Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (11): 228-233.doi: 10.11896/jsjkx.210800039

• Artificial Intelligence • Previous Articles     Next Articles

Detection Method of Rebar in Concrete Diameter Based on Improved Grey Wolf Optimizer-based SVR

LU Chun-yi1, YU Jin1, YU Zhong-dong1, DING Shuang-song1, ZHANG Zhan-long2, QIU Ke-cheng2   

  1. 1 Lanxi Power Supply Company,State Grid Zhejiang Electric Power Co.,Ltd,Jinhua,Zhejiang 321100,China
    2 State Key Laboratory of Power Transmission Equipment & System Security and New Technology,Chongqing University,Chongqing 400044,China
  • Received:2021-08-04 Revised:2021-10-23 Online:2022-11-15 Published:2022-11-03
  • About author:LU Chun-yi,born in 1978,master,se-nior engineer.His main research inte-rests include electromagnetic measurement,electrical engineering and automation.
    ZHANG Zhan-long,born in 1971,professor,Ph.D supervisor.His main research interests include electromagnetic measurement and numerical analysis.
  • Supported by:
    National Natural Science Foundation of China(52077012).

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Abstract: The traditional reinforced concrete detection method uses linear fitting or standard value look-up table method,which can only roughly estimate the diameter of rebar.In view of the fact that there are few sample data of the diameter detection,and the detection result changes non-linearly due to the influences of the buried depth and the distance between adjacent rebars,a SVR detection method based on IGWO is proposed(IGWO-SVR).Firstly,the inverse learning strategy is used to optimize the initial population distribution,which improves the GWO global search ability.And he random differential mutation strategy is used to expand the search range,which can avoid the GWO algorithm from falling into the local optimum.Then,the IGWO algorithm is applied to the core parameter optimization of the SVR to improve the detection performance.Finally,the comparison and analysis of experimental results with the other three algorithm models show that the accuracy of the proposed method in the detection of rebar diameter has been effectively improved.

Key words: Rebar diameter, GWO, SVR, Reverse learning strategy, Random differential mutation strategy

CLC Number: 

  • TP301
[1]YE H,ZHANG Z,DAN Y,et al.Novel Method for Measurement of Rebar State of Cement Tower[J].IEEE Transactions on Instrumentation and Measurement,2021,70:1-8.
[2]XU C,ZHOU N,XIE J,et al.Investigation on Eddy CurrentPulsed Thermography to Detect Hidden Cracks On Corroded Metal Surface[J].NDT&E International,2016,84(12):27-35.
[3]KAUR P,DANA K J,ROMERO F A,et al.Automated GPR Rebar Analysis for Robotic Bridge Deck Evaluation[J].IEEE Transactions on Cybernetics,2016,46(10):2265-2276.
[4]LIAO Y,XIONG B,LI Q,et al.Study on Imaging Algorithm by Using Ultrasonic Array Probes for Concrete Structures[J].Piezoelectrics and Acoustooptics,2012,34(6):932-935.
[5]SUN Y,LI Q,HUANG L,et al.Application of Simulated Annealing Genetic Algorithm to CT Imaging of Concrete Structure[J].Piezoelectrics and Acoustooptics,2013,35(4):487-490.
[6]EDDY I C,UNDERHILL P R,MORELLI J,et al.Pulsed Eddy Current Response to Liftoff in Different Sizes of Concrete Embedded Rebar[C]//2019 IEEE SENSORS.Montreal,QC,Canada,2019:1-4.
[7]HE D F,TAUTSUMI N,TSUTSUMI N,et al.Corrosion Eva-luation of Steel Reinforcing Bar Using Electromagnetic Method[C]//2019 PhotonIcs & Electromagnetics Research Symposium-Spring(PIERS-Spring).Rome,Italy,2019:690-693.
[8]LU J J.Development of Rebar Detector Based on MagneticMeasurement[D].Harbin Institute of Technology,2017.
[9]LI T B,YIN Y H,LI C C,et al.Analysis of the detection algorithm for internal reinforcement of reinforced concrete based on soft sensing [J].Construction Technology,2019,48(6):88-92.
[10]NI G Z.Engineering electromagnetic field[M].Beijing:Higher Education Press,2009.
[11]LEI J S,CHEN J F.The regression prediction analysis of grouting concretion stone’s strength based on SVR [J].AdvancedMaterials Research,2013,859(10):171-176.
[12]LI S,FANG H,LIU X.Parameter optimization of support vector regression based on sine cosine algorithm [J].Expert System,2018,91(2):63-77.
[13]CAO Q K,ZHAO F.Forecast of water inrush quantity from coal floor based on genetic algorithm-support vector regression[J].Journal of China Coal Society,2011,36(12):2097-2101.
[14]LIU D W,XU Q,TANG Y,et al.Prediction of Water Inrush in Long-Lasting Shutdown Karst Tunnels Based on the HGWO-SVR Model [J] IEEE Access,2021,9:6368-6378.
[15]ZHOU P,GUO D W,WANG H,et al.Data-Driven RobustM-LS-SVR-Based NARX Modeling for Estimation and Control of Molten Iron Quality Indices in Blast Furnace Ironmaking[J].IEEE Transactions on Neural Networks and Learning Systems,2018,29(9):4007-4021.
[16]HATTA N M,ZAIN A M,SALLEHUDDIN R,et al.Recent studies on optimization method of Grey Wolf Optimizer(GWO):a review(2014-2017) [J].Artificial Intelligence Review,2018,18(2):1-33.
[17]MISHRA A K,DAS S R,RAY P K,et al.PSO-GWO Optimized Fractional Order PID Based Hybrid Shunt Active Power Filter for Power Quality Improvements[J].IEEE Access,2020,8:74497-74512.
[18]LI K,CHENG G Y,SUN X D,et al.A Nonlinear Flux Linkage Model for Bearingless Induction Motor Based on GWO-LSSVM [J] IEEE Access,2019,7:36558-36567.
[19]XU L W,WANG H,LIN W,et al.GWO-BP Neural Network Based OP Performance Prediction for Mobile Multiuser Communication Networks[J].IEEE Access,2019,7:152690-152700.
[20]TIZHOOSH H R.Opposition-based learning:a new scheme for machine intelligence[C]//International Conference on Computational Intelligence for Modelling,Control & Automation.IEEE,2005:695-701.
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