Enhancing dam safety evaluation using dam digital twins

Comprehensive evaluation of dams in a dynamic and proactive way is accepted as an effective strategy to improve dam safety. However, there is a lack of efficient and standardised approaches for managing monitoring-related information that can help to provide dynamic and reliable information for continuous dam evaluation. With the importance of digital twins (DTs) being proven in better data integration and interoperability, a DT-based approach for comprehensive dam evaluation and its data integration method based on extended industry foundation classes (IFC) are provided in this study. This paper presents a new data structure and corresponding data classification strategy, using which information required for continuous evaluation can be effectively organised. Considering that existing evaluation methods cannot fully characterise uncertainties in the evaluation process, a cloud model based comprehensive evaluation framework is proposed. In this approach, a multi-rule cloud reasoning model is utilised to evaluate monitoring points. Also, a multi-dimensional cloud model and an improved Criteria Importance Though Intercriteria Correlation (CRITIC) method are leveraged to assess evaluation indices affected by multiple factors. Finally, the results of a case study verify that the proposed DT-based solution realises a continuous dam safety evaluation, which contributes to automated and efficient dam condition monitoring.