Small-Scale Void-Size Determination in Reinforced Concrete Using GPR. (6th June 2020)

• Record Type: Journal Article
• Authors: Yang, Yong; Lu, Junwei; Li, Rongzhe; Zhao, Weigang; Yan, Deli
• Other Names: Cavaleri Liborio Academic Editor.
• Is Part Of: Advances in civil engineering. Volume 2020(2020)
• Publication Date: 2020-06-06
• Subjects: Civil engineering -- Periodicals; Civil engineering; Periodicals; 624
• DOI: 10.1155/2020/2740309
• Languages: English
• ISSNs: 1687-8086

Abstract

Abstract : The detection and evaluation of void in concrete are imperative issues in health monitoring of civil engineering. However, the void is difficult to be detected at its early stage of formation on account of its small scale and concealment. Although, in view of the remarkable performance such as precision and continuity, ground penetrating radar (GPR) is widely employed in some special fields of nondestructive testing, the strong reflection of rebar which could submerge the echo of void is now a stumbling block for the application of GPR in reinforced concrete. In order to develop the tactic of small-scale void-size determination with considerable noise originated from rebar, a two-stage regression method is proposed in this study. First, a superposition model of target echoes was established to analyze the relationship between the space-time distribution and spectrum of the rebar echo and the void echo, and a curvelet transform was employed to reduce the strong reflection of the rebar. Then, the echo amplitude of small-scale void was investigated, and a regression method was put forward to determine the void size of small scale. Finally, forward simulation and experiment were carried out to verify the feasibility of the proposed method. Results demonstrated that the spectral distribution of the rebar echo is significantly higher than that of the void echo. The curvelet-based low-pass filter can effectively ensure the discrimination of void echo. The regression method performs a high-accuracy void-size determination with more than 90% in small scale.