基于VSW-CDD-AuKELM的混凝土坝长期变形预测模型OA

Long term high-precision prediction of dam deformation is critical for the operational safety monitoring of concrete dams.Existing data-driven deformation monitoring models trained on historical data,neglecting the dynamic changes in the input-output mapping caused by material degradation or unforeseen disturbances during prolonged ser-vice,resulting in short prediction horizons that cannot support the long-term online safety monitoring of dams.To address these issues,the Concept Drift Detection(CDD)strategy is embedded into Kernel Extreme Learning Machine(KELM)algorithm,and the frequency of consecutive abnormal prediction results is defined as the sliding window size,thereby proposing a variable sliding window(VSW)technique to enhance the applicability of CCD strat-egy.Consequently,an adaptive updating KELM model based on CDD with VSW(VSW-CDD-AuKELM),is estab-lished for long-term deformation prediction of concrete dams.This model employs the VSW-enhanced CDD technique to monitor the prediction accuracy of the KELM model in real-time and adaptively updates the model parameters through the sliding window mechanism to maintain predictive performance,enabling online high-precision prediction of long-term dam deformation.A case study and comparative analysis involving a concrete arch dam demonstrate that the proposed model can resist concept drift in the mapping rules and maintain high-precision prediction over extended periods.Under identical forecast horizon,the model achieves an average Root Mean Square Error of 0.575 mm for deformation prediction,representing an improvement of over 26％in predictive accuracy compared to main-stream algorithms such as Multiple Linear Regression,Extreme Learning Machine,Support Vector Machine,and Long Short-Term Memory.