3D-Based building damage assessment and visualization for sustainable maintenance planning: A case study of the Universitas Muhammadiyah Sukabumi rectorate building

Nia Kartika; Pratikso; Henny Pratiwi Adi

doi:10.58524/jasme.v6i4.1610

Authors

Nia Kartika Universitas Muhammadiyah Sukabumi | Universitas Islam Sultan Agung Semarang, Indonesia
Pratikso Universitas Islam Sultan Agung, Indonesia
Henny Pratiwi Adi Universitas Islam Sultan Agung, Indonesia

DOI:

https://doi.org/10.58524/jasme.v6i4.1610

Keywords:

Building Damage Assessment, 3D Modeling, Damage Visualization, Handling Cost Estimation

Abstract

Background: Effective building maintenance requires systematic damage assessment and integrated condition information to support informed decisions and sustainable infrastructure management. However, conventional inspection reports often lack spatial visualization, limiting the identification and management of damaged building components.

Aims: This study aims to assess building damage and develop a three-dimensional (3D) visualization model integrating damage characteristics, severity classifications, repair recommendations, and cost estimates to support sustainable maintenance planning.

Method: A descriptive case study was conducted at the Universitas Muhammadiyah Sukabumi Rectorate Building through regulatory reviews, technical documentation, field inspections, damage severity analysis, and 3D modeling using Autodesk Revit 2025.

Results: The assessment identified 108 damage instances involving architectural components, comprising 77 minor, 19 moderate, and 12 severe cases. The overall building damage level was 11.01%, indicating minor damage. The developed 3D model linked damaged components with spatial locations, damage dimensions, severity levels, repair recommendations, and cost estimates. The total estimated repair cost reached IDR 149,175,912.

Conclusion: Integrating damage assessment with 3D visualization facilitates structured damage documentation, component identification, and maintenance prioritization, providing a practical foundation for more systematic and potentially resource-efficient building maintenance planning.

References

Abideen, D. K., Yunusa-Kaltungo, A., Manu, P., & Cheung, C. (2022). A Systematic Review of the Extent to Which BIM Is Integrated into Operation and Maintenance. In Sustainability (Switzerland) (Vol. 14, Number 14). MDPI. https://doi.org/10.3390/su14148692

Abuhussain, M. A., Waqar, A., Khan, A. M., Othman, I., Alotaibi, B. S., Althoey, F., & Abuhussain, M. (2024). Integrating Building Information Modeling (BIM) for optimal lifecycle management of complex structures. Structures, 60, 105831. https://doi.org/10.1016/j.istruc.2023.105831

Adamopoulos, E., & Rinaudo, F. (2021). Close-Range Sensing and Data Fusion for Built Heritage Inspection and Monitoring-A Review. Remote Sensing, 13(19), 3936. https://doi.org/10.3390/rs13193936

Alavi, H., Bortolini, R., & Forcada, N. (2022). BIM-based decision support for building condition assessment. Automation in Construction, 135. https://doi.org/10.1016/j.autcon.2021.104117

Artus, M., Alabassy, M. S. H., & Koch, C. (2022). A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC. Applied Sciences (Switzerland), 12(6). https://doi.org/10.3390/app12062772

Azhar, S. (2011). Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadership and Management in Engineering, 11(3). https://doi.org/10.1061/(ASCE

Cardinal, S. M., Tosin, C. E., Pilz, S. E., & Costella, M. F. (2020). Inspection Method for Building Maintenance Management in Higher Education Institutions. Journal of Performance of Constructed Facilities, 34(6), 04020112. https://doi.org/10.1061/(ASCE

Chew, M. Y. L., & Gan, V. J. L. (2022). Long-Standing Themes and Future Prospects for the Inspection and Maintenance of Façade Falling Objects from Tall Buildings. Sensors, 22(16), 6070. https://doi.org/10.3390/s22166070

Dahuri, M., Che-Ani, A. I., Johar, S., Talib, O., Mokhatar, S. N., Abd Wahab, M. A., & Shamsuddin, M. (2025). Optimizing maintenance budget allocation in higher educational institutions: A systematic review of building condition assessment and service quality. Journal of Facilities Management, 24(2), 245–270. https://doi.org/10.1108/JFM-09-2024-0112

De Fino, M., Galantucci, R. A., & Fatiguso, F. (2023). Condition Assessment of Heritage Buildings via Photogrammetry: A Scoping Review from the Perspective of Decision Makers. Heritage, 6(11), 7031–7066. https://doi.org/10.3390/heritage6110367

Faqih, F., & Zayed, T. (2021). Defect-based building condition assessment. Building and Environment, 191, 107575. https://doi.org/10.1016/j.buildenv.2020.107575

Ferreira, C., Silva, A., de Brito, J., Dias, I. S., & Flores-Colen, I. (2021). The impact of imperfect maintenance actions on the degradation of buildings' envelope components. Journal of Building Engineering, 33, 101571. https://doi.org/10.1016/j.jobe.2020.101571

Hong, Z., Zhang, H., Tong, X., Liu, S., Zhou, R., Pan, H., Zhang, Y., Han, Y., Wang, J., & Yang, S. (2024). Rapid Fine-Grained Damage Assessment of Buildings on a Large Scale: A Case Study of the February 2023 Earthquake in Turkey. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17, 5204–5220. https://doi.org/10.1109/JSTARS.2024.3362809

Jalaei, F., Gourabpasi, A. H., Fazeli, A., & Jrade, A. (n.d.). Automated Quantity Takeoff And Life-Cycle Costing In Construction Projects Using An Openbim-Ifc Approach.

