论文部分内容阅读
为解决由于材料老化、疲劳效应、荷载作用和环境变化等多种因素造成的大桥基础及上部结构形变影响,必须对大桥形变及其影响因素进行监测.针对大桥形变影响因素复杂多变的特点,采用径向基神经网络形变预测模型,推导了网络参数的改进算法,利用大桥实际监测数据,建立了径向基网络形变预测模型.研究结果表明:该方法在水平方向和垂直方向均能够达到4 mm以内的形变预测精度,能够为大桥的安全运行提供保障.
In order to solve the deformation of the bridge foundation and the superstructure caused by many factors such as material aging, fatigue effect, load effect and environmental change, it is necessary to monitor the deformation of the bridge and its influential factors.According to the complex and ever changing characteristics of the deformation of the bridge, Based on the deformation prediction model of RBF neural network, an improved algorithm of network parameters is deduced, and the deformation prediction model of RBF is established by using the actual monitoring data of the bridge. The results show that this method can achieve both horizontal and vertical deformation mm deformation prediction accuracy, to provide safeguards for the safe operation of the bridge.