基于LightGBM的超大沉井下沉状态预测及传感器优化布置

Sinking state prediction and optimal sensor placement of super large open caissons based on LightGBM

沉井下沉状态预测及传感器优化布置有利于确保沉井安全平稳下沉、降低监测成本。基于机器学习中的LightGBM框架建立超大沉井下沉状态预测模型,利用沉井底部结构应力传感器监测数据,准确预测沉井下沉速度、横桥向高差和顺桥向高差,并通过传感器重要程度分析,提出可满足下沉状态预测精度的传感器优化布置方案。将提出的沉井下沉状态预测模型和传感器优化布置方法应用于常泰长江大桥主塔超大沉井下沉工程,结果表明:沉井下沉预测时,3个预测指标的R2均大于0.94,下沉状态预测精度高;对下沉状态预测较为重要的传感器主要集中在沉井外圈和横纵轴线附近区域;在满足下沉状态预测精度的条件下,传感器优化布置方案可减少传感器数量达45.5%。优化布置方案包含的传感器数量相同时,提出的优化布置方案在下沉状态预测精度方面整体优于基于特征变量相关性分析的优化布置方案。

The sinking state prediction and optimal sensor placement are conducive to ensuring safe and steady sinking of open caissons and reducing monitoring costs.Based on LightGBM,a framework in the field of machine learning,a sinking state prediction model of super large open caisson is established.By using the monitoring data of the stress sensors at the bottom of the open caisson,the sinking speed of the open caisson,the height difference in the transverse direction and the height difference along the bridge direction are accurately predicted.Through the analysis of the sensor importance,the optimal sensor placement scheme that can meet the sinking state prediction accuracy is determined.The proposed sinking state prediction model and the optimal sensor placement method were applied to the super large open caisson sinking project of the main tower of Changtai Yangtze River Bridge.The results show that the model has high accuracy in predicting the sinking state of the open caisson,and the R2 of the three prediction indexes is greater than 0.94.The important sensors for predicting the sinking state are mainly concentrated in the outer circle of the open caisson and the area near the transverse and longitudinal axes.Under the condition of satisfying the prediction accuracy of sinking state,the optimal sensor placement scheme can reduce the number of sensors by 45.5%.When the numbers of sensors in the optimal sensor placement schemes are same,the proposed optimization scheme is better than the scheme based on the correlation analysis of characteristic variables in terms of overall accuracy of sinking state prediction.