Integration of Traffic Speed Deflectometer and Ground-Penetrating Radar for Network-Level Roadway Structure Evaluation

2017 ◽  
Vol 2639 (1) ◽  
pp. 55-63 ◽  
Author(s):  
Kenneth Maser ◽  
Pete Schmalzer ◽  
William Shaw ◽  
Adam Carmichael
Keyword(s):  
Ground Penetrating Radar ◽  
Layer Structure ◽  
Project Planning ◽  
Preliminary Review ◽  
Remaining Life ◽  
Wide Range ◽  
Traffic Speed ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Structure Evaluation

The project has focused on the East Idaho Loop Corridor (EILC), representing 518 mi of primary roadways covering a wide range of geographic conditions. The Idaho Transportation Department (TD) has pursued this effort to support future project planning and design efforts and advance the management of its assets into a more efficient, best-first set of priorities. The EILC was surveyed with a traffic speed deflectometer (TSD) and with ground-penetrating radar (GPR). After preliminary review of the TSD data, segments were selected for falling weight deflectometer (FWD) testing to confirm patterns observed in the TSD data and to adapt FWD analysis methods to the TSD data. Ninety-nine borings were taken to confirm the pavement layer structure and verify thickness calculations. The Idaho TD data were analyzed at a 10-m interval with the GPR layer thickness data to determine subgrade modulus, pavement modulus, and structural number. These values were then used to estimate overlay requirements and, given traffic projections, to calculate the remaining life as a continuous function of roadway position. The data were incorporated into a spatial geodatabase that provides the Idaho TD with a convenient means to visualize and evaluate the overall condition of the network down to the detail of its individual segments. The segments were subdivided into homogenous subsections on the basis of remaining life, and these subsegments were used for identifying and programming rehabilitation projects. The level of condition detail available at the subsegment level allows for pavement design and for scoping restoration projects.

Effect of antennas on velocity estimates obtained from crosshole GPR data

Geophysics ◽  
2005 ◽  
Vol 70 (5) ◽  
pp. K39-K42 ◽  
Author(s):  
James D. Irving ◽  
Rosemary J. Knight
Keyword(s):  
Numerical Modeling ◽  
Ground Penetrating Radar ◽  
Significant Fraction ◽  
High Angle ◽  
Standard Assumption ◽  
Tomographic Images ◽  
Wide Range ◽  
Antenna Length ◽  
Ground Penetrating

To obtain tomographic images with the highest possible resolution from crosshole ground-penetrating radar (GPR) data, raypaths covering a wide range of angles between the boreholes are required. In practice, however, the inclusion of high-angle ray data in crosshole GPR inversions often leads to tomograms so dominated by inversion artifacts that they contain little reliable subsurface information. Here, we investigate the problems that arise from the standard assumption that all first-arriving energy travels directly between the centers of the antennas. Through numerical modeling, we show that this assumption is often incorrect at high transmitter-receiver angles and can lead to significant errors in tomographic velocity estimates when the antenna length is a significant fraction of the borehole spacing.

Use of COST networking tools to achieve the objectives of a COST Action, enhance its impact, and maximize the benefits of its Members – challenges and lessons learnt in COST Action TU1208

2020 ◽  
Author(s):  
Aleksandar Ristic ◽  
Lara Pajewski ◽  
Miro Govedarica ◽  
Milan Vrtunski
Keyword(s):  
Ground Penetrating Radar ◽  
Science Communication ◽  
Civil Engineering ◽  
Public Awareness ◽  
Host Institution ◽  
Full Time ◽  
Cost Action ◽  
Lessons Learnt ◽  
Wide Range ◽  
Ground Penetrating

