GI2.1

ML has been successfully applied to solve many NDT-CE tasks. This is usually demonstrated with performance metrics that evaluate the model as a whole based on a given set of data. However, since in most cases the creation of reference data is extremely expensive, the data used is generally much sparser than in other areas, such as e-commerce. As a result, performance indicators often do not reflect the practical applicability of the ML model. Estimates that quantify transferability from one case to another are necessary to meet this challenge and pave the way for real world applications.

In this contribution we invetigate the uncertainty of ML in new NDT-CE scenarios. For this purpose, we have extended an existing training data set for the classification of corrosion damage by a new case study. Our data set includes half-cell potential mapping and ground-penetrating radar measurements. The measurements were performed on large-area concrete samples with built-in chloride-induced corrosion of reinforcement. The experiment simulated the entire life cycle of chloride induced exposed concrete components in the laboratory. The unique ability to monitor deterioration and initiate targeted corrosion initiation allowed the data to be labelled - which is crucial to ML. To investigate transferability, we extend our data by including new design features of the test specimen and environmental conditions. This allows to express the change of these features in new scenarios as uncertainties using statistical methods. We compare different sampling and statistical distribution-based approaches and show how these methods can be used to close knowledge gaps of ML models in NDT.

How to cite: Völker, C., Kruschwitz, S., and Benner, P.: Uncertainty quantification for a sparse machine learning (ML) data set in non-destructive testing in civil engineering (NDT-CE), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8798, https://doi.org/10.5194/egusphere-egu21-8798, 2021.

Increasing agricultural production challenges, such as climate change, environmental concerns, energy demands, and growing expectations from consumers triggered the necessity for innovation using data-driven approaches such as visual analytics. Although the visual analytics concept was introduced more than a decade ago, the latest developments in the data mining capacities made it possible to fully exploit the potential of this approach and gain insights into high complexity datasets (multi-source, multi-scale, and different stages). The current study focuses on developing prototypical visual analytics for an apple variety testing program in South Tyrol, Italy. Thus, the work aims (1) to establish a visual analytics interface enabled to integrate and harmonize information about apple variety testing and its interaction with climate by designing a semantic model; and (2) to create a single visual analytics user interface that can turn the data into knowledge for domain experts. 

This study extends the visual analytics approach with a structural way of data organization (ontologies), data mining, and visualization techniques to retrieve knowledge from an extensive collection of apple variety testing program and environmental data. The prototype stands on three main components: ontology, data analysis, and data visualization. Ontologies provide a representation of expert knowledge and create standard concepts for data integration, opening the possibility to share the knowledge using a unified terminology and allowing for inference. Building upon relevant semantic models (e.g., agri-food experiment ontology, plant trait ontology, GeoSPARQL), we propose to extend them based on the apple variety testing and climate data. Data integration and harmonization through developing an ontology-based model provides a framework for integrating relevant concepts and relationships between them, data sources from different repositories, and defining a precise specification for the knowledge retrieval. Besides, as the variety testing is performed on different locations, the geospatial component can enrich the analysis with spatial properties. Furthermore, the visual narratives designed within this study will give a better-integrated view of data entities' relations and the meaningful patterns and clustering based on semantic concepts.

Therefore, the proposed approach is designed to improve decision-making about variety management through an interactive visual analytics system that can answer "what" and "why" about fruit-growing activities. Thus, the prototype has the potential to go beyond the traditional ways of organizing data by creating an advanced information system enabled to manage heterogeneous data sources and to provide a framework for more collaborative scientific data analysis. This study unites various interdisciplinary aspects and, in particular: Big Data analytics in the agricultural sector and visual methods; thus, the findings will contribute to the EU priority program in digital transformation in the European agricultural sector.

This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 894215.

How to cite: Chuprikova, E., Mejia Aguilar, A., and Monsorno, R.: An Ontology-based Visual Analytics for Apple Variety Testing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15804, https://doi.org/10.5194/egusphere-egu21-15804, 2021.

The increase of the metropolises stresses the urban areas and intensive planning works is necessary. Therefore, the development of new technologies and methodologies able to explore the subsoil and manage its resources in urban areas becomes an important source in terms of saving time and money. In the last decade, a new subdiscipline in the Applied Geophysics started: Urban Geophysics (Lapenna, 2017). Urban Geophysics analyzes the contribute, in terms of limits and potentialities, that geophysical methodologies can give for providing useful information about the subsoil, environment, buildings and civil infrastructures and supporting the public administrations in planning interventions in urban scenarios.

