Skip to main content
Top

08-10-2024

Semantic Enrichment of a BIM Model Using Revit: Automatic Annotation of Doors in High-Rise Residential Building Models Using Machine Learning

Authors: Soheila Bigdeli, Pieter Pauwels, Steven Verstockt, Nico Van de Weghe, Bart Merci

Published in: Fire Technology

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This study explores the potential of automated fire safety conformity checks using a BIM tool. The focus is on travel distance regulations, one of the major concerns in building design. Checking travel distances requires information about the location of exits. Preferably, the Building Information Model (BIM) of the building should contain such information, and if not, user input can be requested. However, a faster, yet still reliable and accurate, methodology is strived for. Therefore, this study presents an automated solution that uses machine learning to add the required semantics to the building model. Three algorithms (Bagged KNN, SVM, and XGBoost) are evaluated at a low Level of Detail (LOD) BIM models. With precision, recall, and F1 scores ranging from 0.87 to 0.90, the model exhibits reliable performance in the classification of doors. In a validation process with two separate sample buildings, the models accurately identified all ’Exits’ in the first building with 94 samples, with only 5 to 6 minor misclassifications. In the second building, all models- with the exception of the SVM - correctly classified every door. Despite their theoretical promise, oversampling techniques do not enhance the results, indicating their inherent limitations.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Appendix
Available only for authorised users
Footnotes
1
The variance is an error from sensitivity to small fluctuations in the training set. High variance may result from an algorithm modeling the random noise in the training data (overfitting).
 
Literature
1.
go back to reference Arthur EC, John RH, Pam P, Casey CG, Robert ES (2008) Codes and standards for the built environment. In: Arthur, E.C., Casey, C.G. (eds.) Fire protection handbook, vol. 1, 20th edn., pp. 1–51. National Fire Protection Association, Quincy, Mass Arthur EC, John RH, Pam P, Casey CG, Robert ES (2008) Codes and standards for the built environment. In: Arthur, E.C., Casey, C.G. (eds.) Fire protection handbook, vol. 1, 20th edn., pp. 1–51. National Fire Protection Association, Quincy, Mass
4.
go back to reference Norbert W. Young Jr, Stephen A. Jones HMB (2007) Interoperability in the construction industry, smartmarket report. Technical report, McGraw-Hill Construction Research and Analytics. www.construction.com Norbert W. Young Jr, Stephen A. Jones HMB (2007) Interoperability in the construction industry, smartmarket report. Technical report, McGraw-Hill Construction Research and Analytics. www.​construction.​com
7.
go back to reference Lee Jin K (2011) Building environment rule and analysis (BERA) language and its application for evaluating building circulation and spatial program. Universiteit Antwerpen, Belgium, Phd Lee Jin K (2011) Building environment rule and analysis (BERA) language and its application for evaluating building circulation and spatial program. Universiteit Antwerpen, Belgium, Phd
9.
go back to reference Hjelseth E, Nisbet N (2010) Exploring semantic based model checking. In: Proceedings of the CIB W78 2010: 27th International Conference, pp. 341–351 Hjelseth E, Nisbet N (2010) Exploring semantic based model checking. In: Proceedings of the CIB W78 2010: 27th International Conference, pp. 341–351
14.
go back to reference Solihin W, Shaikh N, Rong X, Lam K (2004) Beyond interoperatibility of building model: a case for code compliance checking. In: BP-CAD Workshop, Carnegie Melon University, pp. 1, 13 Solihin W, Shaikh N, Rong X, Lam K (2004) Beyond interoperatibility of building model: a case for code compliance checking. In: BP-CAD Workshop, Carnegie Melon University, pp. 1, 13
20.
go back to reference Bomba M (2020) Level Of Development (LOD) specification part I & commentary for building information models and data. BIM Forum, 15–19 Bomba M (2020) Level Of Development (LOD) specification part I & commentary for building information models and data. BIM Forum, 15–19
22.
