钢拱塔斜拉桥的温度耦合效应和索力预测

钢拱塔斜拉桥的受力体系与传统斜拉桥有所不同，为研究环境温度变化对这种异形桥塔斜拉桥主要受力部件的影响，以某钢拱塔斜拉桥为工程背景，首先基于在线监测获取的环境和部件温度数据，分析斜拉索索力、拱塔倾角和主梁应变的温度时变效应；然后以斜拉索为研究对象，通过该桥的有限元模型升降温模拟，分析各部件温差引起的温度耦合效应对拉索索力的影响；最后以环境温度、主梁温度、桥塔温度为输入，索力为输出，利用长短期记忆神经网络对实测索力-温度数据进行映射，实现数据压缩和特征提取，建立温度-索力预测模型，再对网络模型输入新的温度监测数据，以预测索力。研究结果表明：主梁和钢拱塔温度变化具有周期性，且滞后于环境温度；主梁应变与环境温度的变化趋势基本一致但具有一定的滞后性，环境温度变化对拱塔倾角的影响很小且没有周期性规律；索力与环境温度呈线性负相关，且需要考虑斜拉桥各部件的温差所引起的温度耦合效应；长短期记忆神经网络对带有时序特性的数据训练效果好，建立的温度-索力关系模型准确度高，可用于该桥索力的实时预测。

Temperature coupling effects and cable force prediction of cable-stayed bridge with steel arch tower

The mechanical system of a cable-stayed bridge with a steel arch tower is different from that of a traditional cable-stayed bridge. In order to investigate the effects of ambient temperature variations on the main components of a cable-stayed bridge with a tower in an abnormal shape, an actual cable-stayed bridge with a steel arch tower has been used as the engineering prototype. The online temperature data of the onsite environment and the bridge components were first collected and used to analyze the time-varying effects of the environmental temperature on the cable forces, the tower obliquity and the stress of the main girder. Subsequently, the analysis was focused on the cable forces. The temperature variation simulation was applied to the finite element model of the bridge, and the temperature coupling effects caused by the temperature difference between different bridge components on the cable forces were analyzed. Lastly, the temperatures of the environment, the tower and the main girder were used as the inputs, while the cable forces were defined as the outputs of a long short-term memory neural network. The network was trained using the actual measurement samples of the temperatures and the cable forces. Data compression and feature extraction were realized during the training process. Then, the prediction model for the cable forces was established, and new temperature monitoring data were input into the network model for predicting the cable forces. The analysis results show that the temperature variations of the main girder and the steel arch tower follow a periodic rule and lag behind the ambient temperature. The strain variation tendency of the main girder accords well with the ambient temperature, but the latter has a time lag. The influence of the ambient temperature variation on the obliquity of the arch tower is very small without any periodic rule. A linear negative correlation is found between the cable forces and the ambient temperature. The temperature coupling effect caused by the temperature difference between different bridge components should be considered in the analysis. The long and short-term memory neural network is suitable for the data with timing characteristics. The cable force prediction model based on the neural network has high prediction accuracy, and it can be used for the real-time prediction of this bridge.