Open Access
Int. J. Simul. Multidisci. Des. Optim.
Volume 14, 2023
Article Number 5
Number of page(s) 8
Published online 28 June 2023
  1. F.M. Dinis, J. Poças Martins, A.S. Guimarães et al., BIM and semantic enrichment methods and applications: a review of recent developments, Arch. Computat. Methods Eng. 29, 879–895 (2022) [CrossRef] [Google Scholar]
  2. H.N. Rafsanjani, A.H. Nabizadeh, Towards digital architecture, engineering, and construction (AEC) industry through virtual design and construction (VDC) and digital twin, Energy Built. Environ. 4, 169–178 (2023) [Google Scholar]
  3. R. Sacks, M. Girolami, I. Brilakis, Building information modelling, artificial intelligence and construction tech, Dev. Built Environ. 4, 100011 (2020) [CrossRef] [Google Scholar]
  4. C. Musella, M. Serra, C. Menna, D. Asprone, BIM & AI: advanced technologies for the digitalisation of seismic damages in masonry buildings (2019) Available at the_digitalisation_of_seismic_damages_in_masonry_buildings [Google Scholar]
  5. Т.Н. Костюнина, Технологии искусственного интеллектавзадачах BIM[C]//BIM-моделирование в задачах строительства и архитектуры. 80–85 (2019) [Google Scholar]
  6. C. Lu, J. Liu, Y. Liu, Y. Liu, Intelligent construction technology of railway engineering in China, Front Eng. Manag. 6, 503–516 (2019) [Google Scholar]
  7. J.W. Ouellette, BIM Tomorrow: BIM for Design Firms (1st ed.) (2019), pp. 175–202. [Google Scholar]
  8. F.L. Rossini, Integration between BIM and agent-based simulation for the 4.0 detailed design, TECHNE-J. Technol. Archit. Environ. 18, 282–287 (2019) [Google Scholar]
  9. K. Hussaina, M.N.M. Salleha, S. Talpura et al., Big data and machine learning in construction: a review, Int. J. Soft. Comput. Metaheurist. (2018) [Google Scholar]
  10. V. Qiuchen Lu, A.K. Parlikad, P. Woodall et al., Developing a dynamic digital twin at a building level: Using Cambridge campus as case study, in International Conference on Smart Infrastructure and Construction 2019 (ICSIC) Driving data-informed decision-making (ICE Publishing, 2019), pp. 67–75 [CrossRef] [Google Scholar]
  11. F. Brunone, M. Cucuzza, M. Imperadori et al., From cognitive buildings to digital twin: the frontier of digitalization for the management of the built environment, Wood Addit. Technolog. 81–95 (2021) [CrossRef] [Google Scholar]
  12. Q. Lu, X. Xie, J. Heaton, A.K. Parlikad, J. Schooling, From BIM towards digital twin: strategy and future development for smart asset management, in: Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future. SOHOMA 2019. Studies in Computational Intelligence, edited by T. Borangiu, D. Trentesaux, P. Leitão,A. Giret Boggino, V. Botti (Springer, Cham, 2019), vol 853 [Google Scholar]
  13. R. Sacks, I. Brilakis, E. Pikas et al., Construction with digital twin information systems, Data-Centric Eng. 1, e14 (2020) [CrossRef] [Google Scholar]
  14. B. Daniotti, A. Pavan, S. Lupica Spagnolo et al., Collaborative working in a BIM environment (BIM platform), BIM-Based Collaborative Building Process Management (2020), pp. 71–102 [CrossRef] [Google Scholar]
  15. C.H. Chang, C.Y. Lin, R.G. Wang et al., Applying deep learning and building information modeling to indoor positioning based on sound, Computing in Civil Engineering 2019 Visualization, Information Modeling, and Simulation (American Society of Civil Engineers, Reston, VA, 2019), pp. 193–199 [Google Scholar]
  16. M. Locatelli, E. Seghezzi, G.M. Di Giuda, Exploring BIM and NLP applications: a scientometric approach, Proc. Int. Struct. Eng. Constr. 8 (2021) [Google Scholar]
  17. T. Kaddoura, Unlocking the full potential of BIM with artificial intelligence, Metabuild (2019) [Google Scholar]
  18. V. Krausková, H. Pifko, Use of artificial intelligence in the field of sustainable architecture: current knowledge, A.L.F.A. 26, 20–29 (2021) [Google Scholar]
  19. C.D. Thiele, J. Brötzmann, T.J. Huyeng et al., A Digital Twin as a framework for a machine learning based predictive maintenance system, in ECPPM 2021 -eWork and eBusiness in Architecture, Engineering and Construction (CRC Press, 2021), pp. 313–319 [Google Scholar]
  20. Z. Hamid, I. Musirin, M.N.A. Rahim et al., Optimization assisted load tracing via hybrid ant colony algorithm for deregulated power system, WSEAS Trans. Power Syst. 7, 145–158 (2012) [Google Scholar]
  21. Y. Bao, H. Li, Artificial intelligence for civil engineering, Tumu Gongcheng Xuebao China Civ. Eng. J. 52, 1–11 (2019) [Google Scholar]
  22. M.S. Orooje, M.M. Latifi, A review of embedding artificial intelligence in internet of things and building information modelling for healthcare facility maintenance management, Energy Environ. Res. 11 (2021) [Google Scholar]
  23. E.A. Petrova, AI for BIM-based sustainable building design: Integrating knowledge discovery and semantic data modelling for evidence-based design decision support (2019) [Google Scholar]