go back to reference Châteauvieux-Hellwig C, Abualdenien J, Borrmann A (2020) Towards semantic enrichment of early-design timber models for noise and vibration analysis. ECPPM 2020:1–7 Châteauvieux-Hellwig C, Abualdenien J, Borrmann A (2020) Towards semantic enrichment of early-design timber models for noise and vibration analysis. ECPPM 2020:1–7
23.
go back to reference Bloch T (2022) Connecting research on semantic enrichment of bim-review of approaches, methods and possible applications. J Inform Technol Constr 27:416–440 Bloch T (2022) Connecting research on semantic enrichment of bim-review of approaches, methods and possible applications. J Inform Technol Constr 27:416–440
26.
go back to reference Flach PA, Kakas AC (2000). In: Flach PA, Kakas AC (eds) Abductive and inductive reasoning: background and issues. Springer, Dordrecht, pp 1–27 Flach PA, Kakas AC (2000). In: Flach PA, Kakas AC (eds) Abductive and inductive reasoning: background and issues. Springer, Dordrecht, pp 1–27
28.
29.
go back to reference Núnez-Calzado PE, Alarcón-López IJ, Martínez-Gómez DC (2018) Machine learning in bim. In: EUBIM 2018: Proceedings of the International BIM Conference, pp. 99–109 Núnez-Calzado PE, Alarcón-López IJ, Martínez-Gómez DC (2018) Machine learning in bim. In: EUBIM 2018: Proceedings of the International BIM Conference, pp. 99–109
30.
go back to reference Koo B, Jung R, Yu Y (2021) Automatic classification of wall and door bim element subtypes using 3d geometric deep neural networks. Adv Eng Inform 47:101200CrossRef Koo B, Jung R, Yu Y (2021) Automatic classification of wall and door bim element subtypes using 3d geometric deep neural networks. Adv Eng Inform 47:101200CrossRef
31.
go back to reference Collins FC, Braun A, Ringsquandl M, Hall DM, Borrmann A (2021) Assessing ifc classes with means of geometric deep learning on different graph encodings. In: Proc. of the 2021 European Conference on Computing in Construction, pp. 332–341 Collins FC, Braun A, Ringsquandl M, Hall DM, Borrmann A (2021) Assessing ifc classes with means of geometric deep learning on different graph encodings. In: Proc. of the 2021 European Conference on Computing in Construction, pp. 332–341
32.
go back to reference Kim J, Song J, Lee J (2019) Recognizing and classifying unknown object in bim using 2d cnn. In: Lee, J.-H. (ed.) Computer-Aided Architectural Design. “Hello, Culture”- 18th International Conference, CAAD Futures 2019, Selected Papers. Communications in Computer and Information Science, pp. 47–57. Springer, Germany. https://doi.org/10.1007/978-981-13-8410-3_4 Kim J, Song J, Lee J (2019) Recognizing and classifying unknown object in bim using 2d cnn. In: Lee, J.-H. (ed.) Computer-Aided Architectural Design. “Hello, Culture”- 18th International Conference, CAAD Futures 2019, Selected Papers. Communications in Computer and Information Science, pp. 47–57. Springer, Germany. https://​doi.​org/​10.​1007/​978-981-13-8410-3_​4
33.
go back to reference Luo H, Gao G, Huang H, Ke Z, Peng C, Gu M (2023) Automatic classification of wall and door bim element subtypes using 3d geometric deep neural networks. In: Karlinsky L, Michaeli T, Nishino K (eds) Computer Vision - ECCV 2022 Workshops. Springer, Cham, pp 349–365CrossRef Luo H, Gao G, Huang H, Ke Z, Peng C, Gu M (2023) Automatic classification of wall and door bim element subtypes using 3d geometric deep neural networks. In: Karlinsky L, Michaeli T, Nishino K (eds) Computer Vision - ECCV 2022 Workshops. Springer, Cham, pp 349–365CrossRef
34.
