桥梁重载实时在线识别系统研究
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摘要
本文所述桥梁重载实时在线识别系统采集前端采用光纤光栅传感器,相对于传统的重载识别具有很大的优势,是桥梁健康监测的一个重要组成部分,已经成为国内外该领域研究的重点。传统载重检查需要车辆停止静态称重,容易造成交通堵塞；采用的传感器主要是基于机电原理设计的,在性能上有其特定的弊端。而本识别装置,首先采集传感器采用的是光纤光栅传感器,在抗干扰性、体积小容易安装、测量值范围空间分辨率,线性范围等性能上都优于传统机电原理的传感器；由于是实时在线识别,车辆在动态运行中就被检查识别,不容易造成交通堵塞。由于动态检查对车辆静态重量以外的因素：车速、检查处桥面平整度、车辆自身的震动比较敏感,在动态识别克服传统识缺点的基础上,保证识别精度是本文识别系统的重点。
本文所述桥梁重载实时在线识别主要包括两部分：相关数据如动应变、车速等信息的采集、显示和储存,超重车辆的识别。
数据的采集、显示和储存部分通过网口将光纤光栅解调仪等下位机装置的数据传入服务器,通过实时曲线显示采集的数据,通过数据库保存采集的数据,以备以后数据的查询。采集部分涉及网口编程,储存部分涉及利用AD0方式访问数据库,由于采集频率高,数据量大,采集、显示和储存同时进行,所以系统开辟了多个线程,提高CPU利用率。
超重车辆识别部分,由于对最终识别结果的影响因素较多,所以将BP神经网络算法引入到本文识别部分。针对传统BP神经网络只能达到局部最优,收敛速度较慢的缺点,利用遗传算法优化传统的BP神经网络,使得优化后的BP神经网络既能提高收敛速度又能达到全局最优。
实验结果表明,本文识别系统能在克服传统静态称重的基础上,满足一定误差要求(5%)的现场应用,相对于单纯的动态识别提高了精度。
Data acquisition terminal of real-time online identification system of weigh-in-motion of vehicles on bridge described in this article uses fiber bragg grating sensors.There are a great of advantages compared to traditional weigh-in-motion recognition. It is an important part of the bridge health monitoring.It is becoming a focus on domestic and international research in this field. During traditional load check, vehicles need to be stopped and acceptd static weighting.It is likely to cause a traffic jam; sensors used here are mainly based on the electrical and mechanical principles, there are many specific drawbacks in performance. During the identification device, first,acquisition sensors use fiber Bragg grating are superior to traditional sensors based on the electrical and mechanical principles in many aspects,for example, anti-interference, small size and easy to install, resolving accuracy of the scope of the measured values, linear range; during real-time online identification, vehicles Dynamically are checked which is not likely to cause traffic jam. During Dynamic check,many factors can cause changing of result outside of the static weighting:speed, flatness of Bridge deck on check, vibration of the vehicle, dynamic recognition need overcoming the shortcuts of traditional load check. It is important to ensure that accuracy of this identification system.
Real-time online identification system of weigh-in-motion of vehicles on bridge consists of two parts:the collection、display and storage of relevant data, such as dynamic strain、speed and other information, the identification of overweight vehicles.
In the part of data acquisition, display and storage, acquisition of data is through Ethernet port.Data of fiber grating demodulator machine device is transmissed into sevice.Real-time curve shows data collected through the database.The collected data are saved to database for preparing for data query in future. Acquisition relates to the programming of the Ethernet port. Storage relates to the use of ADO accessing to database. Due to the high frequency of collection and the large volume of data acquisition、display and store at the same time, the system has opened up multiple threads to improve CPU utilization.
Final result of identification part of weigh-in-motion of vehicle is affected by many factors. the BP neural network algorithm is introducted to identify the part of this article. Due to traditional BP neural network can only reach a local optimum and convergent slower, genetic algorithms is used to optimize the traditional BP neural network.Optimized BP neural network can not only improve the convergence speed but also reach the global optimum.
The experimental results show that this article recognition system can overcome shortcut of the traditional static weighing.lt meets some field applications with error requirement under5%, improvimg the accuracy relativs to a simple dynamic recognition.
