Empirical Validation of Predictive Project Controls for Early Schedule-Risk Detection: Analysis of CPM Float Erosion and Milestone Performance in Construction Projects
DOI:
https://doi.org/10.63665/IJAICE.0204.02Keywords:
Project Controls, Predictive Scheduling, Schedule Risk, CPM, Float ErosionAbstract
Construction projects involve early detection of schedule degeneration before milestones failure can be detected. This paper measures predictive project controls using the secondary quantitative longitudinal analysis of the historical Critical Path Method schedule data. It discusses the monitors of early-warning measures by Float Erosion Rate, Float Runway, Runway Ratio, and schedule-performance indicators. Primavera P6 updates has been archived to evaluate the deterioration of milestones, warning lead time, false positives and false negatives. The study aims to substantiate whether longitudinal schedule, patterns give accurate and timely risk indication evidence of exposure of developing risk to reinforce previous research, making of informed decisions and successful intervention in construction project management.
References
[1] Koulinas, G.K., Xanthopoulos, A.S., Tsilipiras, T.T. and Koulouriotis, D.E., 2020. Schedule delay risk analysis in construction projects with a simulation-based expert system. Buildings, 10(8), p.134.
[2] Yaseen, Z.M., Ali, Z.H., Salih, S.Q. and Al-Ansari, N., 2020. Prediction of risk delay in construction projects using a hybrid artificial intelligence model. Sustainability, 12(4), p.1514.
[3] Banerjee Chattapadhyay, D., Putta, J. and Rao P, R.M., 2021. Risk identification, assessments, and prediction for mega construction projects: A risk prediction paradigm based on cross analytical-machine learning model. Buildings, 11(4), p.172.
[4] Milat, M., Knezić, S. and Sedlar, J., 2021. Resilient scheduling as a response to uncertainty in construction projects. Applied Sciences, 11(14), p.6493.
[5] Przywara, D. and Rak, A., 2021. Monitoring of time and cost variances of schedule using simple earned value method indicators. Applied Sciences, 11(4), p.1357.
[6] Cheng, Y., Yuan, J., Zhu, L. and Li, W., 2020. Risk propagation model and simulation of schedule change in construction projects: A complex network approach. Complexity, 2020(1), p.8854609.
[7] Zhang, S. and Wang, X., 2021. Dynamic probability analysis for construction schedule using subset simulation. Advances in civil engineering, 2021(1), p.1567261.
[8] Alzarrad, M.A., Moynihan, G.P., Hatamleh, M.T. and Song, S., 2019. Fuzzy Multicriteria Decision‐Making Model for Time‐Cost‐Risk Trade‐Off Optimization in Construction Projects. Advances in Civil Engineering, 2019(1), p.7852301.
[9] Ou-Yang, C. and Chen, W.L., 2019. A hybrid approach for project crashing optimization strategy with risk consideration: a case study for an EPC project. Mathematical Problems in Engineering, 2019(1), p.9649632.
[10] Ma, G., Hao, K., Xiao, Y. and Zhu, T., 2019. Critical chain design structure matrix method for construction project scheduling under rework scenarios. Mathematical problems in engineering, 2019(1), p.1595628.
[11] Sepasgozar, S.M., Karimi, R., Shirowzhan, S., Mojtahedi, M., Ebrahimzadeh, S. and McCarthy, D., 2019. Delay causes and emerging digital tools: A novel model of delay analysis, including integrated project delivery and PMBOK. Buildings, 9(9), p.191.
[12] Hriday, M.S.H., 2022. Quantitative risk assessment of rail infrastructure projects using Monte Carlo simulation and fuzzy logic. American Journal of Advanced Technology and Engineering Solutions, 2(01), pp.55-87.
[13] Vaagen, H. and Ballard, G., 2021. Lean and flexible project delivery. Applied Sciences, 11(19), p.9287.
