Pavement Evaluation using Ground Penetrating Radar

Cao, Yuejian; D’Agostino, Mollie; Labuz, Joseph F.

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Pavement Evaluation using Ground Penetrating Radar

2008-03-01
By Cao, Yuejian ; D’Agostino, Mollie ; Labuz, Joseph F.

File Language:

English

Details

Creators:

Cao, Yuejian ; D’Agostino, Mollie ; Labuz, Joseph F.
Corporate Creators:

University of Minnesota. Dept. of Civil Engineering
Corporate Contributors:

Minnesota. Dept. of Transportation
Subject/TRT Terms:
Publication/ Report Number:

MN/RC 2008-10
Resource Type:

Tech Report
Geographical Coverage:

Minnesota;United States
Corporate Publisher:

Minnesota. Department of Transportation
Abstract:

The objective of this project was to develop an efficient and accurate algorithm for the back analysis of pavement conditions measured by ground penetrating radar (GPR). In particular, more reliable information about the thickness of the asphalt concrete (AC) layer and the dielectric constants of the AC and base layers were obtained from the electromagnetic field measurements performed on roads using GPR. A brief introduction to the existing methodology for interpreting GPR images is reviewed, and the theory associated with electromagnetic wave propagation in layered structures is described. Utilizing the full waveform solution, algorithms for back analysis of pavement conditions were developed based on the artificial neural network approach and the frequency response function concept. Software called ''GopherGPR'' uses the GPR signal from one antenna to interpret the characteristics of the AC layer with no assumptions on material properties. Thus, the new technique has the capability of providing information not previously available.
Format:

PDF
Funding:

(c) 81655 (wo) 131
Download URL:

https://rosap.ntl.bts.gov/view/dot/37651/dot_37651_DS1.pdf
File Type:

[PDF - 5.11 MB]
Collection(s):

US Transportation Collection
Main Document Checksum:

urn:sha-512:7110e5f7d6bb88dd71570c4794b9d8bf50fc7795df774049986bac0828251682a3fdc0eb359a3a6314b7659d8922626c3532cc768ac53b2b1e0df92628ce1e93

File Language:

English

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