TY - JOUR
T1 - Factors Impacting Monitoring Asphalt Pavement Density by Ground Penetrating Radar
AU - Wang, Siqi
AU - Al-Qadi, Imad L.
AU - Cao, Qingqing
N1 - This publication is based on the results conducted in cooperation with the Illinois Center for Transportation, Illinois Department of Transportation (IDOT), U.S. Department of Transportation, and Federal Highway Administration. The authors would like to acknowledge the assistance provided by many individuals, including Shan Zhao, Michael Johnson, Greg Renshaw and Shenghua Wu. The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of Illinois Center for Transportation or Illinois Department of Transportation. This paper does not constitute a standard, specification, or regulation.
PY - 2020/10
Y1 - 2020/10
N2 - Real-time asphalt concrete (AC) pavement density monitoring is important for quality control (QC) and quality assurance (QA) purposes, because it allows correction during the compaction process. Ground penetrating radar (GPR) is capable of providing real-time AC mixture density prediction using the Al-Qadi, Lahouar, and Leng (ALL) density prediction model. However, noise sources, such as surface moisture and vibrations, may jeopardize the AC density prediction accuracy. This study proposes a mean reflection coefficient algorithm and digital filter design method to remove the surface moisture and smooth the density profile. In the mean reflection algorithm, the frequency-select bandwidth was selected as 40–70% of the actual peak frequency in the magnitude spectrum through the simulation studies. White Gaussian noise was added in the models for robustness testing. In the digital filter design method, the magnitude spectrum of the GPR predicted density profile was analyzed to decide filter types and corresponding parameters. Thresholding method was used to remove abnormal values, and window-based finite impulse response (FIR) filters were used to smooth the density profile. Lab-controlled and field tests were performed for both algorithms. Estimated aggregate dielectric constant was used to predict pavement density. A sensitivity analysis was performed to evaluate the effect of different aggregate dielectric constant on density (or air void). For surface moisture effect removal, mean reflection coefficient algorithm may be utilized to reconstruct dielectric constant values at an error less than 4%. This algorithm is independent of the antenna central frequency. For the density profile smoothing during continuous GPR survey, results show that various filter types have comparable smoothing performances. For the effect of aggregate dielectric constant on density prediction, sensitivity analysis shows that when aggregate dielectric constant values changes from 6.5 to 7, the predicted air void increases from 2.5% to 6.3%. This indicates the importance of an accurate aggregate dielectric constant estimate when applying ALL model for pavement density predictions; hence, aggregate dielectric constant estimate must be utilized.
AB - Real-time asphalt concrete (AC) pavement density monitoring is important for quality control (QC) and quality assurance (QA) purposes, because it allows correction during the compaction process. Ground penetrating radar (GPR) is capable of providing real-time AC mixture density prediction using the Al-Qadi, Lahouar, and Leng (ALL) density prediction model. However, noise sources, such as surface moisture and vibrations, may jeopardize the AC density prediction accuracy. This study proposes a mean reflection coefficient algorithm and digital filter design method to remove the surface moisture and smooth the density profile. In the mean reflection algorithm, the frequency-select bandwidth was selected as 40–70% of the actual peak frequency in the magnitude spectrum through the simulation studies. White Gaussian noise was added in the models for robustness testing. In the digital filter design method, the magnitude spectrum of the GPR predicted density profile was analyzed to decide filter types and corresponding parameters. Thresholding method was used to remove abnormal values, and window-based finite impulse response (FIR) filters were used to smooth the density profile. Lab-controlled and field tests were performed for both algorithms. Estimated aggregate dielectric constant was used to predict pavement density. A sensitivity analysis was performed to evaluate the effect of different aggregate dielectric constant on density (or air void). For surface moisture effect removal, mean reflection coefficient algorithm may be utilized to reconstruct dielectric constant values at an error less than 4%. This algorithm is independent of the antenna central frequency. For the density profile smoothing during continuous GPR survey, results show that various filter types have comparable smoothing performances. For the effect of aggregate dielectric constant on density prediction, sensitivity analysis shows that when aggregate dielectric constant values changes from 6.5 to 7, the predicted air void increases from 2.5% to 6.3%. This indicates the importance of an accurate aggregate dielectric constant estimate when applying ALL model for pavement density predictions; hence, aggregate dielectric constant estimate must be utilized.
KW - Asphalt concrete pavement
KW - Ground penetrating radar
KW - Noise cancellation
KW - Real-time compaction monitoring
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U2 - 10.1016/j.ndteint.2020.102296
DO - 10.1016/j.ndteint.2020.102296
M3 - Article
AN - SCOPUS:85084385614
SN - 0963-8695
VL - 115
JO - NDT and E International
JF - NDT and E International
M1 - 102296
ER -