Multi-sensor real-time monitoring of dam behavior using self-adaptive online sequential learning

The behavior monitoring model is the most widely used method in dam health monitoring, but existing methods still concentrate mainly on offline modeling or batch learning, neglecting the timeliness requirement. This paper describes an online model based on sequential learning for real-time monitoring of dam displacement behavior. The proposed method involves two major modeling stages. First, kernel principal component analysis (KPCA) is used for multi-sensor data fusion to extract the more essential contextual components and remove the multicollinearity from the raw sensory data. Second, a novel self-adaptive online sequential extreme learning machine (SOS-ELM) is presented to efficiently capture the complex nonlinear mapping from environmental variables to displacements by coupling the classic OS-ELM with regularization technique and forgetting mechanism. The proposed model is verified using monitoring data of a real-world concrete dam. The results show that the proposed sequential model can obtain satisfactory prediction accuracy with a low computational cost, and hence, is a competitive modeling tool for dam behavior monitoring.

Automated summary

This paper presents an online model based on sequential learning for real-time monitoring of dam displacement behavior. The proposed model effectively captures the complex nonlinear mapping from environmental variables to displacements through sequential learning and data fusion techniques. The verification using real monitoring data shows that the proposed model can achieve satisfactory prediction accuracy with low computational cost.