Year: 2025
Type: article
Abstract: New Austrian Tunneling Method tunnel construction projects are highly complex due to geological uncertainties, structural interdependencies, and environmental risks. Traditional risk assessment models primarily evaluate risks based on likelihood and impact, which oversimplifies risk interactions and fails to capture cascading effects. To address this limitation, this study introduces a multidimensional risk identification model that integrates key parameters, including risk conjunction, manageability, frequency, detection, duration, uniqueness, and threat, offering a systematic and comprehensive risk evaluation framework. Validated using real-world data, the model effectively prioritizes the top 20% of high-risk factors, such as tunnel ceiling deformation, groundwater level decline, and side wall deformation, ensuring better risk detection and strategic resource allocation. By applying the Pareto principle, it optimizes mitigation efforts, significantly improving project stability. Compared to traditional methods, this approach enhances accuracy, prioritization, and decision making, making it applicable beyond tunnels to other complex infrastructure projects. Future research should explore machine learning integration for enhanced automation and predictive accuracy, further improving the adaptability of risk assessment frameworks.
Institution: Kyungpook National University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: hybrid
Awards: G5301522678, G582434143
OpenAlex ID: W4413801894