«上一篇/Previous Article|本期目录/Table of Contents|下一篇/Next Article»

《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:

39卷

期数:

2022年04期

页码:

137-145

栏目:

出版日期:

2022-07-12

文章信息/Info

Title:

Intelligent Construction Control of Continuous Rigid Frame Bridge with Corrugated Steel Webs During Erection Process

文章编号:

1673-2049(2022)04-0137-09

作者:

白云腾^1,2,王晓明^1,2,录哲元³,张嘉鼎⁴

(1. 长安大学 桥梁工程研究所,陕西 西安 710064; 2. 长安大学 旧桥检测与加固技术交通行业重点实验室,陕西 西安 710064; 3. 黄河勘测规划设计研究院有限公司,河南 郑州 450003; 4. 中交第二公路工程局有限公司,陕西 西安 710065)

Author(s):

BAI Yun-teng^1,2, WANG Xiao-ming^1,2, LU Zhe-yuan³, ZHANG Jia-ding⁴

(1. Institute of Bridge Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Key Laboratory of Transport Industry of Bridge Detection Reinforcement Technology, Chang'an University, Xi'an 710064, Shaanxi, China; 3. Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, Henan, China; 4. CCCC Second Highway Engineering Co., Ltd., Xi'an 710065, Shaanxi, China)

关键词:

架设过程;  波形钢腹板;  连续刚构桥;  挠度预测;  MEC-BP神经网络;  智能化施工控制

Keywords:

erection process;  corrugated steel web;  continuous rigid frame bridge;  deflection prediction;  MEC-BP neural network;  intelligent construction control

分类号:

TU745

DOI:

10.19815/j.jace.2022.03115

文献标志码:

A

摘要:

针对波形钢腹板连续刚构桥架设过程中腹板剪切变形所致挠度预测问题,建立了采用思维进化算法(MEC)优化BP神经网络(MEC-BP)的挠度预测框架,研发了基于可视化管控系统的智能化施工控制模块。挠度预测框架主要包括三部分:首先,获取用于建立预测模型的数据集,其中输入样本和输出样本分别由拉丁超立方体采样(LHS)技术和高精度实体有限元模型生成; 然后,基于所获得的数据集,建立了基于MEC-BP神经网络的挠度预测模型,并根据统计准则对模型性能进行评估; 最后,利用训练良好的MEC-BP模型对波形钢腹板连续刚构桥架设施工的挠度进行预测。智能化施工控制模块主要包括两部分:首先,根据规范建立分级预警指标体系,分别为红色、黄色及绿色预警; 其次,基于可视化管控系统研发了施工控制模块,并开发了手机端APP,在此基础上,结合MEC-BP预测模型,实现了波形钢腹板连续刚构桥架设过程的智能化施工控制。以梁渠沟大桥为背景,将挠度预测值与现场实测值进行对比验证。结果表明:MEC-BP预测模型的挠度预测值与现场实测值吻合较好,预测误差均满足要求; 智能化施工控制系统在梁渠沟大桥架设过程中成功预警3次,助力梁渠沟大桥的建设取得圆满成功; 研究成果可为波形钢腹板连续刚构桥架设施工的智能化控制提供理论依据。

Abstract:

Aiming at the deflection prediction problem caused by web shear deformation during the erection of corrugated steel web continuous rigid frame bridge, the deflection prediction framework of BP neural network(MEC-BP)optimized by mind evolutionary algorithm(MEC)was established, and an intelligent construction control module based on visual control system was developed. The deflection prediction framework mainly included three parts. Firstly, the data set used to establish the prediction model was obtained, in which the input samples and output samples were generated by Latin Hypercube Sampling(LHS)technology and high-precision solid finite element model respectively. Then, based on the obtained data set, the deflection prediction model based on MEC-BP neural network was established, and the performance of the model was evaluated according to the statistical criteria. Finally, the well-trained MEC-BP model was used to predict the deflection of corrugated steel web continuous rigid frame bridge. The intelligent construction control module mainly included two parts. Firstly, a hierarchical early warning index system according to the specifications was established, which were red, yellow and green early warning respectively. Secondly, the construction control module was developed based on the visual control system, and the mobile APP was developed. On the basis, combined with the MEC-BP prediction model, the intelligent construction control of the erection process of corrugated steel web continuous rigid frame bridge was realized. Taking Liangqugou bridge as the background, the deflection prediction results were compared and verified with the field measured values. The results show that the deflection prediction values of MEC-BP prediction model are in good agreement with the field measured values, and the prediction errors meet the requirements. The intelligent construction control system successfully gives early warning three times during the erection of Liangqugou bridge, which helps the construction of Liangqugou bridge to a complete success. The research results can provide a theoretical basis for the intelligent control of the erection and construction of corrugated steel web continuous rigid frame bridge.

