基于网络通信的非线性桥梁挠度监测预报系统

IF 2.4 Q2 ENGINEERING, MECHANICAL Nonlinear Engineering - Modeling and Application Pub Date : 2023-01-01 DOI:10.1515/nleng-2022-0251
Y. Lei, Zhiqiang Li
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引用次数: 0

摘要

摘要为了研究基于网络通信的桥梁挠度监测与预测系统,首先对桥梁挠度监测系统的发展现状、系统的总体需求、系统的硬件组成、系统软件的实现以及监测数据的及时处理与分析进行了论述。然后,对挠度监测数据中包含的外荷载变化趋势和结构阻力衰减信息进行动态预测,同时建立结构外作用的预测函数。最后,开发了一套专门用于处理桥梁意外灾害的快速监测系统,并在重庆高家花园大桥工程试验中进行了安装和应用。对高家花园大桥进行了挠度数据分析,并对大桥的运行维护提出了建议,实现了大桥的安全状态评估。结果表明,该系统具有较强的实用性、实时性和准确性。为桥梁管理人员对桥梁进行监督,及时制定具体的桥梁养护计划提供了方便、准确的途径。在通信数据的射频调制过程中,由于振荡数据的非线性特性,会产生谐波振荡,因此难以提高通信中传输数据的无线调制解调能力。传统方法采用神经网络模糊控制、分布估计、谐波平衡算法和非线性滚动控制。动态预测控制在质量、谐波平衡和稳定性控制方面的性能较差。为了平衡非线性通信系统的稳定性控制模型,构建非线性通信系统模型,提取通信系统的信号和信道特性,提出了一种改进的基于谐波的通信网络。设计了信道模型,采用通信网络控制方法对控制算法进行了改进。仿真结果表明,该算法可以提高非线性通信系统的稳定性,降低误码率,克服旁瓣中相干分量的干扰,实现接收机脉冲响应的自相关。累积输出的稳定性好,可以克服由于振荡数据的非线性特性而引起的谐波振荡带来的通信误差,提高通信质量。
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Nonlinear bridge deflection monitoring and prediction system based on network communication
Abstract In order to study the bridge deflection monitoring and prediction system based on network communication, first, the development status of the bridge deflection monitoring system, overall demand of the system, hardware composition of the system, realization of the system software, and the timely processing and analysis of monitoring data are discussed. Then, the dynamic prediction of the change trend of the external load and the decay information of the structural resistance contained in the deflection monitoring data is carried out, and the prediction function of the external effect of the structure is established at the same time. Finally, a rapid monitoring system specially designed to deal with accidental bridge disasters was developed, and it was installed and applied in the engineering experiment of Chongqing Gaojia Garden Bridge. The deflection data analysis of Gaojia Huayuan Bridge was carried out, and suggestions for the operation and maintenance of the bridge were put forward, and the safety status assessment of the bridge was realized. The results show that the system has strong practicability, real-time monitoring and accuracy. It provides a convenient and accurate way for bridge managers to supervise bridges and formulate specific bridge maintenance plans in a timely manner. During the radio frequency modulation of communication data, harmonic oscillations occur due to the nonlinear characteristics of oscillating data, so it is difficult to improve the wireless ability to modulate and demodulate transmitted data in communications. The traditional method uses neural network fuzzy control distribution estimation harmonic balance algorithm and nonlinear rolling. The performance of dynamic predictive control is poor in quality, harmonic balance, and stability control. An improved harmonic-based communication network is proposed to balance the stability control model of nonlinear communication system, construct nonlinear communication system model, and extract signal and channel characteristics of the communication system. The channel model is designed and the communication network control method is adopted to improve the control algorithm. The simulation results show that the proposed algorithm can be used to improve the stability of nonlinear communication system, reduce the bit error rate, overcome the interference of coherent component in sidelobe, and autocorrelated the impulse response of receiver. The stability of the cumulative output is good, which can overcome the communication error caused by the harmonic oscillation due to the nonlinear characteristics of the oscillating data, and improve the communication quality.
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来源期刊
CiteScore
6.20
自引率
3.60%
发文量
49
审稿时长
44 weeks
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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