{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T15:10:24Z","timestamp":1778339424165,"version":"3.51.4"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002553","name":"Seoul National University of Science and Technology","doi-asserted-by":"publisher","award":["2025-0646"],"award-info":[{"award-number":["2025-0646"]}],"id":[{"id":"10.13039\/501100002553","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,4,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Automated clash detection in Building Information Modeling (BIM) often produces an excessive number of results, many of which are irrelevant or non-critical to constructability. This burden requires project teams to manually filter clashes, introducing inefficiency and subjectivity. To address this limitation, this study proposed a deep learning\u2013based framework for classifying irrelevant clashes into penetration categories that reflect their constructability implications. A taxonomy of 15 categories was developed through expert consultation, differentiating penetrations by orientation, size, and shape. Two multi-view architectures were evaluated: the Multi-View Convolutional Neural Network (MVCNN), a widely adopted baseline, and the Multi-View Vision Transformer (MVT), a state-of-the-art architecture designed to capture inter-view dependencies through attention mechanisms. A dataset generated from a federated BIM model was used to train and test both models. Results showed that MVT achieved superior performance across accuracy and F1-score, with particular improvements in minority categories involving small or diagonal penetrations. IoU-based analysis further demonstrated that MVT attended more precisely to clash regions, enhancing interpretability. The findings confirmed that Transformer-based multi-view learning offers significant advantages for clash classification. By linking automated classification with reinforcement and constructability requirements, the proposed framework supports more reliable constructability analysis, cost estimation, and project planning.<\/jats:p>","DOI":"10.1093\/jcde\/qwag032","type":"journal-article","created":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T12:33:37Z","timestamp":1774182817000},"page":"227-251","source":"Crossref","is-referenced-by-count":0,"title":["Transformer-based multi-view learning for BIM clash classification: Penetration taxonomy and constructability analysis"],"prefix":"10.1093","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3593-0657","authenticated-orcid":false,"given":"Youngsu","family":"Yu","sequence":"first","affiliation":[{"name":"Korea Construction Standards Center, Korea Institute of Civil Engineering and Building Technology , Goyang 10223 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1953-5935","authenticated-orcid":false,"given":"Hyunwoo","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University of Science and Technology Department of Civil Engineering, , Seoul 01811 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4725-6041","authenticated-orcid":false,"given":"Wonbok","family":"Lee","sequence":"additional","affiliation":[{"name":"Seoul National University of Science and Technology Department of Civil Engineering, , Seoul 01811 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1029-3375","authenticated-orcid":false,"given":"Bonsang","family":"Koo","sequence":"additional","affiliation":[{"name":"Seoul National University of Science and Technology Department of Civil Engineering, , Seoul 01811 ,","place":["Republic of Korea"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"2026050910320159000_bib2","doi-asserted-by":"publisher","first-page":"3611","DOI":"10.3390\/buildings14113611","article-title":"Improved building information modeling-based method for prioritizing clash detection in the building construction design phase","volume":"14","author":"Bitaraf","year":"2024","journal-title":"Buildings"},{"key":"2026050910320159000_bib3","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neunet.2018.07.011","article-title":"A systematic study of the class imbalance problem in convolutional neural networks","volume":"106","author":"Buda","year":"2018","journal-title":"Neural Networks"},{"key":"2026050910320159000_bib5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"Journal of Artificial Intelligence Research"},{"key":"2026050910320159000_bib6","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2110.13083","article-title":"MVT: Multi-view vision transformer for 3D object recognition","author":"Chen","year":"2021","journal-title":"arXiv"},{"key":"2026050910320159000_bib7","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1109\/cvpr.2019.00020","article-title":"Autoaugment: Learning augmentation strategies from data","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Cubuk","year":"2019"},{"key":"2026050910320159000_bib8","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1109\/IROS.2015.7353481","article-title":"VoxNet: A 3D convolutional neural network for real-time object