Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network
Safety Prediction Using Vehicle Safety Evaluation Model Passing on Long-Span Bridge with Fully Connected Neural Network
复制标题
基于全连接神经网络大跨桥梁车辆安全评估模型的安全预测
DOI：
10.1155/2019/8130240
复制
发表时间：
2019-10
影响因子：
1.8
通讯作者：
Robert Soltys
中科院分区：
文献类型：
--
作者：
Yang Yang;Yang Lin;Wu Bo;Yao Gang;Li Hang;Robert Soltys
近年来，大跨度桥梁上车辆通行的安全状况越来越受到人们的关注。为了寻找方便和高效的措施，已经进行了许多研究。提出了一种基于全连接神经网络（FCN）的大跨度桥梁车辆安全评价模型。首先，采用风洞试验和有限元模型研究了大跨度桥梁在风荷载作用下的响应。在此基础上，给出了典型车辆模型，建立了考虑天气条件的车桥系统。估计恶劣天气下车辆的事故类型。具体而言，确定车辆安全评价模型的输入和输出变量，同时实现训练、验证和测试数据。利用隐层、初始学习率、批量大小、激活函数和优化方法对29个模型进行了比较和分析。研究发现，4-15-15-4模型具有较好的预测性能，可为交通控制和降低桥梁上车辆事故概率提供一种实用工具。
The safety condition of vehicles passing on long-span bridges has attracted more and more attention in recent years. Many research studies have been done to find convenience and efficiency measures. A vehicle safety evaluation model passing on a long-span bridge is presented in this paper based on fully connected neural network (FCN). The first step is to investigate the long-span bridge responses with wind excitation by using the wind tunnel test and finite element model. Subsequently, typical vehicle models are given and a vehicle-bridge system is established by considering weather conditions. Accident types of vehicles with severe weather are estimated. In particular, the input and output variables of the vehicle safety evaluation model are determined, and simultaneously training, validation, and testing data are achieved. Twenty-nine models have been compared and analyzed by using hidden layer, initial learning rate, batch size, activation function, and optimization method. It is found that the 4-15-15-4 model occupies a preferable prediction performance, and it can provide a kind of utility for traffic control and reduce the probability of vehicle accidents on the bridge.
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影响因子：
1.6
作者：
Xinfeng Yin;Yang Liu;Suren Chen
通讯作者：
Xinfeng Yin;Yang Liu;Suren Chen
影响因子：
2.4
作者：
P. Ekins
通讯作者：
P. Ekins
影响因子：
1.9
作者：
F. Blattner
通讯作者：
F. Blattner
影响因子：
1.6
作者：
Vicki D Crinis
通讯作者：
Vicki D Crinis
影响因子：
0.2
作者：
R. Eglash
通讯作者：
R. Eglash