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Purpose The purpose of this study is to address the primary challenges in evaluating the safety status of concrete gravity dam construction. These challenges primarily consist of the complexity of the evaluation index system and the imprecision in describing random and uncertain information. The objective of this paper is to propose a new, integrated, comprehensive evaluation approach to overcome the limitations of traditional evaluation methods in determining indicator importance and handling ambiguous data. Design/methodology/approach This research proposes a comprehensive evaluation method integrating the Decision-Making Trial and Evaluation Laboratory (DEMATEL), Analytic Network Process (ANP) and a cloud model. Initially, a safety evaluation index system is to be established, encompassing human, environmental, management, quality and equipment factors. Second, DEMATEL analyses causal relationships between indicators, while ANP calculates their final weights. In conclusion, the construction safety level is determined using the cloud model’s three-dimensional digital eigenvalues. This approach is empirically evaluated through a case study of the Linhai Reservoir Dam Project in Heilongjiang Province. Findings Empirical research has identified a number of key factors that contribute to the safety of dam construction. These factors include the frequency of supervision, the status of the equipment being used and the presence of cold climates. Using the proposed model, the overall safety level of the Linhai Reservoir Dam Project was assessed as “Relatively Safe.” This evaluation is in alignment with the actual conditions on-site, demonstrating the model’s effectiveness in real-world applications. Originality/value This study proposes a novel, integrated analytical framework that effectively manages complex, ambiguous and random data in safety evaluations. This research combines DEMATEL, ANP and cloud models to offer a reliable theoretical foundation and actionable practical guidance for identifying and managing safety risks in heavy construction projects. This is of particular value for infrastructure development in cold regions.
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Li et al. (2026) studied this question.
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