综合岭回归和SARIMA方法在桥梁健康监测数据分析中的应用  

Application of Integrated Ridge Regression and SARIMA Methods in the Analysis of Bridge Health Monitoring Data

作　　者：谌桢文 常军[1] CHEN Zhen-wen;CHANG Jun(College of Civil Engineering,Suzhou University of Science and Technology,Suzhou 215011,China)

机构地区：[1]苏州科技大学土木工程学院,苏州215011

出　　处：《科学技术与工程》2023年第20期8846-8853,共8页Science Technology and Engineering

基　　金：国家自然科学基金青年科学基金(51908395);江苏省研究生科研与实践创新计划(SJCX22_1569)。

摘　　要：桥梁健康监测系统的实测数据普遍存在缺失问题,为了保证桥梁监测数据的完整性,更好地预测桥梁未来的健康状况,提出了一种具有样本内和样本外预测能力的组合模型。样本外预测可以基于现在数据预测未来的桥梁健康状态,样本内回归用于填补传感器数据中的缺失值,确保桥梁监测数据的完整性。由于不同位置处相同类型传感器的相关性较强,首先利用岭回归(ridge regression,RR)解决共线性问题,建立各传感器数据之间的关联,并预测缺失数据。接着引入季节性差分自回归滑动平均(seasonal autoregressive integrated moving average,SARIMA)方法,利用其样本外预测能力并结合岭回归方法预测桥梁未来运行数据。最后,将该方法应用于实桥中,验证了其有效性,为传感器数据填补以及预测桥梁未来状态提供了有效的预测模型。To ensure the integrity of bridge monitoring data and to better predict the future health status of bridges,a combined model with in-sample and out-of-sample prediction capability was proposed to ensure the integrity of bridge monitoring data,which were commonly missing in the real measurement data of bridge health monitoring systems.The out-of-sample prediction can predict the future bridge health status based on the present data,and the in-sample regression was used to missing data imputation in the sensor data to ensure the integrity of the bridge monitoring data.Due to the strong correlation of the same type of sensors at different locations,ridge regression(RR)was first used to solve the covariance problem,establish the correlation between each sensor data,and predict the missing data.Then,the seasonal autoregressive integrated moving average(SARIMA)method was introduced to predict the future operation data of the bridge by using its out-of-sample prediction capability and combining with the ridge regression method.Finally,the method was applied to a real bridge to verify its effectiveness and provide an effective prediction model for missing sensor data imputation as well as predicting the future state of the bridge.

关 键 词：大数据 缺失数据填补 数据预测 岭回归(RR) 季节性差分自回归滑动平均(SARIMA) 

分 类 号：U446[建筑科学—桥梁与隧道工程]