MnDOT Road Doctor Survey Van

Evaluating Pavement Thickness With 3D Ground-Penetrating Radar

Building on previous MnDOT-sponsored work, researchers have developed a nondestructive method of assessing pavement thickness using 3D ground-penetrating radar (GPR). A vehicle equipped with an array of transmitting and receiving antenna pairs travels at traffic speed collecting full-width GPR data for analysis, minimizing the amount of pavement coring required.

MnDOT assesses pavement thickness as a quality assurance and quality control measure in pavement maintenance and renewal processes. Existing pavement thickness influences the type of maintenance chosen for a road segment, such as full-depth reclamation, cold in-place recycling or whitetopping. 

“The 3D GPR is a fast, innovative method to evaluate subsurface conditions including pavement thickness. It will save time and money while providing accurate pavement data to support roadway restoration decisions,” said Shongtao Dai, research operations engineer, MnDOT Office of Materials and Road Research.

Currently, MnDOT determines pavement thickness by removing pavement samples or cores from a roadway, a destructive test method. The core provides data about a very specific area, and the core site must be quickly repaired. Previous research through MnDOT’s Office of Materials and Road Research examined the use of ground-penetrating radar (GPR) in local road applications. Further research addressed the surveying, mapping and reporting processes that needed to be developed to make it more effective as a tool for evaluating pavement thickness.  

MnDOT already maintains a vehicle equipped with 3D GPR and wanted to know the extent to which its capabilities could reduce or replace core sampling to determine pavement thickness. In addition, the agency wanted a software tool that would assist operators in more accurately evaluating pavement thickness than traditional, single-channel GPR systems. 

What Was Our Goal?

The project’s objectives were to establish equipment and test standards for pavement thickness evaluation using 3D GPR, as well as develop a calculation method and a software tool that uses that method to represent pavement thickness accurately and efficiently using 3D GPR data. 

What Did We Do?

The research team first reviewed MnDOT’s existing 3D GPR system, including its Examiner software procedures, to become familiar with the equipment’s data formats. Then the research team, MnDOT engineers and the Technical Advisory Panel (TAP) identified in-field pavements to be tested, with the understanding that additional test locations would be selected throughout the project.

GPR uses pulses of electromagnetic radiation in the microwave band (UHF/VHF frequencies) of the radio spectrum and detects reflected signals from target subsurface structures. Unlike other GPR, which uses one or two transmitting and receiving antenna pairs, 3D GPR uses multiple sets, generating a wider image. The 3D GPR for this project employs 11 transmitting and 11 receiving antennas, for a possible 121 transmitter and receiver pairs, and is incorporated into a van that can travel at traffic speed while acquiring data. 

A cross section of pavement shows how transmitted and received signals of GPR move through hot-mix asphalt, base and subgrade layers of a road.
The transmitted (Tx) and received (Rx) GPR signals are shown as they travel through hot-mix asphalt and other layers of a roadway and are then reflected back for data collection. 

Researchers set out to develop an algorithm based on the generalized common midpoint method using calculations of signal velocity in the asphalt layer to determine its depth. The algorithm would generate a pavement thickness estimate derived from the signals from many 3D GPR antenna pairs.

The calculation method was integrated into the first build of the software tool slated to be used by MnDOT 3D GPR vehicle operators. The software was then tested using the data from the in-field sites that were earlier identified and tested. The testing and refining effort continued among the research team, MnDOT engineers and the TAP, with substantial discussion and revision throughout the extensive process. Results generated through the software tool were compared against core samples and against other nondestructive testing methods. 

What Was the Result?  

Using all 121 possible transmitting and receiving antenna pairs would make data collection unnecessarily slow. Thus, data collection for this project also uses 27 transmitting and receiving pairs focused on nine points on the pavement. The software tool can analyze data sets from either 121- or 27-pair configurations. The use of data from multiple antenna pairs that are focused on the same point of the pavement surface and two analysis methods provides redundancy and increases the reliability of the results. 

“The software tool developed in this project efficiently interprets the 3D GPR data, determines the asphalt layer thicknesses and reports the results in a form that can be effectively used by MnDOT engineers,” said Lev Khazanovich, professor, University of Pittsburgh Department of Civil and Environmental Engineering.  

Researchers developed a software program that includes a graphical user interface coded in Java for ease of use. The computational program is coded in Fortran. The user provides appropriate inputs, such as ranges for the asphalt layer thickness and files with the 3D radar data. The program analyzes the data and reports the computed asphalt layer thicknesses in comma-separated text files that can be entered into Excel spreadsheets or other tools for analysis. Researchers also developed a YouTube video to assist users with the software program. 

What’s Next?

MnDOT’s 3D GPR vehicle is already in use, and the developed software was tested using the data collected from MnDOT test sections. Future activities include further validating the software with data collection from other projects, disseminating the information gathered in this study and sharing the necessary skills with those who will gather and analyze the data. The expected benefits are clear: reduced need for destructive and costly core sampling operations, and fewer maintenance staff working in live traffic to obtain the samples. 

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