Real-Time Density and Thickness Estimation of Thin Asphalt Pavement Overlay During Compaction Using Ground Penetrating Radar Data

2019 ◽  
Vol 41 (3) ◽  
pp. 431-445 ◽  
Author(s):  
Siqi Wang ◽  
Shan Zhao ◽  
Imad L. Al-Qadi
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Asphalt Pavement ◽  
Radar Data ◽  
Thickness Estimation ◽  
Pavement Overlay ◽  
Ground Penetrating ◽  
Time Density

scholarly journals On the Introduction of Canny Operator in an Advanced Imaging Algorithm for Real-Time Detection of Hyperbolas in Ground-Penetrating Radar Data

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Urban Areas ◽  
Radar Data ◽  
Detection Algorithm ◽  
Advanced Imaging ◽  
Canny Operator ◽  
Imaging Algorithm ◽  
Canny Edge ◽  
Ground Penetrating

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.

Real-Time Monitoring of Asphalt Concrete Pavement Density during Construction using Ground Penetrating Radar: Theory to Practice

2019 ◽  
Vol 2673 (5) ◽  
pp. 329-338 ◽  
Author(s):  
Siqi Wang ◽  
Shan Zhao ◽  
Imad L. Al-Qadi
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Asphalt Concrete ◽  
Linear Optimization ◽  
Field Tests ◽  
The United States ◽  
Construction Process ◽  
Pavement Construction ◽  
Ground Penetrating ◽  
Time Density

Accurate real-time density monitoring is crucial in quality control and quality assurance during the asphalt concrete (AC) pavement construction process. Ground penetrating radar (GPR) technology has shown great potential in the continuous real-time density prediction of AC pavement. However, it is not accepted as a routine method by transportation agencies in the United States due to the lack of validation under field testing conditions. In this study, three field tests were performed using GPR to estimate AC pavement density. The Al-Qadi-Lahouar-Leng model was used to predict the density from GPR signals. The reference scan method was used to remove the effect of surface moisture during construction. The gradient descent-based non-linear optimization method was used to reconstruct the overlapped GPR signals result from the use of thin AC overlay, which has been widely implemented as an AC pavement rehabilitation technique. Digital filtering and other signal processing methods were used to de-noise the signal. GPR results using the proposed methods were compared with field core data and nuclear gauge results. The results show that the proposed methods were effective in estimating in-situ AC pavement density using GPR. Continuous density estimation by installing GPR on the roller is suggested to provide real-time compaction monitoring during the AC pavement construction process.

scholarly journals Improvement of GPR-Based Rebar Diameter Estimation Using YOLO-v3

2021 ◽  
Vol 13 (10) ◽  
pp. 2011
Author(s):  
Sehwan Park ◽  
Jinpyung Kim ◽  
Kyoyoung Jeon ◽  
Junkyeong Kim ◽  
Seunghee Park
Keyword(s):  
Real Time ◽  
Ground Penetrating Radar ◽  
Recognition Performance ◽  
Human Life ◽  
Radar Data ◽  
Prediction Methods ◽  
Reinforcing Bar ◽  
Naked Eye ◽  
Earthquake Resistant ◽  
Ground Penetrating

As the frequency of earthquakes has increased in Korea in recent years, designing earthquake-resistant facilities has been increasingly emphasized. Structures constructed with rebars are vulnerable to shaking, which reduces their seismic performance and may result in damage to human life and property. Because the construction of facilities requires the maintenance of sub-constructions, such as by cutting rebars or compensating for missing rebars, information on rebar diameter is required. In this study, the YOLO-v3 algorithm, which has the fastest object recognition performance, was applied to the structural correction data, and a basic experiment was conducted in the air to predict the diameter of rebars in a facility, in real time based on ground-penetrating radar data. The reason for using the YOLO-v3 algorithm is that in the case of GPR data that change slightly according to the diameter of the reinforcing bar, it is difficult to discriminate with the naked eye, and the result may change depending on the inspector. The model achieved a higher accuracy than conventional rebar detection and diameter prediction methods. In addition, the possibility of real-time rebar diameter prediction during construction, using the proposed method, was verified.

Real‐time object detection using power spectral density of ground‐penetrating radar data

2019 ◽  
Vol 26 (6) ◽  
pp. e2354 ◽  
Author(s):  
Abolfazl Saghafi ◽  
Sajad Jazayeri ◽  
Sanaz Esmaeili ◽  
Chris P. Tsokos
Keyword(s):  
Spectral Density ◽  
Object Detection ◽  
Real Time ◽  
Power Spectral Density ◽  
Ground Penetrating Radar ◽  
Radar Data ◽  
Power Spectral ◽  
Ground Penetrating

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating

Archive of ground penetrating radar data collected during USGS field activity 13BIM01—Dauphin Island, Alabama, April 2013

Data Series ◽  
10.3133/ds982 ◽  
2016 ◽  
Author(s):  
Arnell S. Forde ◽  
Christopher G. Smith ◽  
Billy J. Reynolds
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Field Activity ◽  
Ground Penetrating

scholarly journals Ice thickness distribution of all Swiss glaciers based on extended ground-penetrating radar data and glaciological modeling

2021 ◽  
pp. 1-19
Author(s):  
Melchior Grab ◽  
Enrico Mattea ◽  
Andreas Bauder ◽  
Matthias Huss ◽  
Lasse Rabenstein ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Thickness Distribution ◽  
Radar Data ◽  
Tourism Industry ◽  
Swiss Alps ◽  
Ice Thickness ◽  
Hazard Management ◽  
Bed Topography ◽  
Ice Volume ◽  
Ground Penetrating

Abstract Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

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