<p>Scientists and experts participating in COST Actions can benefit from a wide range of COST networking tools. Meetings, workshops, conferences and training schools can be organized. Short-term scientific missions (STSM) can be funded: these are exchange visits where an Action Member spends five days up to six months abroad, in a host institution; the aim of STSMs is to foster collaboration between institutions and sharing of new techniques that may not be available in a participant’s home institution. COST also funds dissemination and communication of Action’s outcomes within research communities and beyond. Finally, conference grants for early-career researchers from Inclusiveness Target Countries (ITC) aim at helping participants from ITC to attend international science and technology related conferences that are not organised by COST Actions.</p><p>In this presentation, we discuss the challenges and lessons learnt in COST Action TU1208 “Civil engineering applications of ground penetrating radar” [1] while using COST networking tools to fulfill the objectives of the Action, enhance its impact, and maximize the benefits of its Members. We consider one tool at a time focusing on the obstacles that we encountered and how we overcame them, as well as giving hints on how the Action and its Members made the most from the use of the tool. We describe how the use of the tools changed during the Action’s lifetime. </p><p>COST networking tools can of course be used in a customary way and they are all extremely frutiful. More creative solutions can be implemented too, to keep Members engaged or achieve particular goals. Therefore, this presentation continues with examples of less-common exploitations of the tools in TU1208. For instance, we used the “Meeting” tool for the organization of a series of science communication initiatives aimed at increasing public awareness about ground penetrating radar capabilities and applications and at establishing a dialogue with policymakers, stakeholders and end-users of our research (TU1208 GPR RoadShow [2]); the Roadshow included non-scientific workshops, practical demonstrations, and a series of educational activities with children and citizens. We repeatedly exploited the “Meeting” tool also for one week gatherings with a small number of Members, where we worked full-time together at bringing forward specific Action’s activities, one of the challenges of COST Actions being the lack of funds to finance research and the difficulty to “make Members work” for the Action when they are at their home institutions.</p><p>We hope that recently started Actions can build upon our experience.</p><p> </p><p>[1] L. Pajewski, A. Benedetto, X. Dérobert, A. Giannopoulos, A. Loizos, G. Manacorda, M. Marciniak, C. Plati, G. Schettini, I. Trinks, "Applications of Ground Penetrating Radar in Civil Engineering – COST Action TU1208," Proc. 7th IWAGPR, 2013, Nantes, France, pp. 1-6, doi.org/10.1109/IWAGPR.2013.6601528</p><p>[2] L. Pajewski, H. Tõnisson, K. Orviku, M. Govedarica, A. Ristić, V. Borecky, S. S. Artagan, S. Fontul, and K. Dimitriadis, “TU1208 GPR Roadshow: Educational and promotional activities carried out by Members of COST Action TU1208 to increase public awareness on the potential and capabilities of the GPR technique,” Ground Penetrating Radar, Volume 2(1), March 2019, pp. 67-109, doi.org/10.26376/GPR2019004</p>

scholarly journals Analysis of the capabilities of low frequency ground penetrating radar for cavities detection in rough terrain conditions: The case of Divača cave, Slovenia

2012 ◽  
Vol 41 (1) ◽  
Author(s):  
Andrej Gosar
Keyword(s):  
Ground Penetrating Radar ◽  
Open Space ◽  
Low Frequency ◽  
Processing Parameters ◽  
Rough Terrain ◽  
Depth Range ◽  
Shallow Subsurface ◽  
Cave Entrance ◽  
Wide Range ◽  
Ground Penetrating

High frequency ground penetrating radar (GPR) is usually applied for cavities detection in a shallow subsurface of karst areas to prevent geotechnical hazards. For specific projects, such as tunnel construction, it is important to detect also larger voids at medium depth range. However, dimensions of classical rigid low frequency antennas seriously limit their applicability in a rough terrain with dense vegetation commonly encountered in a karst. In this study recently developed 50 MHz antennas designed in a tube form were tested to detect cave gallery at the depth between 12 m and 60 m. The Divaca cave was selected because of a wide range of depths under the surface, possibility of unknown galleries in the vicinity and a rough terrain surface typical for Slovenian karst. Seven GPR profiles were measured across the main gallery of the cave and additional four profiles NE of the cave entrance where no galleries are known. Different acquisition and processing parameters were analysed together with the data resolution issues. The main gallery of the cave was clearly imaged in the part where the roof of the gallery is located at the depth from 10 m to 30 m. The width of the open space is mainly around 10 m. Applied system was not able to detect the gallery in the part where it is located deeper than 40 m, but several shallower cavities were discovered which were unknown before. The most important result is that the profiles acquired NE of the cave entrance revealed very clearly the existence of an unknown gallery which is located at the depth between 15 m and 22 m and represents the continuation of the Divaca cave. Access to this gallery is blocked by the sediment fill in the entrance shaft of the cave. The results of the study are important also for future infrastructure projects which will involve construction of tunnels through karstified limestone and for speleological investigations to direct the research efforts.Keywords: ground penetrating radar, cavity detection, spatial resolution, limestone, Divača cave.

scholarly journals Estimation of Flexible Pavement Structural Capacity Using Machine Learning Techniques

2020 ◽  
Author(s):  
Nader Karballaeezadeh ◽  
Hosein Ghasemzadeh Tehrani ◽  
Danial Mohammadzadeh S. ◽  
Shahaboddin Shamshirband
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Ground Penetrating Radar ◽  
Flexible Pavement ◽  
Falling Weight Deflectometer ◽  
Machine Learning Methods ◽  
Back Calculation ◽  
Ground Penetrating ◽  
Falling Weight ◽  
Surface Deflections

