AI–ENHANCED BUILDING INFORMATION MODELING: A STATE-OF-THE-ART REVIEW IN THE ARTIFICIAL INTELLIGENCE ERA

Authors

  • Ioana-Roxana VIZITIU BACIU
  • Cristina Liliana VLADOIU
  • Sebastian George MAXINEASA

Keywords:

BIM; artificial intelligence; generative design; digital twin; smart construction; engineering education.

Abstract

Building Information Modelling (BIM) has become an essential digital approach in modern engineering, helping professionals create integrated, data-rich representations of the built environment. In recent years, rapid advances in artificial intelligence (AI) have further expanded BIM’s capabilities, turning it from a modelling tool into a valuable decision-support system. AI-enhanced BIM can assist with design optimization, predictive analysis, clash detection, maintenance assurance, and connections to digital twin technologies. These developments influence engineering design, construction management, and structure/building services lifecycle planning. This paper reviews current research on BIM–AI integration in practice and education, highlighting key technological trends and analysing its implications for engineering curricula.

Downloads

Download data is not yet available.

References

Eastman, C., Teicholz, P., Sacks, R., Liston, K.

(2018). BIM Handbook: A Guide to Building

Information Modeling for Owners, Designers,

Engineers and Contractors, 3rd ed., Wiley, ISBN

, Hoboken, NJ.

Baciu, I.-R., Pruna, L., Slonovschi, A., Maxineasa,

S.G. (2024) The Transformative Impact of Advanced

Graphics Technologies and Computer-Generated

Imagery in Civil Engineering, JIDEG, Vol. 19, No. 1.

Zhang, J., El-Gohary, N. (2020). Automated rule

checking in BIM-based construction projects.

Automation in Construction, Vol. 113, (May 2020)

pp. 103130–103142, ISSN 0926-5805.

Turrin, M., von Buelow, P., Stouffs, R. (2011).

Design explorations of performance-driven geometry

in architectural design using parametric modelling

and genetic algorithms. Automation in Construction,

Vol. 20, No. 5, (July 2011) pp. 656–675, ISSN 0926

Bolpagni, M., Ciribini, A. (2016). The evolution of

BIM towards digital construction environments.

Journal of Information Technology in Construction,

Vol. 21, (December 2016) pp. 516–534, ISSN 1874

Onatayo, D., Garcia, A., Smith, J., Patel, R. (2024).

Generative

engineering

AI applications in architecture,

and construction: Trends and

implications for practice. Architecture, Vol. 4, No. 4,

(October 2024) pp. 877–902, ISSN 2673-8945.

Ma, Y., Siau, K., Wang, W. (2022). Artificial

intelligence in engineering education: A review of

emerging technologies. Computer Applications in

Engineering Education, Vol. 30, No. 1, (January

pp. 1–15, ISSN 1061-3773.

Tao, F., Qi, Q., Liu, A., Kusiak, A. (2019). Digital

twin-driven smart manufacturing. Journal of

Manufacturing Systems, Vol. 48, (January 2019) pp.

–169, ISSN 0278-6125.

Lu, Q., Parlikad, A., Woodall, P. (2020). Digital

twin-enabled asset management: Framework and

applications. Automation in Construction, Vol. 115,

(July 2020) pp. 103223–103235, ISSN 0926-5805.

Pruna L., Slonovschi A. (2017) Building a Pergola

in CAD Systems, JIDEG, Vol. 12, No. 1.

Luckin, R., Holmes, W., Griffiths, M., Forcier, L.

(2016). Intelligence Unleashed: An Argument for

Artificial Intelligence in Education, UCL Press, ISBN

, London.

Wang, S., Li, H., Chen, Y. (2024). Artificial

intelligence in education: A systematic literature

review. Expert Systems with Applications, Vol. 237,

(March 2024) pp. 121334–121350, ISSN 0957-4174.

Downloads

Published

2026-06-08

Issue

Section

Engineering Computer Graphics