recognition","volume-title":"Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems","author":"Daniel","year":"2015"},{"key":"2026050910320159000_bib9","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","article-title":"An image is worth 16\u00d716 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020","journal-title":"arXiv"},{"key":"2026050910320159000_bib11","doi-asserted-by":"publisher","first-page":"228","DOI":"10.29007\/n223","article-title":"Investigation of machine learning for clash resolution automation","volume":"2","author":"Harode","year":"2021","journal-title":"EPiC Series in Built Environment"},{"key":"2026050910320159000_bib12","doi-asserted-by":"publisher","first-page":"04024005","DOI":"10.1061\/JCCEE5.CPENG-5548","article-title":"Developing a machine-learning model to predict clash resolution options","volume":"38","author":"Harode","year":"2024","journal-title":"Journal of Computing in Civil Engineering"},{"key":"2026050910320159000_bib13","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1177\/01436244221080023","article-title":"An improvement in clash detection process by prioritizing relevance clashes using fuzzy-AHP methods","volume":"43","author":"Hasannejad","year":"2022","journal-title":"Building Services Engineering Research and Technology"},{"key":"2026050910320159000_bib15","volume-title":"Clash Resolution Optimization Based on Component and Clash Dependent Networks","author":"Hu","year":"2020"},{"key":"2026050910320159000_bib16","doi-asserted-by":"publisher","first-page":"04019003","DOI":"10.1061\/(ASCE)CP.1943-5487.0000810","article-title":"Clash relevance prediction based on machine learning","volume":"33","author":"Hu","year":"2019","journal-title":"Journal of Computing in Civil Engineering"},{"key":"2026050910320159000_bib17","doi-asserted-by":"publisher","first-page":"102832","DOI":"10.1016\/j.autcon.2019.102832","article-title":"Holistic clash detection improvement using a component dependent network in BIM projects","volume":"105","author":"Hu","year":"2019","journal-title":"Automation in Construction"},{"key":"2026050910320159000_bib18","doi-asserted-by":"publisher","first-page":"04021085","DOI":"10.1061\/(ASCE)CO.1943-7862.0002092","article-title":"Component change list prediction for BIM-based clash resolution from a graph perspective","volume":"147","author":"Hu","year":"2021","journal-title":"Journal of Construction Engineering and Management"},{"key":"2026050910320159000_bib20","doi-asserted-by":"publisher","first-page":"324","DOI":"10.22260\/ISARC2019\/0044","article-title":"Automatic classification of design conflicts using rule-based reasoning and machine learning\u2014An example of structural clashes against the MEP model","volume":"36","author":"Huang","year":"2019","journal-title":"Proceedings of the International Symposium on Automation and Robotics in Construction"},{"key":"2026050910320159000_bib23","doi-asserted-by":"publisher","first-page":"012009","DOI":"10.1088\/1755-1315\/476\/1\/012009","article-title":"Implementation of building information modeling for construction clash detection process in the design stage: A case study of Malaysian police headquarter building","volume":"476","author":"Kermanshahi","year":"2020","journal-title":"IOP Conference Series: Earth and Environmental Science"},{"key":"2026050910320159000_bib24","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1093\/jcde\/qwae041","article-title":"Client-centered detached modular housing: Natural language processing-enabled design recommender system","volume":"11","author":"Kim","year":"2024","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026050910320159000_bib25","doi-asserted-by":"publisher","first-page":"101200","DOI":"10.1016\/j.aei.2020.101200","article-title":"Automatic classification of wall and door BIM element subtypes using 3D geometric deep neural network","volume":"47","author":"Koo","year":"2021","journal-title":"Advanced Engineering Informatics"},{"key":"2026050910320159000_bib26","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1093\/jcde\/qwaa075","article-title":"A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models","volume":"8","author":"Koo","year":"2021","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026050910320159000_bib27","doi-asserted-by":"publisher","first-page":"22","DOI":"10.7315\/cadcam.2015.022","article-title":"BIM clash quality control by expert system","volume":"20","author":"Kwon","year":"2015","journal-title":"Korean Journal of Computational Design and Engineering"},{"key":"2026050910320159000_bib29","doi-asserted-by":"publisher","first-page":"5324","DOI":"10.3390\/app9245324","article-title":"Filtering of irrelevant clashes detected by BIM software using a hybrid method of rule-based reasoning and supervised machine learning","volume":"9","author":"Lin","year":"2019","journal-title":"Applied Sciences"},{"key":"2026050910320159000_bib34","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1016\/j.aei.2012.08.003","article-title":"Querying a building information model for construction-specific spatial information","volume":"26","author":"Nepal","year":"2012","journal-title":"Advanced Engineering Informatics"},{"key":"2026050910320159000_bib35","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1109\/CVPR.2017.16","article-title":"PointNet: