Search Results (3)

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. Full article

To enhance the safety risk management and control capabilities for TBM (Tunnel Boring Machine) construction in hydraulic tunnels, this study conducts a correlation analysis and dynamic evolution study of safety risks. Data were collected through multiple channels, including a literature review, on-site records, and expert interviews. Grounded theory was employed for three-level coding to initially identify risk factors, and gray relational analysis was used for indicator optimization, ultimately establishing a safety risk system comprising 5 categories and 21 indicators. A multi-level hierarchical structure of risk correlation was established using fuzzy DEMATEL and ISM, which was then mapped into a Bayesian network (BN). The degree of correlation was quantified based on probabilistic information, leading to the construction of a risk correlation analysis model based on fuzzy DEMATEL–ISM–BN. Furthermore, considering the risk correlations, a safety risk evolution model for TBM construction in hydraulic tunnels was developed based on system dynamics. The validity of the model was verified using the AY project as a case study. The results indicate that the safety risk correlation structure for TBM construction in hydraulic tunnels consists of 7 levels, with the closest correlation found between “inadequate management systems” and “failure to implement safety training and technical disclosure”. As the number of interacting risk factors increases, the trend of risk level evolution also rises, with the interrelations within the management subsystem being the key targets for prevention and control. The most sensitive factors within each subsystem were further identified as adverse geological conditions, improper construction parameter settings, inappropriate equipment selection and configuration, weak safety awareness, and inadequate management systems. The control measures proposed based on these findings can provide a basis for project risk prevention and control. The main limitations of this study are that some probability parameters rely on expert experience, which could be optimized in the future by incorporating more actual monitoring data. Additionally, the applicability of the established model under extreme geological conditions requires further verification. Full article

The schedule is a critical factor in the development of prefabricated buildings. This paper establishes the schedule risk influencing factors for prefabricated building projects across five dimensions—design, production, transportation, installation, and others—encompassing a total of 14 factors. By integrating DEMATEL and ISM, it constructs a hierarchical network model using expert knowledge and maps it to Bayesian networks (BN), and the node probabilities were calculated using fuzzy set theory combined with the noisy-OR gate model. This DEMATEL-ISM-BN model not only infers the probability of schedule risk occurrence in prefabricated construction projects through causal reasoning and controls the schedule risk of prefabricated construction projects, but it also deduces the posterior probabilities of other influencing factors when a schedule risk occurs through diagnostic reasoning. This approach identifies the key factors contributing to schedule risk and pinpoints the final influencing factors. Research has shown that the three influencing factors of “tower crane worker lifting level”, “construction worker component installation technology”, and “design changes” significantly affect project progress, providing a new risk assessment tool for prefabricated building project progress, effectively helping enterprises identify potential risks, formulate risk control strategies, improve project success rates, and overall benefits. Full article