go back to reference Koo B, Jung R, Yu Y, Kim I (2021) A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models. J Comput Design Eng 8(1):239–250CrossRef Koo B, Jung R, Yu Y, Kim I (2021) A geometric deep learning approach for checking element-to-entity mappings in infrastructure building information models. J Comput Design Eng 8(1):239–250CrossRef
35.
go back to reference Bigdeli S, Pauwels P, Verstockt S, Weghe N, Merci B (2023) ML-based Exit identification. CodeOcean. Accessed 02(08):2023 Bigdeli S, Pauwels P, Verstockt S, Weghe N, Merci B (2023) ML-based Exit identification. CodeOcean. Accessed 02(08):2023
36.
go back to reference Zhang R, El-Gohary N (2020) A machine-learning approach for semantic matching of building codes and building information models (BIMs) for supporting automated code checking. In: Rodrigues H, Morcous G, Shehata M (eds) Recent Res Sustain Struct. Springer, Cham, pp 64–73CrossRef Zhang R, El-Gohary N (2020) A machine-learning approach for semantic matching of building codes and building information models (BIMs) for supporting automated code checking. In: Rodrigues H, Morcous G, Shehata M (eds) Recent Res Sustain Struct. Springer, Cham, pp 64–73CrossRef
39.
go back to reference Mohd Nawi N, Hussein A, Samsudin N, Hamid N, Mohd Yunus MA, Aziz MF (2017) The effect of pre processing techniques and optimal parameters selection on back propagation neural networks. Int J Adv Sci Eng Inform Technol 7:770CrossRef Mohd Nawi N, Hussein A, Samsudin N, Hamid N, Mohd Yunus MA, Aziz MF (2017) The effect of pre processing techniques and optimal parameters selection on back propagation neural networks. Int J Adv Sci Eng Inform Technol 7:770CrossRef
40.
go back to reference Ak D, Venugopalan SRD (2017) The effect of normalization on intrusion detection classifiers (naïve bayes and j48). Int J Future Revol Comput Sci Commun Eng 3:60–64 Ak D, Venugopalan SRD (2017) The effect of normalization on intrusion detection classifiers (naïve bayes and j48). Int J Future Revol Comput Sci Commun Eng 3:60–64
41.
go back to reference Hoste V (2005) Optimization issues in machine learning of coreference resolution. PhD thesis, Universiteit Antwerpen, Faculteit Letteren en Wijsbegeerte Hoste V (2005) Optimization issues in machine learning of coreference resolution. PhD thesis, Universiteit Antwerpen, Faculteit Letteren en Wijsbegeerte
44.
go back to reference Awad M, Khanna R (2015) Support vector machines for classification. Apress, Berkeley, CA, pp 39–66 Awad M, Khanna R (2015) Support vector machines for classification. Apress, Berkeley, CA, pp 39–66
45.
go back to reference Fawzy H, Rady EHA, Abdel Fattah AM (2020) Comparison between support vector machines and k-nearest neighbor for time series forecasting. J Math Comput Sci 10(6):2342–2359 Fawzy H, Rady EHA, Abdel Fattah AM (2020) Comparison between support vector machines and k-nearest neighbor for time series forecasting. J Math Comput Sci 10(6):2342–2359
47.
go back to reference Subhi Malallah H, Bahjat Abdulrazzaq M (2023) Web-based agricultural management products for marketing system: Survey. Academic J Nawroz Univ 12(2):49–62CrossRef Subhi Malallah H, Bahjat Abdulrazzaq M (2023) Web-based agricultural management products for marketing system: Survey. Academic J Nawroz Univ 12(2):49–62CrossRef
48.
go back to reference Witten IH, Frank E, Hall MA (2011) Data Mining: Practical Machine Learning Tools and Techniques. Third edition edn. The Morgan Kaufmann Series in Data Management Systems, Boston, pp 587–605 Witten IH, Frank E, Hall MA (2011) Data Mining: Practical Machine Learning Tools and Techniques. Third edition edn. The Morgan Kaufmann Series in Data Management Systems, Boston, pp 587–605
49.
