通过具有长短期记忆和注意机制的双向递归神经网络桥接运动中称重

IF 2.1 3区 工程技术 Q2 ENGINEERING, CIVIL
Yang Wang, Zhichao Wang
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引用次数: 4

摘要

在桥梁动态称重(BWIM)中,桥梁动态响应是在交通过程中测量的,用于识别超载车辆。过去对BWIM的大多数研究都使用基于力学的算法来估计轴重。相反,这项研究针对BWIM研究了深度学习,特别是递归神经网络(RNN)。为了获得大数据量来训练利用桥梁响应估计轴重的RNN网络,通过商业软件包LS-DYNA建立了桥梁有限元模型。为了模拟日常交通场景,模拟了数以万计的随机车辆编队,车辆类型、间距、速度、轴重、轴距等的不同组合。记录每个随机交通场景的动态响应,以训练RNN。在本文中,我们提出了一种针对BWIM的三阶段双向RNN。在BRNN中嵌入了长短期记忆(LSTM)和注意力机制,以进一步提高网络性能。额外的测试数据表明,与传统的移动力识别(MFI)方法相比,BRNN网络在估计轴重方面实现了高精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bridge weigh-in-motion through bidirectional Recurrent Neural Network with long short-term memory and attention mechanism
In bridge weigh-in-motion (BWIM), dynamic bridge response is measured during traffic and used to identify overloaded vehicles. Most past studies of BWIM use mechanics-based algorithms to estimate axle weights. This research instead investigates deep learning, specifically the recurrent neural network (RNN), toward BWIM. In order to acquire the large data volume to train a RNN network that uses bridge response to estimate axle weights, a finite element bridge model is built through the commercial software package LS-DYNA. To mimic everyday traffic scenarios, tens of thousands of randomized vehicle formations are simulated, with different combinations of vehicle types, spacings, speeds, axle weights, axle distances, etc. Dynamic response from each of the randomized traffic scenarios is recorded for training the RNN. In this paper we propose a 3-stage Bidirectional RNN toward BWIM. Long short-term memory (LSTM) and attention mechanism are embedded in the BRNN to further improve the network performance. Additional test data indicates that the BRNN network achieves high accuracy in estimating axle weights, in comparison with a conventional moving force identification (MFI) method.
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来源期刊
Smart Structures and Systems
Smart Structures and Systems 工程技术-工程:机械
CiteScore
6.50
自引率
8.60%
发文量
0
审稿时长
9 months
期刊介绍: An International Journal of Mechatronics, Sensors, Monitoring, Control, Diagnosis, and Management airns at providing a major publication channel for researchers in the general area of smart structures and systems. Typical subjects considered by the journal include: Sensors/Actuators(Materials/devices/ informatics/networking) Structural Health Monitoring and Control Diagnosis/Prognosis Life Cycle Engineering(planning/design/ maintenance/renewal) and related areas.
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