Efficient rebar dimension detection using prior-guided discrete global optimization full waveform inversion
Zihan Xia, Liyu Xie, Songtao Xue
Abstract. In non-destructive rebar dimension detection in concrete using full waveform inversion (FWI), exhaustive search methods and global optimization algorithms struggle with nonlinear electromagnetic responses and computational complexity, hindering their integration into engineering detection systems. This paper proposes a prior-guided discrete global optimization full waveform inversion (PG-DGOFWI) method to improve the efficiency and accuracy of rebar dimension detection. The method first uses reliable prior information to convert rebar coordinates and dimensions from continuous to discrete arrays, reducing the search space. It then constructs an objective function from electromagnetic wave data and identifies parameters using a global optimization algorithm. Simulation using the gprMax compared the inversion results of exhaustive search, global optimization, and PG-DGOFWI methods. Experimental tests based on the simulation model showed that PG-DGOFWI significantly improves rebar dimension detection. This method uses prior information to guide the search, reducing computational complexity and avoiding local optima, thereby enhancing stability and reliability.
Keywords
FWI, Rebar Dimension Detection, Prior-Guided, Nonlinear Electromagnetic Responses
Published online 3/25/2025, 8 pages
Copyright © 2025 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Zihan Xia, Liyu Xie, Songtao Xue, Efficient rebar dimension detection using prior-guided discrete global optimization full waveform inversion, Materials Research Proceedings, Vol. 50, pp 244-251, 2025
DOI: https://doi.org/10.21741/9781644903513-28
The article was published as article 28 of the book Structural Health Monitoring
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] Kim S. Investigating Structural Stability and Constructability of Buildings Relative to the Lap Splice Position of Reinforcing Bars. Journal of The Korea Institute of Building Construction. 2023;23:315-26.
[2] Parra-Montesinos GJ. High-performance fiber-reinforced cement composites: an alternative for seismic design of structures. ACI Structural Journal. 2005;102:668. https://doi.org/10.14359/14662
[3] Zhang J, Liu X, Liu J, Zhang M, Cao W. Seismic performance and reparability assessment of recycled aggregate concrete columns with ultra-high-strength steel bars. Engineering Structures. 2023;277:115426. https://doi.org/10.1016/j.engstruct.2022.115426
[4] Abou-Zeid M, Fowler DW, Nawy EG, Allen JH, Halvorsen GT, Poston RW et al. Control of cracking in concrete structures. Rep ACI Comm. 2001;224:12-6.
[5] Liu J, Tian Q, Wang Y, Li H, Xu W. Evaluation method and mitigation strategies for shrinkage cracking of modern concrete. Engineering-Prc. 2021;7:348-57. https://doi.org/10.1016/j.eng.2021.01.006
[6] Bala D, Garg R, Jain S. Rebar detection using GPR: An emerging non-destructive QC approach. Int J Eng Res Appl(IJERA). 2011;1:2111-7.
[7] Rathod H, Debeck S, Gupta R, Chow B. Applicability of GPR and a rebar detector to obtain rebar information of existing concrete structures. Case Stud Constr Mat. 2019;11:e00240. https://doi.org/10.1016/j.cscm.2019.e00240
[8] Jeon D, Kim MK, Jeong Y, Oh JE, Moon J, Kim DJ et al. High-accuracy rebar position detection using deep learning-based frequency-difference electrical resistance tomography. Automat Constr. 2022;135:104116. https://doi.org/10.1016/j.autcon.2021.104116
[9] Zanzi L, Arosio D. Sensitivity and accuracy in rebar diameter measurements from dual-polarized GPR data. Constr Build Mater. 2013;48:1293-301. https://doi.org/10.1016/j.conbuildmat.2013.05.009
[10] Asadi P, Gindy M, Alvarez M, Asadi A. A computer vision based rebar detection chain for automatic processing of concrete bridge deck GPR data. Automat Constr. 2020;112:103106. https://doi.org/10.1016/j.autcon.2020.103106
[11] Warren C, Giannopoulos A, Giannakis I. gprMax: Open source software to simulate electromagnetic wave propagation for Ground Penetrating Radar. Comput Phys Commun. 2016;209:163-70. https://doi.org/10.1016/j.cpc.2016.08.020
[12] Giannopoulos A. Modelling ground penetrating radar by GprMax. Constr Build Mater. 2005;19:755-62. https://doi.org/10.1016/j.conbuildmat.2005.06.007
[13] Riedel C, Mahmoudi E, Trapp M, Lamert A, Hölter R, Zhao C et al. A hybrid exploration approach for the prediction of geological changes ahead of mechanized tunnel excavation. J Appl Geophys. 2022;203:104684. https://doi.org/10.1016/j.jappgeo.2022.104684
[14] Romdhane A, Grandjean G, Brossier R, Réjiba F, Operto S, Virieux J. Shallow-structure characterization by 2D elastic full-waveform inversion. Geophysics. 2011;76:R81-R93. https://doi.org/10.1190/1.3569798
[15] Devine RD, Barbachyn SM, Thrall AP, Kurama YC. Effect of tripping prefabricated rebar assemblies on bar spacing. J Constr Eng M. 2018;144:04018099. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001559