go back to reference Caon DRS, Amehraye A, Razik J, Chollet G, Andreão RV, Mokbel C (2010) Experiments on acoustic model supervised adaptation and evaluation by K-Fold cross validation technique. In: 2010 5th International Symposium On I/V Communications and Mobile Network, pp. 1–4. https://doi.org/10.1109/ISVC.2010.5656264 Caon DRS, Amehraye A, Razik J, Chollet G, Andreão RV, Mokbel C (2010) Experiments on acoustic model supervised adaptation and evaluation by K-Fold cross validation technique. In: 2010 5th International Symposium On I/V Communications and Mobile Network, pp. 1–4. https://​doi.​org/​10.​1109/​ISVC.​2010.​5656264
50.
go back to reference Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Joint Conf Artif Intell Organ 14:1137–1143 Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Joint Conf Artif Intell Organ 14:1137–1143
53.
go back to reference Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357CrossRef Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321–357CrossRef
54.
go back to reference Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B (eds) Adv Intell Comput. Springer, Berlin, Heidelberg, pp 878–887CrossRef Han H, Wang W-Y, Mao B-H (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. In: Huang D-S, Zhang X-P, Huang G-B (eds) Adv Intell Comput. Springer, Berlin, Heidelberg, pp 878–887CrossRef
55.
go back to reference He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1322–1328 He H, Bai Y, Garcia EA, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN), pp. 1322–1328
56.
go back to reference Baru (2013) Prowsyn: Proximity weighted synthetic oversampling technique for imbalanced data set learning. In: Advances in Knowledge Discovery and Data Mining, pp. 317–328 Baru (2013) Prowsyn: Proximity weighted synthetic oversampling technique for imbalanced data set learning. In: Advances in Knowledge Discovery and Data Mining, pp. 317–328
58.
go back to reference Feng H, Hang L (2013) A novel boundary oversampling algorithm based on neighborhood rough set model: NRSBoundary-SMOTE. Mathematical Problems in Engineering, 10 Feng H, Hang L (2013) A novel boundary oversampling algorithm based on neighborhood rough set model: NRSBoundary-SMOTE. Mathematical Problems in Engineering, 10
59.
go back to reference Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-Level-SMOTE: Safe-Level-Synthetic minority over-sampling technique for handling the class imbalanced problem. In: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 475–482 Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2009) Safe-Level-SMOTE: Safe-Level-Synthetic minority over-sampling technique for handling the class imbalanced problem. In: Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 475–482
60.
go back to reference Gazzah S, Amara NEB (2008) New oversampling approaches based on polynomial fitting for imbalanced data sets. In: The Eighth IAPR International Workshop on Document Analysis Systems, pp. 677–684 Gazzah S, Amara NEB (2008) New oversampling approaches based on polynomial fitting for imbalanced data sets. In: The Eighth IAPR International Workshop on Document Analysis Systems, pp. 677–684
62.
go back to reference Rivera WA, Xanthopoulos P (2016) A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets. Expert Sys Appl 66:124–135CrossRef Rivera WA, Xanthopoulos P (2016) A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets. Expert Sys Appl 66:124–135CrossRef
64.
go back to reference Dang XT, Tran DH, Hirose O, Satou K (2015) SPY: A novel resampling method for improving classification performance in imbalanced data. In: 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), pp. 280–285 Dang XT, Tran DH, Hirose O, Satou K (2015) SPY: A novel resampling method for improving classification performance in imbalanced data. In: 2015 Seventh International Conference on Knowledge and Systems Engineering (KSE), pp. 280–285
65.
go back to reference Batista GEAPA, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor Newslett 6:20–29CrossRef Batista GEAPA, Prati RC, Monard MC (2004) A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor Newslett 6:20–29CrossRef
68.
go back to reference Cateni S, Colla V, Vannucci M (2011) Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems. In: 2011 11th International Conference on Intelligent Systems Design and Applications, pp. 402–407 Cateni S, Colla V, Vannucci M (2011) Novel resampling method for the classification of imbalanced datasets for industrial and other real-world problems. In: 2011 11th International Conference on Intelligent Systems Design and Applications, pp. 402–407
69.