参考文献/References:

[1] WANG S H,LIU Y Q,HE J,et al.Experimental Study on Cyclic Behavior of Composite Beam with Corrugated Steel Web Considering Different Shear-span Ratio[J].Engineering Structures,2019,180:669-684.
[2]WANG X D,MIAO C Q,WANG X M.Prediction Analysis of Deflection in the Construction of Composite Box-girder Bridge with Corrugated Steel Webs Based on MEC-BP Neural Networks[J].Structures,2021,32:691-700.
[3]ELGAALY M,SESHADRI A,HAMILTON R W.Bending Strength of Steel Beams with Corrugated Webs[J].Journal of Structural Engineering,1997,123(6):772-782.
[4]HE J,LI X,LI C X,et al.A Novel Asynchronous-pouring-construction Technology for Prestressed Concrete Box Girder Bridges with Corrugated Steel Webs[J].Structures,2020,27:1940-1950.
[5]LU N W,NOORI M,LIU Y.Fatigue Reliability Assessment of Welded Steel Bridge Decks Under Stochastic Truck Loads via Machine Learning[J].Journal of Bridge Engineering,2017,22(1):04016105.
[6]DING S F,SU C Y,YU J Z.An Optimizing BP Neural Network Algorithm Based on Genetic Algorithm[J].Artificial Intelligence Review,2011,36(2):153-162.
[7]ZHANG J,YANG Y P,ZHANG J M.A MEC-BP-Adaboost Neural Network-based Color Correction Algorithm for Color Image Acquisition Equipments[J].Optik,2016,127(2):776-780.
[8]周绪红,刘界鹏,冯 亮,等.建筑智能建造技术初探及其应用[M].北京:中国建筑工业出版社,2021.
ZHOU Xu-hong,LIU Jie-peng,FENG Liang,et al.Preliminary Study on Intelligent Building Technology and Its Application[M].Beijing:China Architecture & Building Press,2021.
[9]李 政,张 圣,张卢喻,等.基于物联网技术的桥梁监测系统[J].物联网学报,2018,2(3):104-110.
LI Zheng,ZHANG Sheng,ZHANG Lu-yu,et al.Bridge Monitoring System Based on Internet of Things Technology[J].Chinese Journal on Internet of Things,2018,2(3):104-110.
[10]XU S Y,WANG J,SHOU W C,et al.Computer Vision Techniques in Construction:A Critical Review[J].Archives of Computational Methods in Engineering,2021,28(5):3383-3397.
[11]叶肖伟,董传智.基于计算机视觉的结构位移监测综述[J].中国公路学报,2019,32(11):21-39.
YE Xiao-wei,DONG Chuan-zhi.Review of Computer Vision-based Structural Displacement Monitoring[J].China Journal of Highway and Transport,2019,32(11):21-39.
[12]朱前坤,陈建邦,张 琼,等.基于计算机视觉人行桥挠度影响线非接触式识别[J].工程力学,2021,38(8):145-153.
ZHU Qian-kun,CHEN Jian-bang,ZHANG Qiong,et al.A Non-contact Recognition for Deflection Influence Line of Footbridge Based on Computer Vision[J].Engineering Mechanics,2021,38(8):145-153.
[13]GOH A T C.Back-propagation Neural Networks for Modeling Complex Systems[J].Artificial Intelligence in Engineering,1995,9(3):143-151.
[14]AGARWAL A,SINGH R D.Runoff Modelling Through Back Propagation Artificial Neural Network with Variable Rainfall-runoff Data[J].Water Resources Management,2004,18(3):285-300.
[15]孙承意,谢克明,程明琦.基于思维进化机器学习的框架及新进展[J].太原理工大学学报,1999,30(5):453-457.
SUN Cheng-yi,XIE Ke-ming,CHENG Ming-qi.Mind Evolution Based Machine Learning Framework and New Development[J].Journal of Taiyuan University of Technology,1999,30(5):453-457.
[16]公路工程结构可靠性设计统一标准:JTG 2120—2020[S].北京:人民交通出版社,2020.
Unified Standard for Reliability Design of Highway Engineering Structures:JTG 2120—2020[S].Beijing:China Communications Press,2020.
[17]公路波形钢腹板预应力混凝土箱梁桥设计规范:DB41/T 643—2010[S].北京:人民交通出版社,2010.
Code for Design of the Prestressed Concrete Box-girder Highway Bridge with Corrugated Steel Webs:DB41/T 643—2010[S].Beijing:China Communications Press,2010.

相似文献/References:

备注/Memo

备注/Memo:

收稿日期:2022-03-27
基金项目:国家自然科学基金项目(52178104); 陕西省交通科技项目(19-30K)
作者简介:白云腾(1995-),男,陕西延安人,工学博士研究生,E-mail:1071153073@qq.com。
通信作者:王晓明(1983-),男,山西朔州人,副教授,博士研究生导师,工学博士,E-mail:wang_xiaoming @qq.com。

常用功能

更新日期/Last Update: 2022-07-10