A Proposed UAV-BIM-Integrated Earned Value Management Framework with a Climate-Adjusted Schedule Performance Index for High-Rise Construction Project Control: A Conceptual Framework and Illustrative Demonstration
DOI:
https://doi.org/10.54536/ajcec.v2i2.8387Keywords:
Building Information Modeling, Climate Resilience, Earned Value Management, Integrated Digital Construction Index, UavAbstract
Earned Value Management (EVM) is the dominant method for integrated cost and schedule control in construction, yet its Schedule Performance Index (SPI) does not distinguish delay caused by contractor inefficiency from delay caused by unavoidable climate hazards such as monsoon rainfall or cyclones. In climate-vulnerable, monsoon-affected markets, this conflation risks misrepresenting project performance and weakening EVM-based decision-making. This paper proposes a UAV–BIM-integrated EVM framework whose central contribution is a composite metric, the Integrated Digital Construction Index (IDCI), intended to combine a Climate-Adjusted Schedule Performance Index (CASPI), a Climate-Adjusted Cost Performance Index (CACPI), a UAV-BIM Verification Confidence score (UBVC), and a Digital Maturity Score (DMS) into one dashboard-ready decision metric. The framework specifies how Earned Value could be derived bottom-up from UAV-photogrammetric progress reconciled against a cost-loaded Building Information Model rather than subjective percentage-complete estimates, and defines a companion Climate Delay Ratio (CDR) that quantifies the schedule baseline share falling within climate-hazard periods. Following a design-science research approach, the framework is derived analytically from earned value, earned schedule, and UAV–BIM progress-monitoring theory, and is illustrated - not empirically validated - through a fully hypothetical 18-month high-rise case built on a synthetic dataset; no real project, UAV, or cost-accounting data are used. All reported figures demonstrate only the mathematical behavior of the proposed indices under assumptions chosen by the author, not their real-world accuracy. In the illustrative case, conventional SPI falls to 0.66 during a simulated cyclone month while CASPI remains at 0.89, and IDCI rises from 0.68 to 0.92 as UAV-BIM verification coverage and framework maturity increase in the synthetic scenario. The paper's contribution is conceptual and methodological a reusable metric set, an explicit and reproducible computational procedure, a dashboard architecture, and a phased empirical validation agenda rather than a demonstrated improvement in project-control outcomes, which remains to be established on real, instrumented projects.
References
Adeeko, A. A., & Akinola, V. O. (2025). BIM-based management integration in tertiary institution construction projects in Southwest Nigeria. American Journal of Civil Engineering and Constructions, 1(1), 44–52. https://doi.org/10.54536/ajcec.v1i1.5323
Aziz, R. F. (2013). Ranking of delay factors in construction projects after Egyptian revolution. Alexandria Engineering Journal, 52(3), 387–406.
Basher, M. K., Islam, M. T., Ahmed, M. N., & Joarder, A. (2025). Design and implementation of a virtual hoisting system: A digital twin approach for early warning and fault detection. American Journal of Innovation in Science and Engineering, 4(1), 32–42. https://doi.org/10.54536/ajise.v4i1.4098
Cárdenas, C., Zapata, P., & Lozano, N. (2018). Integrating 5D Building Information Modeling with Earned Value Management methodologies through a computation tool. Revista Ingeniería de Construcción, 33(3), 263–278.
Ferdous, M. R., Biswas, M., Jany, M. R., & Rakhsit, S. (2025). Application of Building Information Modeling (BIM) in bridge design and construction management. American Journal of Innovation in Science and Engineering, 4(3), 30–37. https://doi.org/10.54536/ajise.v4i3.5681
Fleming, Q. W., & Koppelman, J. M. (2016). Earned value project management (4th ed.). Project Management Institute.
Flyvbjerg, B., Ansar, A., Budzier, A., Buhl, S., Cantarelli, C. C., Garbuio, M., Glenting, C., Holm, M., Lovallo, D., Lunn, D., Molin, E. J. E., Rønnest, A., Stewart, A., & van Wee, B. (2018). Five things you should know about cost overrun. Transportation Research Part A: Policy and Practice, 118, 174–190.
Freimuth, H., & König, M. (2018). Planning and executing construction inspections with unmanned aerial vehicles. Automation in Construction, 96, 540–553.
Golparvar-Fard, M., Peña-Mora, F., & Savarese, S. (2011). Integrated sequential as-built and as-planned representation with D4AR tools in support of decision-making tasks in the AEC/FM industry. Journal of Construction Engineering and Management, 137(12). https://doi.org/10.1061/(ASCE)CO.1943-7862.0000371
Ham, Y., Han, K. K., Lin, J. J., & Golparvar-Fard, M. (2016). Visual monitoring of civil infrastructure systems via camera-equipped Unmanned Aerial Vehicles (UAVs): A review of related works. Visualization in Engineering, 4(1), 1–8.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.
Jany, M. R. (2026). Drone technology in high-rise building construction monitoring: A conceptual review with reference to Bangladesh's real estate sector. American Journal of Civil Engineering and Constructions, 2(2), 14–19. https://doi.org/10.54536/ajcec.v2i2.8147
Jany, M. R., Karim, R., Shirin, M. S., Hossain, M. S., Jami, R. H., & Uddin, M. M. (2026). Adoption of Building Information Modeling (BIM) to improve urban construction in Bangladesh. American Journal of Civil Engineering and Constructions, 2(2), 1–13. https://doi.org/10.54536/ajcec.v2i2.7679
Lipke, W. (2003). Schedule is different. The Measurable News, March 2003, 31–34.
Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International Journal of Project Management, 27(4), 400–407.
Mahmud, A. K. M. R., Waheduzzaman, Hasan, M. M., & Uddin, M. N. (2025). Smart infrastructure for smart cities: Integration of electrical systems, IoT and construction materials. American Journal of Innovation in Science and Engineering, 4(3), 130–140. https://doi.org/10.54536/ajise.v4i3.6263
Panchal, P. B. (2023). The role of advanced earned value management (EVM) metrics in schedule performance analysis of building projects. World Journal of Advanced Engineering Technology and Sciences, 9(2), 391–405. https://doi.org/10.30574/wjaets.2023.9.2.0194
Pour Rahimian, F., Seyedzadeh, S., Oliver, S., Rodriguez, S., & Dawood, N. (2020). On-demand monitoring of construction projects through a game-like hybrid application of BIM and machine learning. Automation in Construction, 110, 103012.
Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting, resource allocation. McGraw-Hill.
Sani, J., Mallick, K. K., Sadhukhan, P. K., Banu, M. A., Shikder, R. R., & Biswas, T. K. (2025). Achieving carbon-neutral construction: Global trends and Bangladesh's sustainable future. American Journal of Environmental Economics, 4(1), 79–85. https://doi.org/10.54536/ajee.v4i1.4167
Vanhoucke, M., & Vandevoorde, S. (2007). A simulation and evaluation of earned value metrics to forecast project duration. Journal of the Operational Research Society, 58(10), 1361–1374.
Zhang, Y., Lu, L., Luo, X., & Pan, J. (2024). Global BIM-point cloud registration and association for construction progress monitoring. Automation in Construction, 168, 105796.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Md. Rafsan Jany, Mst. Shamima Shirin, Shariful Islam, Nahil Nimeri, Abu Hasnat Neloy, Rezaul Karim, Redwanul Haque Jami

This work is licensed under a Creative Commons Attribution 4.0 International License.