This work introduces a laboratory test, that was performed at the Hydrogeosite CNR-IMAA laboratory of Marsico Nuovo (Basilicata region, Italy). The test consisted in a multisensor geophysical application on an analogue engineering model. Thanks to the possibility to work in laboratory conditions, a detailed knowledge of the structure was available, providing great advantages for assess the capability of the geophysical methodologies for analyze engineering issues, regarding the characterization of the infrastructural critical zone placed at the interface soil-structure. For this purpose, geoelectrical and electromagnetic methodologies, including Cross hole Electrical Resistivity Tomography and Ground Penetrating Radar, were used to characterize the geometry of the foundation structures and the disposition of the rebar for the reinforced concrete frame. Finally, new geophysical approaches were applied in order to define the corrosion rate of reinforcement.

How to cite: Fornasari, G., Capozzoli, L., De Martino, G., Giampaolo, V., and Rizzo, E.: Geophysical characterization of an engineering infrastructure: laboratory tests., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4508, https://doi.org/10.5194/egusphere-egu21-4508, 2021.

Nowadays, non-invasive sensing technologies working at different spatial scales represent a recognized tool to support archaeological researches, because their deployment and cooperative use allow detection and localization of buried ruins before performing excavation. Therefore, they get significant advantages in planning the stratigraphic assays, while reducing costs and times, and support holistic approaches where cultural heritage management, protection and fruition aspects are considered under a unified context.

As a further example among those available in literature, this communication summarizes a successful case study carried out at the Archaeological site of Paestum, sited in the southern Italy [1].

Based on the analysis of aerial imagery and several unexpected archaeological findings, terrestrial measurement campaigns, involving magnetometer (MGA) [2] and ground penetrating radar (GPR) [3] methodologies, were carried out in the northwest quarter of the ancient city near the fortification wall and few meters away from the gate of Porta Marina. As detailed in [4], the MGA was exploited to investigate a large subsurface area in a relatively short time and allowed the identification of the most significant archaeological anomalies, by accounting for the variations of the earth magnetic field due to the different magnetic susceptibilities of construction materials and the magnetic characteristics of the shallow subsoil. The georeferenced MGA image was exploited to select the area requiring a further and more detailed survey, which was performed by means of GPR. Then, GPR data were processed by means of a microwave tomography based approach [4], which allowed a high resolution three dimensional reconstruction of buried targets starting from the electromagnetic field that they backscatter when illuminated by a known incident field. By doing so, detailed information about depth, shape, and orientation of the buried targets were retrieved and an impressive visualization of the the basement of the structure was achieved.

The area is currently under excavation and the initial discovered ruins fully confirm the hypotheses formulated on the basis of the elements found on the surface, the photo interpretations and geophysical investigations. The proposed reconstructive hypothesis of the building as a whole is a stylobate of 10.83 m x 6.80 on which 4 x 7 columns were arranged, with a significantly increased intercolumniation on the short sides (2.02 m) compared to the long sides (1.68 m).

[1] https://www.museopaestum.beniculturali.it/?lang=en

[2] A. Aspinall, C. Gaffney, A. Schmidt, A Magnetometry for archaeologists. Geophysical methods for archaeology, Altamira Press, Lanham, (2008).

[3] D. J. Daniels, Ground penetrating radar, IET, (2004).

[4] Capozzoli, L.; Catapano, I.; De Martino, G.; Gennarelli, G.; Ludeno, G.; Rizzo, E.; Soldovieri, F.; Uliano Scelza, F.; Zuchtriegel, G. The Discovery of a Buried Temple in Paestum: The Advantages of the Geophysical Multi-Sensor Application. Remote Sens. 2020, 12, 2711.

How to cite: Catapano, I., Capozzoli, L., Ludeno, G., Gennarelli, G., De Martino, G., Rizzo, E., Scelza, F. U., Zuchtriegel, G., and Soldovieri, F.: Multi-sensing geophysical surveys at the Archaeological Park of Paestum: the discovery of a small Doric temple, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13191, https://doi.org/10.5194/egusphere-egu21-13191, 2021.

Ground Penetrating Radar has widely proven to be an effective tool for archaeological purposes [1, 2]. Our contribution concerns a geophysical experimental activity carried out in the Complex of Villa dei Sette Bassi, an archaeological site located in Rome, Italy.

