Systematic Review of 5D BIM Implementation in Construction Projects

Authors

  • Irika Widiasanti Building Construction Engineering Technology, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia
  • Yusron Fikri Building Construction Engineering Technology, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia
  • Moh Akbar Wibisono Building Construction Engineering Technology, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia
  • Anton Wijaya Building Construction Engineering Technology, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia
  • Nadilla Hikmatul Hasanah Building Construction Engineering Technology, Faculty of Engineering, Universitas Negeri Jakarta, Indonesia

DOI:

https://doi.org/10.51601/ijse.v6i1.371

Abstract

integration between scheduling (4D) and cost estimation (5D). The literature search focused on peer-reviewed articles and supporting academic sources published between 2019 and 2025, yielding a set of relevant studies. The analyzed data include project types, software used (Revit, Navisworks/Synchro, CostX), data exchange formats (IFC/CSV), and the relationship among the Work Breakdown Structure (WBS), Quantity Take-Off (QTO), and cost account code (COA). The synthesis shows that 5D BIM improves cost accuracy through model-linked QTO, accelerates estimation workflows, and shortens the time required to identify the impacts of design changes on both cost and schedule. The main obstacles to 5D BIM adoption include the loss of information attributes during cross-software data exchanges, inconsistencies in WBS/COA standards across project stakeholders, and limitations in user competency. Supporting factors for effective implementation include an open BIM approach, a Common Data Environment (CDE), standardized cost libraries, and structured training governance. The study also highlights replicable practices such as early WBS alignment for 4D–5D synchronization, rule-based QTO validation, and linking work progress to payment mechanisms. These findings are expected to contribute to the development of 5D BIM guidelines and educational curricula, particularly for small- to medium-scale contractors.

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References

[1] P. Pishdad and I. O. Onungwa, “Analysis of 5D BIM for cost estimation, cost control and payments,” J. Inf. Technol. Constr., vol. 29, pp. 525–548, Jul. 2024, doi: 10.36680/j.itcon.2024.024.

[2] L. Inzerillo, F. Acuto, A. Pisciotta, K. Mantalovas, and G. Di Mino, “xploring 4D and 5D analysis in BIM environment for infrastructures: A case study,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLVIII-2/W, pp. 233–240, Feb. 2024, doi: 10.5194/isprs-archives-XLVIII-2-W4-2024-233-2024.

[3] O. A. I. Hussain, R. C. Moehler, S. D. C. Walsh, and D. D. Ahiaga-Dagbui, “Minimizing Cost Overrun in Rail Projects through 5D-BIM: A Conceptual Governance Framework,” Buildings, vol. 14, no. 2, p. 478, Feb. 2024, doi: 10.3390/buildings14020478.

[4] O. A. I. Hussain, R. C. Moehler, S. D. C. Walsh, and D. D. Ahiaga-Dagbui, “Minimizing Cost Overrun in Rail Projects through 5D-BIM: A Systematic Literature Review,” Infrastructures, vol. 8, no. 5, p. 93, May 2023.

[5] A. Rashidi, D. W. M. Chan, M. Ravanshadnia, H. Sarvari, and A. Tajaddini, “Applying Building Information Modelling (BIM) Technology in Pre-Tender Cost Estimation of Construction Projects: A Case Study in Iran,” Buildings, vol. 14, no. 5, p. 1260, Apr. 2024, doi: 10.3390/buildings14051260.

[6] D. Park and S. Yun, “Construction Cost Prediction Using Deep Learning with BIM Properties in the Schematic Design Phase,” Appl. Sci., vol. 13, no. 12, p. 7207, Jun. 2023, doi: 10.3390/app13127207.

[7] K. S. Al-Gahtani, N. M. Alsanabani, A. M. Alsugair, S. I. Aljadhai, and H. F. Alotaibi, “Dynamic BIM Adoption Impact on Contract Cost Variance Factors Using PLS-SEM Techniques,” Appl. Sci., vol. 14, no. 17, p. 8017, Sep. 2024, doi: 10.3390/app14178017.

[8] J. Sun, R. Yi Man Li, and J. Deeprasert, “The Impact of BIM Technology on the Lifecycle Cost Control of Prefabricated Buildings: Evidence from China,” Buildings, vol. 14, no. 12, p. 3709, Nov. 2024, doi: 10.3390/buildings14123709.

[9] A. Gouda Mohamed, F. K. Alqahtani, E. R. Ismail, and M. Nabawy, “Synergizing BIM and Value Engineering in the Construction of Residential Projects: A Novel Integration Framework,” Buildings, vol. 14, no. 8, p. 2515, Aug. 2024, doi: 10.3390/buildings14082515.

[10] C. A. Prastya, I. Hendriyani, and R. Pratiwi, “Implementasi Building Information Modelling (BIM) 5D Pada Estimasi Volume dan Biaya Pekerjaan Struktur,” BANDAR J. Civ. Eng., vol. 7, no. 1, pp. 17–24, 2025, doi: https://doi.org/10.31605/bjce.v7i1.4301.

[11] K. Lu, X. Deng, X. Jiang, B. Cheng, and V. W. Y. Tam, “A review on life cycle cost analysis of buildings based on building information modeling,” J. Civ. Eng. Manag., vol. 29, no. 3, pp. 268–288, Feb. 2023, doi: 10.3846/jcem.2023.18473.

[12] H. Almujibah, “Assessment of Building Information Modeling (BIM) as a Time and Cost-Saving Construction Management Tool: Evidence from Two-Story Villas in Jeddah,” Sustainability, vol. 15, no. 9, p. 7354, Apr. 2023, doi: 10.3390/su15097354.

[13] S. Banihashemi, S. Khalili, M. Sheikhkhoshkar, and A. Fazeli, “Machine learning-integrated 5D BIM informatics: building materials costs data classification and prototype development,” Innov. Infrastruct. Solut., vol. 7, no. 3, p. 215, Jun. 2022, doi: 10.1007/s41062-022-00822-y.

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Published

2026-02-05

How to Cite

Widiasanti, I., Fikri, Y., Wibisono, M. A., Wijaya, A., & Hasanah, N. H. (2026). Systematic Review of 5D BIM Implementation in Construction Projects . International Journal of Science and Environment (IJSE), 6(1), 692–697. https://doi.org/10.51601/ijse.v6i1.371