Non-Destructive Evaluation of the Condition of Subsurface Drainage in Pavement Using Ground Penetrating Radar (GPR)

Hao Bai, Purdue University

Abstract

Pavement drainage systems are one of the key drivers of pavement function and longevity, and effective drain maintenance can significantly extend a pavement’s service life. Maintenance of these drains, however, is often hampered by the challenge of locating the drains. Ground Penetrating Radar (GPR) typically offers a rapid and effective method to detect these underground targets. However, typical detection schema that rely upon the observation of the hyperbolic return from a GPR scan of a buried conduit still tend to miss many of the older drains beneath pavements as they may be partially or fully filled with sediment and/or may be fabricated from clay or other earthen materials, yielding a return signal that is convolved with significant background noise. To manage this challenge, this work puts forward an improved background noise and clutter reduction method to enhance the target signals in what amounts to a constructed environment that tends to have more consistent subsurface properties than one might encounter in a general setting. Within this technique, two major algorithms are employed. Algorithm 1 is the core of this method, and plays the role of reducing background noise and clutter. Algorithm 2 is supplementary, and helps eliminate anomalous discontinuous returns generated by the equipment itself, which could otherwise lead to false detection indications in the output of Algorithm 1. Instead of traditional 2-D GPR images, the result of the proposed algorithms is a 1-D plot along the survey line, highlighting a set of “points of interest” that could indicate buried drain locations identified at any given GPR operating frequency. Subsurface exploration using two different operating frequencies, 900 MHz and 400 MHz herein, is then employed to further enhance detection confidence. Points of interest are ultimately coded to define the confidence of the detection. Comparing the final result of proposed algorithms with the original GPR images, the improved algorithm is demonstrated to provide significantly improved detection results, and could potentially be applied to similar problems in other contexts. Besides the background reduction methods, a group of simulations performed using GPRMAX2D software are examined to explore the influence of road cross-section designs on sub-pavement drainage conduit GPR signatures, and evaluate the effectiveness of alternate GPR antennae configurations in locating these buried conduits in different ground conditions. Two different models were explored to simulate conduit detection. In addition, different pipe and soil conditions were modeled, such as pipe size, pipe material, soil moisture level, and soil type. Four different quantitative measurements are used to analyze GPR performance based on different key factors. The four measurements are 1) signal to background ratio (SBR) in dB; 2) signal to receiver noise ratio (SNR) in dB; 3) signal energy in Volts; and 4) average signal band power in Watts.

Degree

Ph.D.

Advisors

Sinfield, Purdue University.

Subject Area

Electrical engineering

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