The most common index for representing structural condition of the pavement is the structural number. The current procedure for determining structural numbers involves utilizing falling weight deflectometer and ground-penetrating radar tests, recording pavement surface deflections, and analyzing recorded deflections by back-calculation manners. This procedure has two drawbacks: 1. falling weight deflectometer and ground-penetrating radar are expensive tests, 2. back-calculation ways has some inherent shortcomings compared to exact methods as they adopt a trial and error approach. In this study, three machine learning methods entitled Gaussian process regression, m5p model tree, and random forest used for the prediction of structural numbers in flexible pavements. Dataset of this paper is related to 759 flexible pavement sections at Semnan and Khuzestan provinces in Iran and includes “structural number” as output and “surface deflections and surface temperature” as inputs. The accuracy of results was examined based on three criteria of R, MAE, and RMSE. Among the methods employed in this paper, random forest is the most accurate as it yields the best values for above criteria (R=0.841, MAE=0.592, and RMSE=0.760). The proposed method does not require to use ground penetrating radar test, which in turn reduce costs and work difficulty. Using machine learning methods instead of back-calculation improves the calculation process quality and accuracy.

Radius estimation of subsurface cylindrical objects from ground-penetrating-radar data using full-waveform inversion

Geophysics ◽  
2018 ◽  
Vol 83 (6) ◽  
pp. H43-H54 ◽  
Author(s):  
Tao Liu ◽  
Anja Klotzsche ◽  
Mukund Pondkule ◽  
Harry Vereecken ◽  
Yi Su ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Waveform Inversion ◽  
Radar Data ◽  
Full Waveform Inversion ◽  
Data Inversion ◽  
Full Waveform ◽  
Wide Range ◽  
Object Parameters ◽  
Ground Penetrating ◽  
Effective Source

Ray-based radius estimations of subsurface cylindrical objects such as rebars and pipes from ground-penetrating-radar (GPR) measurements are not accurate because of their approximations. We have developed a novel full-waveform inversion (FWI) approach that uses a full-waveform 3D finite-difference time-domain (FDTD) forward-modeling program to estimate the radius including other object parameters. By using the full waveform of the common-offset GPR data, the shuffled complex evolution (SCE) approach is able to reliably extract the radius of the subsurface cylindrical objects. A combined optimization of radius, medium properties, and the effective source wavelet is necessary. Synthetic and experimental data inversion returns an accurate reconstruction of the cylinder properties, medium properties, and the effective source wavelet. Combining FWI of GPR data using SCE and a 3D FDTD forward model makes the approach easily adaptable for a wide range of other GPR FWI approaches.

Prediction of Remaining Life of Asphalt Pavement with Falling-Weight Deflectometer Multiload-Level Deflections

2003 ◽  
Vol 1860 (1) ◽  
pp. 48-56 ◽  
Author(s):  
Hee Mun Park ◽  
Y. Richard Kim
Keyword(s):  
Flexible Pavements ◽  
Fatigue Cracking ◽  
Pavement Performance ◽  
Falling Weight Deflectometer ◽  
Remaining Life ◽  
Performance Models ◽  
Response Models ◽  
Wide Range ◽  
Pavement Response ◽  
Falling Weight

The development of prediction methods for the remaining life of flexible pavements using falling-weight deflectometer (FWD) multiload-level deflections is presented. Pavement response models and pavement performance models were used in developing this procedure. The pavement response models were designed to predict critical pavement responses from surface deflections and deflection basin parameters. The pavement performance models were used to develop the relationships between critical pavement responses obtained from pavement response models and actual pavement performance. Pavement distress data and FWD multiload-level deflection data obtained from the Long-Term Pavement Performance database were used to verify the performance prediction procedure. It was found that the performance of fatigue cracking can be predicted using the proposed procedure except for pavements with high and rapidly increasing cracking in wet-freeze regions. Such trends may be due to environment-induced distresses such as low-temperature cracking, permanent deformation of unbound layers, or both, during the spring thaw period. Predicted rut depths using both single-load and multiload-level deflections show good agreement with measured rut depths over a wide range of rutting potentials. However, the procedure using single–load-level deflections consistently underpredicts the rut depths. This observation demonstrates that the rutting prediction procedure using multiload-level deflections can estimate an excessive level of rutting quite well and thus improve the prediction quality of rutting potential in flexible pavements.

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