In particular, the area was hypothesized to be interested by the track of the ancient via Latina [3, 4], which was the main internal route that connected Rome with the ancient Region of Campania; it ran parallel to the Via Appia, but it was built way before it.

The historical evolution of this landscape has seen great changes since the Middle Ages with a new economy that designed new parcels, new land uses and the stripping of building material from ancient remains: activities that have profoundly altered the territory in its appearance and functioning but also its road network. The uncontrolled building development, has over time hidden the ancient road network, today witnessed only by decontextualized monuments immersed in modern urbanization. Accordingly, great portion of the ancient via Latina remains still buried.

This works reports on the outcomes of the geophysical tests conducted within the area of Villa dei Sette Bassi, with the specific goal of locating the buried track of the via Latina. The survey has been carried out by using multi-frequency ground penetrating radar (GPR) systems with different central frequencies. In detail, a preliminary low frequency analysis was conducted over the entire area that was indicated to be interested by the hidden remains by literary sources, to the intent of detecting the position of the buried road with higher accuracy. Based on the this, a second survey with higher resolution was conducted over a regularly spaced grid.

As a result, GPR tests have returned a coherent reflection pattern that is reasonably representative of a road subgrade/embankment. According to the preliminary archaeological interpretations, these results are most likely related to the historical track of via Latina, even though inspection pits are required in order to verify these assumptions.

In conclusion, GPR demonstrated a great applicability to archaeological purposes, i.e. to detect buried remains and to interpret the function of buried structures, despite the reliability and productivity of the data interpretation are strongly influenced by the expertise of both the geophysicists and the archaeologists involved.

References

[1] Bianchini Ciampoli, L., Santarelli, R., Loreti, E.M., Ten, A., Benedetto, A. {2020} “Structural detailing of buried Roman baths through GPR inspection”, Archaeological Prospections, In Press.

[2] Milligan, R., & M., Atkin, {1993}. The use of ground-probing radar within a digital environment on archaeological sites, in Andresen, J., Madsen, T. and Scollar, I., eds., Computing the Past: Computer Application and Quantitative methods in Archaeology: Aarhus, Denmark, Aarhus University Press, pp. 285–291.

[3] Monti, P.G. {1995} “Via Latina”, Istituto Poligrafico e Zecca dello Stato. Libreria dello Stato Roma.

[4] Rea, R., Montella, F., Egidi, R.. Alteri, R., Diamanti, F., Mongetta, M., {2005} “Via Latina”, in Lexicon Topographicum Urbis Romae, III, pp. 133-202, Quasar ed., Roma.

How to cite: Bianchini Ciampoli, L., Benedetto, A., Ten, A., Amici, C. M., and Santarelli, R.: Retrieving signs of buried historical roads by GPR: preliminary results from Villa dei Sette Bassi in Rome, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16316, https://doi.org/10.5194/egusphere-egu21-16316, 2021.

The estimation of the bulk permittivity of heterogeneous mixtures is of great interest for many Ground Penetrating Radar (GPR) and electromagnetic sensing applications [1], [2]. The most used method for estimating the bulk permittivity is the Complex Refractive Index Model (CRIM). The simplicity of this method is one its advantages however, the accuracy of the permittivity estimation has not been tested. Here, the CRIM model’s shape factor is examined and optimised in order to achieve a more accurate concrete bulk permittivity estimation. The concrete components are aggregate particles, cement particles, air-voids and moisture content; and they are randomly distributed with different volume percentages to produce various combinations. These combinations are modelled using the Finite-Difference Time-Domain (FDTD) method as it is an accurate and computationally efficient method [3]. The numerical modelling is then used to predict the bulk permittivity allowing to fine-tune CRIM model’s shape factor. The models are modelled in 3D and a GSSI-like antenna structure is used as the transmitting source [4]. The permittivity estimation uses an accurate time-zero method, which increases the accuracy of the estimated bulk permittivity hence, the shape factor [5], [6]. The results have shown that the optimised CRIM model over-performs the original CRIM model shape factor therefore, a better and more accurate bulk permittivity estimation is achieved for concrete mixtures.

References

[1] Daniels, D. J., (2004), Ground Penetrating Radar, 2nd ed. London, U.K., Institution of Engineering and Technology.

[2] Annan, A. P., (2005), Ground Penetrating Radar,  in Investigations in Geophysics, Society of Exploration Geophysicists, pp. 357-438.