[14] Kim, M.H., Lee, E.B. and Choi, H.S., 2019. A forecast and mitigation model of construction performance by assessing detailed engineering maturity at key milestones for offshore EPC mega-projects. Sustainability, 11(5), p.1256.
[15] Jo, S.H., Lee, E.B. and Pyo, K.Y., 2018. Integrating a procurement management process into critical chain project management (CCPM): A case-study on oil and gas projects, the piping process. Sustainability, 10(6), p.1817.
[16] Urgiles, P., Sebastian, M.A. and Claver, J., 2020. Proposal and application of a methodology to improve the control and monitoring of complex hydroelectric power station construction projects. Applied Sciences, 10(21), p.7913.
[17] Lishner, I. and Shtub, A., 2022. Using an artificial neural network for improving the prediction of project duration. Mathematics, 10(22), p.4189.
[18] Dallasega, P., Rauch, E. and Frosolini, M., 2018. A lean approach for real-time planning and monitoring in engineer-to-order construction projects. Buildings, 8(3), p.38.
[19] Sobieraj, J. and Metelski, D., 2022. Project risk in the context of construction schedules—combined monte carlo simulation and time at risk (TaR) approach: insights from the fort bema housing estate complex. Applied Sciences, 12(3), p.1044.
[20] Liao, C., Aminudin, E., Mohd, S. and Yap, L.S., 2022. Intelligent risk management in construction projects: Systematic literature review. Ieee Access, 10, pp.72936-72954.
[21] Fan, Q., Wang, X., Yuan, J., Liu, X., Hu, H. and Lin, P., 2022. A review of the development of key technologies for offshore wind power in China. Journal of Marine Science and Engineering, 10(7), p.929.
[22] Olabi, A.G., Wilberforce, T., Elsaid, K., Sayed, E.T., Salameh, T., Abdelkareem, M.A. and Baroutaji, A., 2021. A review on failure modes of wind turbine components. Energies, 14(17), p.5241.
[23] Mackenzie, B., Celliers, L., Assad, L.P.D.F., Heymans, J.J., Rome, N., Thomas, J., Anderson, C., Behrens, J., Calverley, M., Desai, K. and DiGiacomo, P.M., 2019. The role of stakeholders in creating societal value from coastal and ocean observations. Frontiers in Marine Science, 6, p.137.
[24] Hai Nguyen, L., 2020. Empirical analysis of a management function’s failures in construction project delay. Journal of Open Innovation: Technology, Market, and Complexity, 6(2), p.26.
[25] Kim, S., Kim, S. and Lee, D.E., 2020. 3D point cloud and BIM-based reconstruction for evaluation of project by as-planned and as-built. Remote Sensing, 12(9), p.1457.
[26] Mayo-Alvarez, L., Alvarez-Risco, A., Del-Aguila-Arcentales, S., Sekar, M.C. and Yáñez, J.A., 2022. A systematic review of earned value management methods for monitoring and control of project schedule performance: An AHP approach. Sustainability, 14(22), p.15259.
[27] Milind Mehta, S., Chang, S., Oh, H.J., Kwon, J.H. and Kim, S., 2022. An investigation of construction project efficiency: Perception gaps and the interrelationships of critical factors. Buildings, 12(10), p.1559.
[28] Raj, M.W.Z. And Islam, M.M., 2022. Predictive Analytics For Risk And Compliance In It-Enabled Project Management Systems. Review of Applied Science and Technology, 1(01), pp.71-120.
[29] Costin, A., Wehle, A. and Adibfar, A., 2019. Leading indicators—A conceptual IoT-based framework to produce active leading indicators for construction safety. Safety, 5(4), p.86.
[30] Cerezo-Narváez, A., Pastor-Fernández, A., Otero-Mateo, M. and Ballesteros-Pérez, P., 2020. Integration of cost and work breakdown structures in the management of construction projects. Applied sciences, 10(4), p.1386.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Author

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


