斜拉索力的季节自回归预测模型分析OA

To enhance the safety monitoring level of bridge stay cables,this study employs time series a-nalysis methods for dynamic early warning of cable force monitoring data,focusing on a long-span cable-stayed bridge as the engineering case.By collecting continuous cable force monitoring data during the bridge's trial operation period,two prediction models,Autoregressive Integrated Moving Average(ARI-MA)and Seasonal Autoregressive Integrated Moving Average(SARIMA),are constructed,establishing a comprehensive analytical framework encompassing data stationarity processing,model order determination,parameter estimation,and diagnostic validation.The findings reveal that the original cable force sequences exhibit significant diurnal periodic fluctuations.After seasonal differencing,the SARIMA model achieves optimal prediction accuracy,with its root mean square error reduced by 12.8%compared to the ARIMA model and the mean absolute percentage error controlled within 2.5%.This model addres-ses the limitations of traditional methods in capturing periodic characteristics.Engineering validation demonstrates that the proposed approach provides effective predictive maintenance decision support for structural health monitoring of bridges,offering a novel methodology for cable force monitoring data pro-cessing.