混沌理论支持下的桥梁变形监测研究

Research on bridge deformation monitoring based on chaos theory

针对桥墩的非线性下沉问题,引入了混沌理论。采用改进的C-C算法计算时间序列的时间延迟τ,采用改进的G-P算法计算最佳嵌入维数m,进行相空间重构,并与传统算法对比抗干扰性,计算效率等得到了改善,运用Lvyapunov指数判别该时间序列的混沌特性;最后根据所求参数建立加权一阶局域预计模型和RBF神经网络混沌预计模型,分别对观测数据进行预计分析,将混沌时间预测结果与指数平滑法预测结果进行对比分析。得出混沌时间预测精度高于指数平滑法预测精度,RBF神经网络混沌预计模型的预计精度最高,证明混沌时间序列预计精度可靠,能够实时对桥身变形进行监测,避免灾害的发生。

Aiming at the problem of pier nonlinear sinking,chaos theory is introduced.The reconstructed by the improved C-C and the G-P algorithm of time series,compared with traditional algorithms,the anti-interference and computational efficiency are improved. The maximum Lvyapunov exponent is obtained to determine whether there is chaos in time series. Finally,a weighted first-order local prediction model and a RBF neural network chaotic prediction model are established according to the obtained parameters to respectively predict and analyze the observed data.The chaotic time prediction results are compared with those of exponential smoothing method. The prediction precision of chaotic time is higher than that of exponential smooth method,and the predicted precision of chaotic model of RBF neural network is the highest,which proves that the predicted precision of chaotic time series is reliable,and can monitor the deformation of the bridge body in real time to avoid disasters.