Deep learning on point sets for 3D classification and segmentation","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"Qi","year":"2017"},{"key":"2026050910320159000_bib36","doi-asserted-by":"publisher","first-page":"101711","DOI":"10.1016\/j.aei.2022.101711","article-title":"Toward artificially intelligent cloud-based building information modelling for collaborative multidisciplinary design","volume":"53","author":"Sacks","year":"2022","journal-title":"Advanced Engineering Informatics"},{"key":"2026050910320159000_bib37","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1109\/ICCV.2017.74","article-title":"Grad-CAM: Visual explanations from deep networks via gradient-based localization","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Selvaraju","year":"2017"},{"key":"2026050910320159000_bib38","doi-asserted-by":"publisher","first-page":"103439","DOI":"10.1016\/j.rineng.2024.103439","article-title":"Enhanced clash detection in building information modeling: Leveraging modified extreme gradient boosting for predictive analytics","volume":"24","author":"Shehadeh","year":"2024","journal-title":"Results in Engineering"},{"key":"2026050910320159000_bib39","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1093\/jcde\/qwae021","article-title":"Automated CAD-to-BIM generation of restroom sanitary plumbing system","volume":"11","author":"Shin","year":"2024","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026050910320159000_bib40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"Journal of Big Data"},{"key":"2026050910320159000_bib41","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1109\/ICCV.2015.114","article-title":"Multi-view convolutional neural network for 3D shape recognition","volume-title":"Proceedings of the IEEE International Conference on Computer Vision","author":"Su","year":"2015"},{"key":"2026050910320159000_bib43","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-84300-6_13","article-title":"Attention is all you need","volume-title":"Advances in Neural Information Processing Systems","author":"Vaswani","year":"2017"},{"key":"2026050910320159000_bib44","doi-asserted-by":"publisher","first-page":"101770","DOI":"10.1016\/j.aei.2022.101770","article-title":"Detecting logical relationships in mechanical, electrical, and plumbing (MEP) systems with BIM using graph matching","volume":"54","author":"Wang","year":"2022","journal-title":"Advanced Engineering Informatics"},{"key":"2026050910320159000_bib45","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2304.11672","article-title":"CBIM: A graph-based approach to enhance interoperability using semantic enrichment","author":"Wang","year":"2023","journal-title":"arXiv"},{"key":"2026050910320159000_bib47","doi-asserted-by":"publisher","first-page":"934","DOI":"10.3390\/buildings12070934","article-title":"Automated rule checking for MEP systems based on BIM and KBMS","volume":"12","author":"Xie","year":"2022","journal-title":"Buildings"},{"key":"2026050910320159000_bib49","doi-asserted-by":"crossref","unstructured":"Yu Y., Ha D., Lee K., Choi J., Koo B. (2022). ArchShapesNet: A novel dataset for benchmarking architectural building information modeling element classification algorithms. Journal of Computational Design and Engineering, 9, 1449\u20131466. 10.1093\/jcde\/qwac064.","DOI":"10.1093\/jcde\/qwac064"},{"key":"2026050910320159000_bib50","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1093\/jcde\/qwaf033","article-title":"Evaluating ChatGPT on Korea\u2019s BIM Expertise Exam and improving its performance through RAG","volume":"12","author":"Yu","year":"2025","journal-title":"Journal of Computational Design and Engineering"},{"key":"2026050910320159000_bib51","doi-asserted-by":"publisher","first-page":"6023","DOI":"10.1109\/iccv.2019.00612","article-title":"Cutmix: Regularization strategy to train strong classifiers with localizable features","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision","author":"Yun","year":"2019"},{"key":"2026050910320159000_bib52","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1710.09412","article-title":"mixup: Beyond empirical risk minimization","author":"Zhang","year":"2017","journal-title":"arXiv"}],"container-title":["Journal of Computational Design and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jcde\/advance-article-pdf\/doi\/10.1093\/jcde\/qwag032\/67484347\/qwag032.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/13\/4\/227\/67484347\/qwag032.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jcde\/article-pdf\/13\/4\/227\/67484347\/qwag032.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T14:32:10Z","timestamp":1778337130000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jcde\/article\/13\/4\/227\/8537782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":37,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4,1]]}},"URL":"https:\/\/doi.org\/10.1093\/jcde\/qwag032","relation":{},"ISSN":["2288-5048"],"issn-type":[{"value":"2288-5048","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,4]]},"published":{"date-parts":[[2026,3,23]]}}}