go back to reference Sáez JA, Luengo J, Stefanowski J, Herrera F (2015) SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inform Sci 10:184–203CrossRef Sáez JA, Luengo J, Stefanowski J, Herrera F (2015) SMOTE-IPF: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inform Sci 10:184–203CrossRef
70.
go back to reference Hu J, He X, Yu D-J, Yang X-B, Yang J-Y, Shen H-B (2014) A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction. PLos ONE 10:1–10 Hu J, He X, Yu D-J, Yang X-B, Yang J-Y, Shen H-B (2014) A new supervised over-sampling algorithm with application to protein-nucleotide binding residue prediction. PLos ONE 10:1–10
71.
go back to reference Maciejewski T, Stefanowski J (2011) Local neighbourhood extension of SMOTE for mining imbalanced data. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 104–111 Maciejewski T, Stefanowski J (2011) Local neighbourhood extension of SMOTE for mining imbalanced data. In: 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), pp. 104–111
72.
go back to reference Koziarski M, Wozniak M (2017) Ccr: a combined cleaning and resampling algorithm for imbalanced data classification. Int J Appl Math Comput Sci 27:727–736MathSciNetCrossRef Koziarski M, Wozniak M (2017) Ccr: a combined cleaning and resampling algorithm for imbalanced data classification. Int J Appl Math Comput Sci 27:727–736MathSciNetCrossRef
73.
go back to reference Li J, Fong S, Zhuang Y (2015) Optimizing SMOTE by metaheuristics with neural network and decision tree. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 26–32 Li J, Fong S, Zhuang Y (2015) Optimizing SMOTE by metaheuristics with neural network and decision tree. In: 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), pp. 26–32
74.
go back to reference Cieslak DA, Chawla NV, Striegel A (2006) Combating imbalance in network intrusion datasets. In: 2006 IEEE International Conference on Granular Computing, pp. 732–737 Cieslak DA, Chawla NV, Striegel A (2006) Combating imbalance in network intrusion datasets. In: 2006 IEEE International Conference on Granular Computing, pp. 732–737
75.
go back to reference Kunakorntum I, Hinthong W, Phunchongharn P (2020) A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets. IEEE Access, 114692–114704 Kunakorntum I, Hinthong W, Phunchongharn P (2020) A synthetic minority based on probabilistic distribution (SyMProD) oversampling for imbalanced datasets. IEEE Access, 114692–114704
76.
go back to reference Calleja J, Fuentes O (2007) A distance-based over-sampling method for learning from imbalanced data sets. In: Proceedings of the Twentieth International Florida Artificial Intelligence, pp. 634–635 Calleja J, Fuentes O (2007) A distance-based over-sampling method for learning from imbalanced data sets. In: Proceedings of the Twentieth International Florida Artificial Intelligence, pp. 634–635
77.
go back to reference Puntumapon K, Waiyamai K (2012) A pruning-based approach for searching precise and generalized region for synthetic minority over-sampling. In: Tan P-N, Chawla S, Ho CK, Bailey J (eds) Adv Knowl Discov Data Min. Springer, Berlin, Heidelberg, pp 371–382CrossRef Puntumapon K, Waiyamai K (2012) A pruning-based approach for searching precise and generalized region for synthetic minority over-sampling. In: Tan P-N, Chawla S, Ho CK, Bailey J (eds) Adv Knowl Discov Data Min. Springer, Berlin, Heidelberg, pp 371–382CrossRef
78.
go back to reference Urban JL, Song J, Santamaria S, Fernandez-Pello C (2019) Ignition of a spot smolder in a moist fuel bed by a firebrand. Fire safety J 108:102833CrossRef Urban JL, Song J, Santamaria S, Fernandez-Pello C (2019) Ignition of a spot smolder in a moist fuel bed by a firebrand. Fire safety J 108:102833CrossRef
79.