Lhara, O., Basyaruddin, S., & Khasanah, U. (2024). Building Maintenance Priority Decision Support System Using the Method Profile Matching. 13(1), 125–137. https://doi.org/10.32832/astonjadro.v13i1

Li, T., Li, Z., Teng, Y., & Tiong, R. L. K. (2026). An integrated framework for preventive maintenance planning of residential building cladding using degradation prediction and multi-objective optimization. Energy and Buildings, 362, 117478. https://doi.org/10.1016/j.enbuild.2026.117478

Liu, Z., Gu, X., Dong, Q., Tu, S., & Li, S. (2021). 3D Visualization of Airport Pavement Quality Based on BIM and WebGL Integration. Journal of Transportation Engineering, Part B: Pavements, 147(3), 04021024. https://doi.org/10.1061/JPEODX.0000280

Lupășteanu, V., Lupășteanu, R., & Chingălată, C. (2022). Condition assessment of buildings in Romania: A proposed method and case study. Journal of Building Engineering, 47, 103814. https://doi.org/10.1016/j.jobe.2021.103814

Matos, R., Rodrigues, F., Rodrigues, H., & Costa, A. (2021). Building condition assessment supported by Building Information Modelling. Journal of Building Engineering, 38, 102186. https://doi.org/10.1016/j.jobe.2021.102186

Nageeb, A., Elyamany, A., Elbehairy, H., & Alhady, A. (2026). A BIM-Integrated Eco-Digital Framework for Markov-Based Predictive Maintenance and Sustainability Assessment in Educational Buildings Towards Digital Twin Readiness. Sustainability, 18(14), 7133. https://doi.org/10.3390/su18147133

Nazri, S. F. A. M., Hashim, K. S. H.-Y., & Suhaimi, M. S. N. M. (2025). Building Condition Assessment For School Building. PLANNING MALAYSIA, 23. https://doi.org/10.21837/pm.v23i35.1692

Pan, X., Yang, T. T. Y., Li, J., Ventura, C., Málaga-Chuquitaype, C., Li, C., Su, R. K. L., & Brzev, S. (2025). A review of recent advances in data-driven computer vision methods for structural damage evaluation: algorithms, applications, challenges, and future opportunities. In Archives of Computational Methods in Engineering (Vol. 32, Number 7, pp. 4587–4619). Springer Science and Business Media B.V. https://doi.org/10.1007/s11831-025-10279-8

Perancangan, M., Bangunan, P., Jojok, … (, Soetjipto, W., Zarkasi, I. K., Trisiana, A., Soetjipto, J. W., & Kahfi Zarkasi, I. (n.d.). Model Perancangan Pemeliharaan Bangunan Gedung Menggunakan Building Information Modeling (BIM) Model of Building Maintenance Planning Using The Building Information Modeling (BIM).

Pereira, C., & Silva, A. (2024). Operationalisation of Building Inspections and Repair: Systematisation-Based Approach. Applied Sciences (Switzerland), 14(16). https://doi.org/10.3390/app14166947

Prusov, D., Makatora, D., & Kubanov, R. (2024). Methodological basis of BIM-analysis of damage and assessment of impacts, consequences, resources for restoration of buildings and structures. Strength of Materials and Theory of Structures, (112), 302–315. https://doi.org/10.32347/2410-2547.2024.112.302-315

Rapiński, J., Bednarczyk, M., Tomaszewski, D., Skotnicka-Siepsiak, A., Templin, T., Zabielski, J., Royano, V., & Serrat, C. (2025). Digital Transformation of Building Inspections: A Function-Oriented and Predictive Approach Using the FastFoam System. Infrastructures, 10(11). https://doi.org/10.3390/infrastructures10110310

Sanrı Karapınar, I., Özsoy Özbay, A. E., & Karadağ, B. (2026). BIM-based seismic damage and repair cost estimation of a reinforced concrete building. Australian Journal of Structural Engineering, 27(4), 425–438. https://doi.org/10.1080/13287982.2025.2593433

Sjölander, A., Belloni, V., Ansell, A., & Nordström, E. (2023). Towards Automated Inspections of Tunnels: A Review of Optical Inspections and Autonomous Assessment of Concrete Tunnel Linings. Sensors, 23(6), 3189. https://doi.org/10.3390/s23063189

Su, Y. C., Lee, Y. C., & Lin, Y. C. (2011). Enhancing maintenance management using building information modeling in facilities management. Proceedings of the 28th International Symposium on Automation and Robotics in Construction, ISARC 2011. https://doi.org/10.22260/isarc2011/0140

Villa, V., Bruno, G., Aliev, K., Piantanida, P., Corneli, A., & Antonelli, D. (2022). Machine Learning Framework for the Sustainable Maintenance of Building Facilities. Sustainability, 14(2), 681. https://doi.org/10.3390/su14020681

Wardach, M., Pawłowicz, J. A., Kosior-Kazberuk, M., & Krentowski, J. R. (2023). The Diagnostics of the Condition and Management of Large-Panel Buildings Using Point Clouds and Building Information Modelling (BIM). Buildings, 13(8). https://doi.org/10.3390/buildings13082089

Zhang, L., & Pan, Y. (2022). Information fusion for automated post-disaster building damage evaluation using deep neural network. Sustainable Cities and Society, 77, 103574. https://doi.org/10.1016/j.scs.2021.103574

Zheng, Z., Zhong, Y., Wang, J., Ma, A., & Zhang, L. (2021). Building damage assessment for rapid disaster response with a deep object-based semantic change detection framework: From natural disasters to man-made disasters. Remote Sensing of Environment, 265, 112636. https://doi.org/10.1016/j.rse.2021.112636