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Diameter Inversion for Dense Rebars Based on Dual-Polarized GPR

  • Shuangying Sun
  • , Junbo Gong
  • , Yuhan Li
  • , Kunao Li
  • , Tian Lan*
  • *此作品的通讯作者
  • Beijing Institute of Technology

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

Reinforced concrete is an essential material in building engineering. Estimating rebar diameter is crucial for evaluating structural load-bearing capacity and overall stability, which is of great significance for the structural health monitoring of buildings. Ground-penetrating radar (GPR), as a nondestructive testing technique, can be used to assess rebar diameter. However, in practical engineering, rebars in concrete are often densely distributed, leading to overlapping electromagnetic echoes from multiple targets. Existing diameter inversion methods based on GPR are primarily designed for isolated single rebar and are not well-suited for diameter estimation in multitarget scenarios. To tackle this problem, this paper presents a fast multiecho separation method to improve the accuracy of diameter inversion in multirebar scenarios. First, a multirebar scattering model for rebars is established. Subsequently, through improved deconvolution and the orthogonal matching pursuit algorithm, individual rebar echoes within the overlapping echoes are rapidly resolved. Finally, simulation and practical experiments have verified that this method can effectively separate individual rebar echoes from multiple rebar echoes. By integrating the diameter inversion algorithms based on the wideband power ratio and natural resonance frequency, respectively, the accuracy of diameter inversion can be significantly enhanced. The relative error in the inverted diameter has decreased by approximately 23%–58%, and the stability of the diameter estimation algorithm has been effectively improved. Compared to the echo separation method using original deconvolution and orthogonal matching pursuit, the enhanced multiecho separation method reduces the computation time by about 80%.

源语言英语
期刊论文编号5470113
期刊Structural Control and Health Monitoring
卷2026
期1
DOI
出版状态已出版 - 2026

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