A Multiple‐Point Deformation Monitoring Model for Ultrahigh Arch Dams Using Temperature Lag and Optimized Gaussian Process Regression

Existing dam displacement statistical methods simulate the thermal effects using simple harmonic functions ignoring the effects of ice periods, extreme heat, and seasonal weather. Moreover, existing data‐driven methods usually utilize a separate modeling strategy, inevitably ignoring the spatiotemporal correlation of multiple displacement points in dams, resulting in poor predictive performance. To overcome these shortcomings, this study proposes a novel machine learning (ML)—aided multiple‐point dam displacement predictive model considering the temperature hysteresis effect. Firstly, an improved hydraulic‐Air_temperture_Time (HT air T) statistical monitoring model is developed using the measured air temperature lagging monitoring data. On this basis, the multitask Gaussian process regression (multipoint GPR) algorithm with an improved kernel function to construct a multipoint deformation prediction model for ultrahigh arch dams. Then, the improved meta‐heuristic physics‐driven Frost algorithm is utilized to determine the optimal parameters of the multipoint GPR model. A high arch dam with a height of 305 m is used as the case study, and five displacement monitoring points are used for validation. Five advanced ML‐based algorithms are used to comparatively evaluate and verify the performance of the proposed method in terms of forecast accuracy and interpretability. The HT air T statistical model can better simulate the hysteresis effect of temperature on dam deformation. Moreover, the Frost‐optimized dam multipoint displacement prediction model with the RQ kernel functions outperforms the other comparison methods in terms of R 2 , mean absolute error (MAE), and root mean squared error (RMSE) evaluation indicators. This indicates the proposed method can mine the spatiotemporal correlation among multiple monitoring points of ultrahigh arch dams, further improving the overall deformation prediction and uncertainty estimation.