Jianuo Gu

Large-scale hydroelectric projects play an essential role in sustainable energy infrastructure, yet their construction schedules are frequently disrupted by complex risk correlations and propagation effects. Traditional critical chain buffer management methods often rely on static presets, impeding dynamic allocation. This paper proposes a dynamic monitoring framework for critical chain buffers that enhances schedule resilience by accounting for risk interdependencies. The method employs a fuzzy DEMATEL-ISM-BN algorithm to identify risk propagation paths and quantify their impact on buffers; combines a WBS-RBS mapping matrix with fuzzy expert scoring to determine activity risk weights; uses a PSO-RF model to predict the actual activity completion times; and defines three buffer consumption scenarios based on the predictions to trigger dynamic reallocation. The approach is demonstrated through a case study of a pumped-storage power station in East China. The calculated project buffer was increased from 19.51 days under the root variance approach to 30.56 days after incorporating risk compensation, representing an increase of 56.6% and demonstrating the additional protection required when risk interdependencies are considered in buffer sizing. When critical activities encounter unexpected risks, the dynamic reallocation mechanism promptly redistributes buffers to compensate, preventing delays from propagating downstream along the critical chain. Under the case-study scenario, the total remaining buffer at project completion was 101.09 days. After accounting for the risk-induced extension of activity M, the resulting critical chain duration was 1940.05 days, which was 97.50 days (approximately 4.79%) shorter than the planned critical chain schedule of 2037.55 days. The proposed mechanism enables unused buffer from completed activities to be transferred to subsequent activities, thereby improving buffer utilization efficiency during project execution. Through a closed-loop mechanism of “risk quantification → buffer calculation → schedule forecasting → dynamic allocation,” the proposed framework transforms schedule management from static presetting to adaptive control, improves project resilience and resource utilization efficiency, and supports sustainable project delivery by improving schedule reliability and reducing unnecessary buffer allocation.

Due to their large scale, long duration, complex geological conditions, and multiple stakeholders, water conservancy engineering projects are subject to diverse, interrelated, and uncertain risk factors that affect the construction timeline. Traditional critical chain buffer calculation methods, such as the cut-and-paste method and the root variance method, typically assume the independence of risks, which limits their effectiveness in addressing schedule delays caused by correlated risk events. To overcome this limitation, this paper proposes a novel critical chain buffer calculation approach that explicitly incorporates risk correlation analysis. A fuzzy DEMATEL-ISM-BN model is employed to systematically identify the interrelationships and influence pathways among schedule risk factors. Bayesian network inference is then used to quantify the overall occurrence probability while accounting for risk correlations. By integrating critical chain management theory, risk impact coefficients are introduced to improve the traditional root variance method, resulting in a buffer calculation model that captures interdependencies among schedule risks. The effectiveness of the proposed model is validated through a case study of the X Pumped Storage Power Station. The results indicate that, compared with conventional methods, the proposed approach significantly enhances the robustness of project schedule planning under correlated risk conditions while appropriately increasing buffer sizes. Consequently, the adaptability and reliability of schedule control are improved. This study provides novel theoretical tools and practical insights for schedule risk management in complex engineering projects.