小波神经网络在基坑变形预测的研究与应用

Research and application on predicting deformation of foundation pit based on wavelet neural network

针对一般小波神经网络存在的学习时间长,网络预测精度低的问题,提出了对网络输入层权值初始值进行归一化处理的优化方法,改进了原有小波神经网络。将改进后的模型应用于某市轨道交通1号线珠江路站深基坑水平变形预测中。监测结果表明,网络输出值与实测值吻合很好,优化后的小波神经网络收敛速度也更快;同时随着大量最新的监测数据输入到网络中学习,将使深基坑水平变形预测更加精确。

In order to solve the ubiquitous problems on cable-stayed bridge＇s loading test which are expensive cost and long testing time, this paper builds a practical cable-stayed bridge＇ s finite element model, analyzes the mechan- ics characteristics of cable-stayed bridge, and puts forward a method of loading test conditions＇ merging. Finally, Dezhou bridge＇ s loading tests are finished with the merging method. The research results show that for cable-stayed bridges, beam maximum moment condition and beam maximum deflection condition can combine into one condi- tion; and tower maximum moment condition and top tower horizontal displacement can combine into one condition. The merging method can be applied to practical bridge＇ s loading test and can popularize to other long-span bridge type＇ s loading test. In summery, the research result has a wide application prospect.