本文所述桥梁重载实时在线识别主要包括两部分：相关数据如动应变、车速等信息的采集、显示和储存,超重车辆的识别。
数据的采集、显示和储存部分通过网口将光纤光栅解调仪等下位机装置的数据传入服务器,通过实时曲线显示采集的数据,通过数据库保存采集的数据,以备以后数据的查询。采集部分涉及网口编程,储存部分涉及利用AD0方式访问数据库,由于采集频率高,数据量大,采集、显示和储存同时进行,所以系统开辟了多个线程,提高CPU利用率。
超重车辆识别部分,由于对最终识别结果的影响因素较多,所以将BP神经网络算法引入到本文识别部分。针对传统BP神经网络只能达到局部最优,收敛速度较慢的缺点,利用遗传算法优化传统的BP神经网络,使得优化后的BP神经网络既能提高收敛速度又能达到全局最优。
实验结果表明,本文识别系统能在克服传统静态称重的基础上,满足一定误差要求(5%)的现场应用,相对于单纯的动态识别提高了精度。
Data acquisition terminal of real-time online identification system of weigh-in-motion of vehicles on bridge described in this article uses fiber bragg grating sensors.There are a great of advantages compared to traditional weigh-in-motion recognition. It is an important part of the bridge health monitoring.It is becoming a focus on domestic and international research in this field. During traditional load check, vehicles need to be stopped and acceptd static weighting.It is likely to cause a traffic jam; sensors used here are mainly based on the electrical and mechanical principles, there are many specific drawbacks in performance. During the identification device, first,acquisition sensors use fiber Bragg grating are superior to traditional sensors based on the electrical and mechanical principles in many aspects,for example, anti-interference, small size and easy to install, resolving accuracy of the scope of the measured values, linear range; during real-time online identification, vehicles Dynamically are checked which is not likely to cause traffic jam. During Dynamic check,many factors can cause changing of result outside of the static weighting:speed, flatness of Bridge deck on check, vibration of the vehicle, dynamic recognition need overcoming the shortcuts of traditional load check. It is important to ensure that accuracy of this identification system.
Real-time online identification system of weigh-in-motion of vehicles on bridge consists of two parts:the collection、display and storage of relevant data, such as dynamic strain、speed and other information, the identification of overweight vehicles.
In the part of data acquisition, display and storage, acquisition of data is through Ethernet port.Data of fiber grating demodulator machine device is transmissed into sevice.Real-time curve shows data collected through the database.The collected data are saved to database for preparing for data query in future. Acquisition relates to the programming of the Ethernet port. Storage relates to the use of ADO accessing to database. Due to the high frequency of collection and the large volume of data acquisition、display and store at the same time, the system has opened up multiple threads to improve CPU utilization.
Final result of identification part of weigh-in-motion of vehicle is affected by many factors. the BP neural network algorithm is introducted to identify the part of this article. Due to traditional BP neural network can only reach a local optimum and convergent slower, genetic algorithms is used to optimize the traditional BP neural network.Optimized BP neural network can not only improve the convergence speed but also reach the global optimum.
The experimental results show that this article recognition system can overcome shortcut of the traditional static weighing.lt meets some field applications with error requirement under5%, improvimg the accuracy relativs to a simple dynamic recognition.
引文
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[8]朱军,2007,《光纤光栅在车辆动态称重系统中的应用研究》。博士论文,长安大学。
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[12]Ni Y Q, Jiang S F, Ko J M. Application of adaptive probabilistic neural network to damage detection of Tsing Ma Suspension Bridge:Health monitoring and management of civil infrastructure systems,2001,7(1):72-75
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[14]周志峰,2007,《应变式汽车轴重动态测量系统性能增长研究》。博士论文,上海交通大学。
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[17]姜德生,李盛,刘胜春.光纤光栅传感系统在桥梁重载车识别中的应用.中外公路,2007,27(3)：153-155
[18]Yee-Ting Li, Leith D, Shorten R N.Experimental Evaluation of TCP Protocols for High-Speed Networks[J]. IEEE/ACM transactions on Networking,2007,15(5):1109-1122
[19]陈根永,程亮,郭含姜燕.基于多线程技术提高电量采集的实时性.数采与监测,2009,25(9-1)：88-89
[20]梁伟.Visual C++网络编程经典案例详解[M].北京：清华大学出版社,2010.
[21]Hossain M I, Ali M M.SQL query based data synchronization in heterogeneous database environment[c].//ICCCI'12:International Conference on Computer Communication and Informatics.2012:1-5
[22]张雨,贺曙新,文绍波,皱政耀,张士强.汽车动态称重信号的两个处理方法.南京工程学院学报(自然科学版),2004,2(4)：1%21
[23]李丽宏,裴春清,刘军,谢克明.采样点选择对动态汽车衡称量精度的影响.中北大学学报(自然科学版),2010,31(1)：19-22
[24]林涛,李诚.汽车轴重动态检测的研究.西安公路学院学报,1994,14(3)：56-59
[25]王慧琴.小波分析与应用[M].北京：北京邮电大学出版社,2011.
