Multi input–single output models identification of tower bridge movements using GPS monitoring system
Multi input–single output models identification of tower bridge movements using GPS monitoring system
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DOI：
10.1016/j.measurement.2013.09.046
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发表时间：
2014
期刊：
影响因子：
5.6
通讯作者：
M. Kaloop;Hui Li
中科院分区：
文献类型：
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作者：
M. Kaloop;Hui Li
本文采用RTK-GPS系统进行运动数据采集。两种识别模型即；使用多输入单输出 (MISO) 稳健拟合回归和具有外源输入的神经网络自回归移动平均 (NNARMAX) 模型来识别这些数据。试验结果分析表明：（1）结合稳健回归分析结果定义的NNARMAX[4 4 1 1]和[5 4 1 5]模型比NNARMAX [0 1 0 0]模型更准确地估计结构运动；（2）稳健拟合回归模型能够很好地映射外加荷载影响因素与塔架位移的关系。然而，温度和湿度对整个模态形状的影响并不显着，（3）交通荷载是影响塔桥位移的主要因素。
In this paper, RTK-GPS system was used for movement data collection. Two identification models namely; Multi input–single output (MISO) robust fit regression and Neural Network Auto-Regression Moving Average with eXogenous input (NNARMAX) models were used for the identification of these data. The analysis of test results indicate that: (1) the NNARMAX [4 4 1 1] and [5 4 1 5] models defined by taking into account the results of robust regression analysis estimate structural movements more accurately than the NNARMAX [0 1 0 0] model, and (2) the robust fit regression models have good capacities for mapping relationship of applied loads effects factors and displacements of tower. However, temperature and humidity effects on the entire modal shapes are insignificant and (3) the traffic loads are the main factor affects tower bridge displacement.