A multiple-point monitoring model for concrete dam displacements based on correlated multiple-output support vector regression

Displacements reflect the overall behavior of a concrete dam; thus, it is of vital importance to evaluate the overall structural health status by displacement-based mathematical monitoring models. However, most of the existing monitoring models focus on point-by-point displacement modeling, ignoring the correlations among displacements at different measurement points. This study therefore proposes a model for dam multiple-point displacement monitoring based on the support vector regression (SVR) algorithm. The improved SVR-based model with multiple-output formulation is a new development based on the statistical learning theory, which can simultaneously analyze and predict displacements at multiple-measurement points. Furthermore, by introducing the weight vectors that separate the common and individual information, the potential correlations among multiple-point displacements can be fully exploited by the multiple-output SVR. Combining the above two improvements, a multiple-point monitoring model for dam displacements considering spatiotemporal correlations, referred to as correlated multiple-output SVR (CMOSVR), is constructed. The proposed model is verified using in-situ monitoring from a full-scale concrete gravity dam. The accuracy, robustness, and efficiency of the CMOSVR-based model are compared with those of conventional single-point monitoring models, such as classical hydrostatic-seasonal-time model and standard SVR-based model. Empirical results show that in both real and simulated noisy scenarios, the CMOSVR-based multiple-point model can achieve a better monitoring performance with less modeling time cost. Moreover, the superior performance of CMOSVR-based model does not require a very strong correlation among multiple-point displacements, which considerably improves the adaptability of the monitoring model to various possible scenarios. The novel multiple-point model will provide an effective technical support tool for ensuring the safe operation of dams.

Automated summary

This study proposed a dam multiple-point displacement monitoring model based on the SVR algorithm, which can analyze and predict displacements at multiple measurement points simultaneously. By introducing weight vectors to separate common and individual information and considering spatiotemporal correlations, the CMOSVR-based model showed better monitoring performance and higher adaptability to various scenarios compared to conventional single-point monitoring models.