Spatial dependency enhanced dam safety evaluation: a digital-twin based monitoring platform integrating multi-sensor correlation analytics

The analysis of monitoring data plays a critical role in dam safety assessment. The data analysis process typically involves three stages: monitoring point information inspection, single-point analysis and early warning, and multi-point data fusion and evaluation. A key challenge in the multi-point evaluation stage is establishing an objective and reproducible standard that effectively links multi-point information with the dam’s structural behavior. For arch dams, the load-transfer system consists of horizontal arches and vertical girders. This study proposes an assessment system that utilizes three evaluation parameters: the alert level of individual monitoring points, the spatial correlation of alerted points, and the anomaly rate across all points. A deterministic correspondence is established between these parameters and the final score, thereby eliminating subjective judgment. The reliability of the evaluation results was verified using measured data and a typical case study. Furthermore, a digital twin (DT) platform was developed for managing, analyzing, and evaluating monitoring information. The proposed model was integrated into this platform. Both the monitoring data and the inferred dam behavior are visualized. This DT platform has been employed by a real arch dam in China. Operational results demonstrate its capability for integrated real-time analysis and presentation, significantly enhancing the intuitiveness of dam safety monitoring and the efficiency of decision-making.