Kimberly Belli (CEE), Northeastern University Carey Rappaport (ECE), Northeastern University Sara Wadia-Fascetti (CEE), Northeastern University Abstract: Ground penetrating radar (GPR) for nondestructive testing is a relatively young technology, especially with application to civil infrastructure such as bridges and roadways. Conventional methods of processing and analyzing GPR data for civil infrastructure are often qualitative, using relative reflection amplitude from subsurface boundaries or reinforcing steel (rebars) as an indicator of health. This poster brings well-understood electrical engineering analysis t l t th li ti f d t ti t ti f b id i GPR Ui it ti f d d li t i ti l itti it dd th l l ti t ti l dl t i t df th d b id Forward Time Domain Ground Penetrating Radar Modeling of Scattering from Anomalies in the Presence of Steel Reinforcements tools to the application of nondestructive testing of bridges using GPR. Using iterative forward modeling to improve upon conventional permittivity and depth calculations, computational model geometry is computed for the assumed bridge deck with no anomalies present. A Finite Difference Time Domain (FDTD) GPR simulation on this model results in healthy bridge deck data that can be removed from measured data to bring anomalies to attention. For the purpose of this poster, the measured data is also simulated. In lieu of modeling the identified rebars as perfect electrical conductors (PECs), they are modeled as hard point source excitations. This allows for examination of the effect that the scattered waves from the rebar have on an anomaly, which is often neglected when clutter signals from the rebar are computationally modeled and subtracted. This is an important consideration for application of many inversion methods. State of the Art: Using data collected from a metal plate and bridge deck surface reflections, conventional data processing (Surface Reflection Parameters Method [1,2]) computes the reflection coefficients at layer boundaries. These parameters, in conjunction with the two-way travel time to the boundaries, are used to compute layer thicknesses and rebar depth. Air coupled antennas, often located 17” above the deck surface, result in less focused energy at the bridge deck surface. Therefore, reflections from a closely-spaced rebar array which, when viewed as a B-scan, appear as a homogeneous stripe rather than the distinct hyperbolas often visible in data collected by a ground coupled antenna. Construct Background Approximate layer Construct Rebar Reflections from point source array Approximate Subtract Background Iteratively perturb model Locate individual rebars Iteratively Use FDTD Determine Total Scattering Off of Anomaly Simulate with Simulate with background defect Subtract to Identify Determine Effect of Rebar on Scattering from Anomaly Simulation Information: • All data here is simulated • Spatial Resolution: 0.5” • Temporal Resolution: 29.5 ps • 2D FDTD with Splitfield PML • Transverse Magnetic Mode layer thicknesses and permittivities Approximate depth to rebar layer Subtract Background & Rebar reflections from Simulated total GPR data perturb model to find best fit permittivities & thicknesses individual rebars and replace with hard point sources perturb FDTD model to find rebar depth to find rebar spacing background, defect and no rebar background, defect, point source array (no external excitation) Scattering from Anomaly Due to Rebar Scattering from anomaly with no rebar present Scattering from anomaly with secondary rebar scattering* removed Simulated Total GPR Data Analogous to real world data Background Determine asphalt & concrete thickness & permittivities Point Source Array Determine rebar layer depth and separation Difference Between Simulated Total GPR Data and Healthy Model Healthy Deck Model = - + This includes errors in assumptions, all reflections off of any anomalies (probing wave, interactions of wave off of rebar and other layers, etc.) * Secondary scattering is scattering from rebar on the bridge deck layers, anomaly and other rebars in the array. Conventional tools for imaging subsurface anomalies (migration time reversal and Born approximation inversion) Discretized FDTD Bridge Deck Model Total Scattering from anomaly Scattering from anomaly (no rebar present) * Scattering from anomaly (secondary rebar scattering removed) * Fractional error in scattering if assumed no rebar * To compensate for the rebar scattered field that is incident on the anomaly the field must be isolated from the original GPR probing field. Subtracting this rebar excited field from the total field leaves the field that would be due to an isolated anomaly illuminated by the above ground GPR source. The field scattered from the anomaly will interact with the all of its surroundings, including rebar. • These interactions can be modeled for a presumably known geometry  Second order scattering (from the anomaly to the rebar, and then back to the anomaly, to be scattered again) can be assumed to be small for low contrast scatterers layers, anomaly and other rebars in the array. Conventional tools for imaging subsurface anomalies (migration, time reversal, and Born approximation inversion) • Are based on single incident wave interaction with regions of dielectric constant variations in an otherwise uniformbackground • In most cases, best image the variations when there is low dielectric contrast with the surrounding medium. • Are complicated by strong scatterers such as rebar which generate strong obscuring clutter relative t o weak low-contract anomalies and scatter secondary waves incident on the anomaly The clutter signals from the rebar can be computationally modeled and subtracted from the observed signals, but, the effects of their scattered waves on the anomaly must be compensated in order to apply inversion methods. Error is the lowest when the anomaly is furthest from the rebar layer (toward the surface) since the wave scatters from the anomaly before the rebar layer. Error is the highest when the anomaly is close to and below the rebar layer since the wave scatters from the rebar layer before the anomaly Analysis presented here is computed automatically. In the delamination case the rebar layer was calculated to be a grid point (0.5”) higher than the actual depth When the contrast scatterers Technology Transfer: Potential technology transfer opportunities include extraction of additional information from GPR data and improved identification and quantification of subsurface anomalies. The technique of replacing the rebar References: 1. Handbook for GPR Inspection of Road Structures, Geophysical Survey Systems, Inc., December 2005. Total Scattering from anomaly Scattering from anomaly due to rebar Error if assumed that no rebar present in model * Cannot be determined without having identified the position and size of anomaly. Rebar layer is too high Rebar layer is too low depth. When the rebar depth is incorrect, it is obvious by the horizontal stripes in the total scattering from the anomaly. with hard point source excitations fed by the incident background field recorded at their locations performs well on synthetic data and establishes a strategy for improved GPR signal processing in the field, as well providing information required for the future application of inversion methods. The work presented here may be of interest to companies such as Relevant Publications: 2. K. Arunachalam, V. Melapundi, L. Udpa, and S. Udpa, “Microwave NDT of Cement-based Materials Using Far-field Reflection Coefficients,” NDT&E International, vol. 39, pp. 585–593, 2006. 3. K. Belli, C. Rappaport, and S. Wadia-Fascetti, “Forward Time Domain Ground Penetrating Radar Modeling of Scattering from Anomalies in the Presence of Steel Reinforcements,” Research in Nondestructive Evaluation, vol. 20, no 4, 2009. 4. K. Belli, Enhanced Analysis of Ground Penetrating Radar Bridge Deck Investigations Using Computational Modeling . Diss. Northeastern University, 2008. Geophysical Survey Systems, TransTech Systems and Infrasense who are focused on products and services associated with nondestructive testing of civil infrastructure. This work was supported in part by Gordon-CenSSIS, The Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC-9986821).