[26]武奇生,王丹,陈圆媛,潘真亮.基于ETC的车辆动态称重系统轴载数据处理算法研究.公路交通科技应用技术版,2009,10：190-194
[27]程路,2008,《车辆动态称重技术研究》。博士论文,浙江大学。
[28]Jinghu Chen, Fossorier, P.M.C.Density evolution for BP-based decoding algorithms of LDPC codes and their quantized versions[c].//GLOBECOM'02:Global Telecommunications Conference.2002,2:1378-1382
[29]Ying He, Baohua Jin, Qiongshuai Lv, Shaoyu Yang.Improving BP Neural Network for the Recognition of Face Direction [c].//ISCCS'11:International Symposium on Computer Science and Society.2011:79-82
[30]史峰,王小川,郁磊,李洋MATLAB神经网络30个案例分析[M].北京：北京航天航空大学出版社,2010.
[31]张良均,曹晶,蒋世忠.神经网络实用教程[M].北京：机械工业出版社,2008.
[32]Lee Y H, Marvin A C, Porter S J.Genetic algorithm using real parameters for array antenna design optimisation [c].//High Frequency Postgraduate Student Colloquium.1999:8-13.
[33]Pengfei Guo, Xuezhi Wang, Yingshi Han.The enhanced genetic algorithms for the optimization design[c].//BMEI 10 3rd:International Conference on Biomedical Engineering and Informatics.2010,7:2990-2994.
[34]Tai-Shan Yan, Yong-Qing Tao, Du-Wu Cui.Research on handwritten numeral recognition method based on improved genetic algorithm and neural network[c].//ICWAPR'07:International Conference on Wavelet Analysis and Pattern Recognition.2007,4:1271-1276
[35]韩立群.人工神经网络理论、设计与应用[M].北京：化学工业出版社,2002.
[36]余立雪.神经网络与实例学习[M].北京：中国铁道出版社,1996.
[37]周志华,曹存根.神经网络及其应用[M].北京：清华大学出版社,2004.
[38]王学会.遗传算法和BP网络在发酵模型中的应用[D].天津：天津大学,2007.
[39]Math Works Inc.Genetic Algorithm and Direct Search Toolbox——MATLAB(?) version2.4.1,User's guide,2009.
[40]Math Works Inc.Genetic Algorithm and Direct Search Toolbox——MATLAB(?) version2.3,User's guide,2008.
[2]何红丽,2007,《汽车动态称重系统的研究与设计》。硕士论文,郑州大学。
[3]范爱民,廖高华,林谋有.公路车辆动态称重系统的设计.仪表技术与传感器,2010,6：62-64
[4]Guo Lan-Ying, Liang Bo, Dong Anguo.Research on Data Processing for Weight-in-Motion of Vehicles[c].//ICMTMA'09:International Conference on Measuring Technology and Mechatronics Automation.2009,2:454-456
[5]Ma Bin, Zou Xinguo.Study of Vehicle Weight-in-Motion System Based on Fiber-Optic Microbend Sensor [c].//ICICTA'10:International Conference on Intelligent Computation Technology and Automation.2010,3:458-461
[6]Zhou Z F,Cai P,Chen R X.Estimating the axle weight of vehicle in motion based on nonlinear curve-fitting [J]. Science,Measurment&Technology,IET.2007,1(4):185-190
[7]凌杰,2001,《公路动态称重系统的设计理论研究》。博士论文,长安大学。
[8]朱军,2007,《光纤光栅在车辆动态称重系统中的应用研究》。博士论文,长安大学。
[9]N.Takeda. Summary report of the structural health-monitoring project for smart composite structure systems:The official Journal of the Japan society of composite materials,2001, 3(10):61-63
[10]J P Ou. Some recent advances of intelligent health monitoring systems for civil infrastructures in mainland China:Proceedings of the first international conference on structural health monitoring and intelligent infrastructure, Tokyo, Japan,2003,5(7):80-83
[11]Z X Li, T H T Chan, J M Ko. Fatigue analysis and life prediction of bridges with structural health monitoring data-part I:Methodology and strategy, international Journal of fatigue, 2001,23(1):37-39
[12]Ni Y Q, Jiang S F, Ko J M. Application of adaptive probabilistic neural network to damage detection of Tsing Ma Suspension Bridge:Health monitoring and management of civil infrastructure systems,2001,7(1):72-75
[13]王守锋,2007,《车辆超限自动检测系统的研究与开发》。硕士论文,山东科技大学。
[14]周志峰,2007,《应变式汽车轴重动态测量系统性能增长研究》。博士论文,上海交通大学。
[15]刘云飞,朱军锋.动态检测汽车轴重.传感器技术,1998,17(5)：49-51
[16]刘飞飞,杨忠.车辆动态称重系统的设计.科技应用,2004,33(3)：12-14
[17]姜德生,李盛,刘胜春.光纤光栅传感系统在桥梁重载车识别中的应用.中外公路,2007,27(3)：153-155
[18]Yee-Ting Li, Leith D, Shorten R N.Experimental Evaluation of TCP Protocols for High-Speed Networks[J]. IEEE/ACM transactions on Networking,2007,15(5):1109-1122
[19]陈根永,程亮,郭含姜燕.基于多线程技术提高电量采集的实时性.数采与监测,2009,25(9-1)：88-89
[20]梁伟.Visual C++网络编程经典案例详解[M].北京：清华大学出版社,2010.
