Analysis of Influencing Factors on Transmission Line Construction Schedule Based on Fuzzy ISM - He, Zhiwen; Jiang, Siyan; Yao, Chi (Distributed Generation & Alternative Energy Journal, 2025)
This study develops a comprehensive analytical framework based on Fuzzy Interpretive Structural Modeling (Fuzzy ISM) to identify and analyze factors that influence transmission line construction schedules. Addressing the limitations of traditional risk assessment approaches in capturing the inherent uncertainties and complex interdependencies within large-scale infrastructure projects, this study integrates fuzzy logic principles with interpretive structural modeling to create a robust analytical tool. An empirical analysis was conducted on a 500 kV transmission line project in southwestern China, where 42 influencing factors were systematically evaluated across technical, management, environmental, and resource categories through expert assessments from 18 industry professionals. The Fuzzy ISM analysis reveals a five-level hierarchical structure, identifying project planning, risk assessment, and regulatory frameworks as fundamental driver factors that cascade their influence through multiple intermediate levels to ultimately impact schedule outcomes. Regulatory approvals emerge as the critical bottleneck with the highest amplification factor of 2.31, while the strongest direct impact relationship exists between construction delays and schedule delays (0.94). The MICMAC analysis demonstrates that most factors cluster in the linkage category, indicating a highly interconnected system requiring careful management of cascading effects. Critical path analysis identifies key influence routes with cumulative impacts ranging from 0.334 to 0.892, providing precise intervention targets for project managers. The methodology successfully bridges the gap between qualitative expert knowledge and quantitative analytical rigor, offering practical insights that could potentially reduce project delays by 30–40% through targeted interventions at identified critical nodes. These findings contribute to both theoretical advancement in project risk assessment methodologies and practical applications in infrastructure project management.