go back to reference Borowska K, Stepaniuk J (2016) Imbalanced data classification: a novel re-sampling approach combining versatile improved SMOTE and rough sets. In: Saeed K, Homenda W (eds) Computer Information Systems and Industrial Management. Springer, Cham, pp 31–42CrossRef Borowska K, Stepaniuk J (2016) Imbalanced data classification: a novel re-sampling approach combining versatile improved SMOTE and rough sets. In: Saeed K, Homenda W (eds) Computer Information Systems and Industrial Management. Springer, Cham, pp 31–42CrossRef
80.
go back to reference Abdi L, Hashemi S (2016) To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Transactions on Knowledge and Data Engineering, 238–251 Abdi L, Hashemi S (2016) To combat multi-class imbalanced problems by means of over-sampling techniques. IEEE Transactions on Knowledge and Data Engineering, 238–251
81.
go back to reference Barua S, Islam MM, Yao X, Murase K (2014) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26:405–425CrossRef Barua S, Islam MM, Yao X, Murase K (2014) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26:405–425CrossRef
83.
go back to reference Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D (2020) Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest. Biocybern Biomed Eng 40(1):352–362CrossRef Peng J, Hao D, Yang L, Du M, Song X, Jiang H, Zhang Y, Zheng D (2020) Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random forest. Biocybern Biomed Eng 40(1):352–362CrossRef
85.
86.
go back to reference Idowu IO, Fergus P, Hussain A, Dobbins C, Khalaf M, Casana Eslava RV, Keight R (2015) Artificial intelligence for detecting preterm uterine activity in gynecology and obstetric care. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 215–220. https://doi.org/10.1109/CIT/IUCC/DASC/PICOM.2015.31 Idowu IO, Fergus P, Hussain A, Dobbins C, Khalaf M, Casana Eslava RV, Keight R (2015) Artificial intelligence for detecting preterm uterine activity in gynecology and obstetric care. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 215–220. https://​doi.​org/​10.​1109/​CIT/​IUCC/​DASC/​PICOM.​2015.​31
88.
go back to reference Vandewiele G, Dehaene I, Kovács G, Sterckx L, Janssens O, Ongenae F, De Backere F, De Turck F, Roelens K, Decruyenaere J, Van Hoecke S, Demeester T (2021) Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling. Artif Intell Med 111:101987. https://doi.org/10.1016/j.artmed.2020.101987CrossRef Vandewiele G, Dehaene I, Kovács G, Sterckx L, Janssens O, Ongenae F, De Backere F, De Turck F, Roelens K, Decruyenaere J, Van Hoecke S, Demeester T (2021) Overly optimistic prediction results on imbalanced data: a case study of flaws and benefits when applying over-sampling. Artif Intell Med 111:101987. https://​doi.​org/​10.​1016/​j.​artmed.​2020.​101987CrossRef
91.
go back to reference Berrar D (2019) Cross-validation. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C (eds) Encyclopedia of Bioinformatics and Computational Biology. Academic Press, Oxford, pp 542–545CrossRef Berrar D (2019) Cross-validation. In: Ranganathan S, Gribskov M, Nakai K, Schönbach C (eds) Encyclopedia of Bioinformatics and Computational Biology. Academic Press, Oxford, pp 542–545CrossRef
93.
go back to reference Komorniczak J, Ksieniewicz P (2023) problexity-an open-source python library for supervised learning problem complexity assessment. Neurocomputing 521:126–136CrossRef Komorniczak J, Ksieniewicz P (2023) problexity-an open-source python library for supervised learning problem complexity assessment. Neurocomputing 521:126–136CrossRef
Metadata
Title
Semantic Enrichment of a BIM Model Using Revit: Automatic Annotation of Doors in High-Rise Residential Building Models Using Machine Learning
Authors
Soheila Bigdeli
Pieter Pauwels
Steven Verstockt
Nico Van de Weghe
Bart Merci
Publication date
08-10-2024
Publisher
Springer US
Published in
Fire Technology
Print ISSN: 0015-2684
Electronic ISSN: 1572-8099
DOI
https://doi.org/10.1007/s10694-024-01655-0