[21]Hossain M I, Ali M M.SQL query based data synchronization in heterogeneous database environment[c].//ICCCI'12:International Conference on Computer Communication and Informatics.2012:1-5
[22]张雨,贺曙新,文绍波,皱政耀,张士强.汽车动态称重信号的两个处理方法.南京工程学院学报(自然科学版),2004,2(4)：1%21
[23]李丽宏,裴春清,刘军,谢克明.采样点选择对动态汽车衡称量精度的影响.中北大学学报(自然科学版),2010,31(1)：19-22
[24]林涛,李诚.汽车轴重动态检测的研究.西安公路学院学报,1994,14(3)：56-59
[25]王慧琴.小波分析与应用[M].北京：北京邮电大学出版社,2011.
[26]武奇生,王丹,陈圆媛,潘真亮.基于ETC的车辆动态称重系统轴载数据处理算法研究.公路交通科技应用技术版,2009,10：190-194
[27]程路,2008,《车辆动态称重技术研究》。博士论文,浙江大学。
[28]Jinghu Chen, Fossorier, P.M.C.Density evolution for BP-based decoding algorithms of LDPC codes and their quantized versions[c].//GLOBECOM'02:Global Telecommunications Conference.2002,2:1378-1382
[29]Ying He, Baohua Jin, Qiongshuai Lv, Shaoyu Yang.Improving BP Neural Network for the Recognition of Face Direction [c].//ISCCS'11:International Symposium on Computer Science and Society.2011:79-82
[30]史峰,王小川,郁磊,李洋MATLAB神经网络30个案例分析[M].北京：北京航天航空大学出版社,2010.
[31]张良均,曹晶,蒋世忠.神经网络实用教程[M].北京：机械工业出版社,2008.
[32]Lee Y H, Marvin A C, Porter S J.Genetic algorithm using real parameters for array antenna design optimisation [c].//High Frequency Postgraduate Student Colloquium.1999:8-13.
[33]Pengfei Guo, Xuezhi Wang, Yingshi Han.The enhanced genetic algorithms for the optimization design[c].//BMEI 10 3rd:International Conference on Biomedical Engineering and Informatics.2010,7:2990-2994.
[34]Tai-Shan Yan, Yong-Qing Tao, Du-Wu Cui.Research on handwritten numeral recognition method based on improved genetic algorithm and neural network[c].//ICWAPR'07:International Conference on Wavelet Analysis and Pattern Recognition.2007,4:1271-1276
[35]韩立群.人工神经网络理论、设计与应用[M].北京：化学工业出版社,2002.
[36]余立雪.神经网络与实例学习[M].北京：中国铁道出版社,1996.
[37]周志华,曹存根.神经网络及其应用[M].北京：清华大学出版社,2004.
[38]王学会.遗传算法和BP网络在发酵模型中的应用[D].天津：天津大学,2007.
[39]Math Works Inc.Genetic Algorithm and Direct Search Toolbox——MATLAB(?) version2.4.1,User's guide,2009.
[40]Math Works Inc.Genetic Algorithm and Direct Search Toolbox——MATLAB(?) version2.3,User's guide,2008.