  24. D. Adio-Moses, O.S. Asaolu, Artificial intelligence for sustainable development of intelligent buildings, in Proceedings of the 9th CIDB Postgraduate Conference, At University of Cape Town, South Africa (2016) [Google Scholar]
  25. M. Mathews, B. Bowe, D. Robles, BIM+Blockchain: A Solution to the Trust Problem in Collaboration? Enhanced Reader[C]. CITA BIM Gather 2017 (2017) [Google Scholar]
  26. M. Juszczyk, Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings' floor structural frames, AIP Conf. Proc. 1946, 020014 (2018) [CrossRef] [Google Scholar]
  27. J. Heaton, A.K. Parlikad, Asset information model to support the adoption of a digital twin: West Cambridge case study, IFAC-PapersOnLine 53, 366–371 (2020) [CrossRef] [Google Scholar]
  28. K. Myers, How artificial intelligence is improving the efficiency of BIM. The Planning, BIM & Construction Today (2020). Available at [Google Scholar]
  29. S. Rafiu, B.E. Young, C. Jamie et al., Innovative changes in quantity surveying practice through BIM, big data, artificial intelligence and machine learning, J. Appl. Sci. Univ. 4, 37–47 (2020) [Google Scholar]
  30. N. Yabuki, Applications of AI, BIM, and sensing to bridge maintenance, in Bridge Maintenance, Safety, Management, Life-Cycle Sustainability and Innovations − Proceedings of the 10th International Conference on Bridge Maintenaince, Safety and Management, IABMAS 2020 (2021) [Google Scholar]
  31. Z. Wang, B. He, Y. Yang, C. Shen, F. Peña-Mora, Building a next generation AI platform for AEC: a review and research challenges, in Proc. 37th CIB W78 Information Technology for Construction Conference (CIB W78), São Paulo, Brazil (2020), pp. 27–45 [Google Scholar]
  32. J. Sresakoolchai, S. Kaewunruen, Integration of building information modeling (BIM) and artificial intelligence (AI) to detect combined defects of infrastructure in the railway system, in Resilient Infrastructure, Lecture Notes in Civil Engineering, edited by S. Kolathayar, C. Ghosh, B.R. Adhikari, I. Pal, A. Mondal, Springer, Singapore (2022), p. 202 [Google Scholar]
  33. X. Peng, X. Zhong, C. Zhao et al., A UAV-based machine vision method for bridge crack recognition and width quantification through hybrid feature learning, Constr. Build. Mater. 299, 123896 (2021) [CrossRef] [Google Scholar]
  34. Z. Yan, S. Teng, W. Luo et al., Bridge modal parameter identification from UAV measurement based on empirical mode decomposition and fourier transform, Appl. Sci. 12, 8689 (2022) [CrossRef] [Google Scholar]
  35. B.F. Spencer, V. Hoskere, Y. Narazaki, Advances in computer vision-based civil infrastructure inspection and monitoring, Engineering 5, 199–222 (2019) [Google Scholar]
  36. J. Zhang, Z. Teng, X. Xu, G. Chen, D. Bassir, Sensitivity analyses of structural damage indicators and ‎experimental validations, J. Appl. Comput. Mech. 7, 798–810 (2021) [Google Scholar]
  37. A. Malekloo, E. Ozer, M. AlHamaydeh, M. Girolami, Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights, Struct. Health Monitor. 21, 1906–1955 (2022) [CrossRef] [Google Scholar]
  38. Z. Yan, Z. Jin, S. Teng, G. Chen, D. Bassir, Measurement of bridge vibration by UAVs combined with CNN and KLT optical-flow method, Appl. Sci. 12, 5181 (2022) [CrossRef] [Google Scholar]
  39. L. Dackermann, D. Ulrike, X. Youlin, B. Samali, Damage identification in civil engineering structures utilising PCA-compressed residual frequency response functions and neural network ensembles, Struct. Control Health Monitor. 18, 207–226 (2015) [Google Scholar]
  40. M. Lin, S. Teng, G. Chen, D. Bassir, Transfer learning with attributes for improving the landslide spatial prediction performance in sample-scarce area based on variational autoencoder generative adversarial network, Land 12, 525 (2023) [CrossRef] [Google Scholar]
  41. G. Chen, Z. Yan, S. Teng, F. Cui, D. Bassir, A bridge vibration measurement method by UAVs based on CNNs ‎and bayesian optimization, J. Appl. Comput. Mech. 9, 1–14 (2023) [Google Scholar]
  42. G. Chen, X. Chen, L. Yang, Z. Han, D. Bassir, An inversion algorithm for the dynamic modulus of concrete pavement structures based on a convolutional neural network, Appl. Sci. 13, 1192 (2023) [CrossRef] [Google Scholar]
  43. S. Teng, X. Chen, G. Chen, L. Cheng, D. Bassir, Structural damage detection based on convolutional neural networks and population of bridges, Measurement 202, 111747 (2022) [CrossRef] [Google Scholar]
  44. S. Howell, Y. Rezgui, Beyond BIM − knowledge management for a smarter Future, IHS Markit (2018) [Google Scholar]
  45. M. Wang, C.C. Wang, S. Sepasgozar, S. Zlatanova, A systematic review of digital technology adoption in off-site construction: current status and future direction towards industry 4.0, Buildings 10, 204 (2020) [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.