify clashes into relevant and irrelevant appears to be indispensable. This paper provides the first step towards this automation by developing a Machine Learning (ML) algorithm capable of recognizing the types of elements from images that are originated from the clash detection report. To achieve this, a Deep Learning (DL) algorithm called ‘YOLO’, that is based on object recognition, is developed, and a set of various images indicating different kinds of clashes are used as the dataset. Using the “Makesense” platform, the images are labeled into different categories to feed the algorithm. The algorithm was able to recognize trusses and beams from the images saved in the data set, which is the first step towards object classification. The paper contributes to the knowledge by, firstly, enabling the clashes to be classified based on images rather than numeric information data, and secondly, by applying the DL algorithm that is used in many author industries in the context of clash detection within a construction project. |
| keywords |
BIM, Clash Detection, Machine Learning (ML), Deep Learning, Image Recognition |
| series |
eCAADe |
| email |
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| full text |
 file.pdf (1,298,241 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/12/10 10:49 |
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