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A platform for research: civil engineering, architecture and urbanism
Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data
In this paper, regularized deconvolution is utilized to analyze GPR signal collected from thin asphalt pavement overlays of various mixtures and thicknesses on a test site. By applying regularized deconvolution and the L-curve method, the overlapped interface was identified in the signal. The thickness of the thin layer was predicted with maximum error of 4.2%, which is less than 1.5 mm, a value well below the layer tolerance during construction. The study shows that the algorithm based on regularized deconvolution is a simple and effective approach for processing GPR data collected from thin pavement layers to predict their thickness.
Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data
In this paper, regularized deconvolution is utilized to analyze GPR signal collected from thin asphalt pavement overlays of various mixtures and thicknesses on a test site. By applying regularized deconvolution and the L-curve method, the overlapped interface was identified in the signal. The thickness of the thin layer was predicted with maximum error of 4.2%, which is less than 1.5 mm, a value well below the layer tolerance during construction. The study shows that the algorithm based on regularized deconvolution is a simple and effective approach for processing GPR data collected from thin pavement layers to predict their thickness.
Application of regularized deconvolution technique for predicting pavement thin layer thicknesses from ground penetrating radar data
Zhao, Shan (author) / Shangguan, Pengcheng (author) / Al-Qadi, Imad L. (author)
NDT&E International ; 73 ; 1-7
2015
7 Seiten, 26 Quellen
Article (Journal)
English
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