[3] Taflove, A., Hagness, S. C., (2005), Computational electromagnetic: The Finite-Difference Time-Domain Method, Artech House, Norwood.

[4] Warren, C., & Giannopoulos, A., (2011), Creating Finite-Difference Time-Domain Models of Commercial Ground Penetrating Radar Antenna Using Taguchi’s Optimization Method, Geophysics, 76(2), G37-G47.

[5] Zadhoush, H., Giannopoulos, A., Giannakis, I., (2020), Optimising GPR time-zero adjustment and two-way travel time wavelet measurement using a realistic 3D numerical model, Near Surface Geophysics, Under review (Minor revisions).

[6] Zadhoush, H., (2020), Numerical Modelling of Ground Penetrating Radar for Optimization of the Time-zero Adjustment and Complex Refractive Index Model, PhD Thesis Submitted at The University of Edinburgh.

How to cite: Zadhoush, H., Giannopoulos, A., and Giannakis, I.: A Revised Complex Refractive Index Model for Inferring the Permittivity of Heterogeneous Concrete Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16517, https://doi.org/10.5194/egusphere-egu21-16517, 2021.

Ground penetrating radar (GPR) is a high-resolution geophysical non-destructive detection method, which is widely used in near surface target detection, and has been successfully applied in urban construction and geotechnical engineering. In urban life, underground pipelines undertake important missions such as energy transmission and information transmission. As the basic data of smart city, the acquisition of spatial location information of underground pipelines depends on geophysical detection data such as GPR. The traditional recognition and interpretation of  GPR underground pipeline image mainly depends on and is seriously limited by the professional experience of the staff, which is very disadvantageous to the development of large-scale urban underground pipeline survey. To address this problem, according to the GPR reflection image characteristics of isolated targets such as underground pipelines, this paper proposes an intelligent recognition concept of isolated targets in GPR profile based on CBIR (Content-based image retrieval) According to Hash algorithm and improved vector K-means clustering analysis, the intelligent detection, automatic image sorting and recognition of underground pipeline target in GPR profile are realized. Finally, the pipeline material is judged by extracting the image brightness function of the middle trace in the recognition area. The application results of numerical simulation experiments and measured data show that this algorithm can effectively identify the hyperbolic characteristics of the pipeline in the GPR profile, and the identified area can accurately reflect the spatial location of the underground pipeline.

How to cite: Li, B. and Zhao, Y.: Intelligent recognition of underground pipeline From GPR image based on Hash algorithm, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11199, https://doi.org/10.5194/egusphere-egu21-11199, 2021.

Shallow-seismic surface wave and ground penetrating radar (GPR) are employed in a wide range of engineering and geosciences applications. Full-waveform inversion (FWI) of either seismic or multi-offset GPR data are able to provide high-resolution subsurface characterization and have received particular attention in the past decade. Those two geophysical methods are involved in the increasing requirements of comprehensive site and material investigations. However, it is still challenging to provide an effective integration between seismic data and electromagnetic data. In this paper, we investigated the joint petrophysical inversion (JPI) of shallow-seismic and multi-offset GPR data for more consistent imaging of near surface. As a bridge between the seismic parameters (P-wave velocity, S-wave velocity, and density) and GPR parameters (relative dielectric permittivity and electric conductivity), the petrophysical relationships with the parameters namely porosity and saturation are employed to link two data sets. We first did a sensitivity analysis of the petrophysical parameters to the seismic and GPR parameters and then determined an efficient integration of using shallow-seismic FWI to update porosity and GPR FWI to update saturation, respectively. A comparison of several parameterisation combinations shows that the seismic velocity parameterisation in shallow-seismic FWI and a modified logarithm parameterisation in GPR FWI works well in reconstructing reliable S-wave velocity and relative dielectric permittivity models, respectively. With the help from the petrophysical links, we realized JPI by transforming those well recovered parameters to the petrophysical parameters and then to other seismic and GPR parameters. A synthetic test indicates that, compared with the individual petrophysical inversion and individual FWI, JPI outperforms in simultaneously reconstructing all seismic, GPR, and petrophysical parameters with higher resolution and improved details. It is proved that JPI would be a potential data integration approach for the shallow subsurface investigation.

How to cite: Qin, T., Bohlen, T., and Pan, Y.: Joint petrophysical full-waveform inversion of the shallow-seismic and multi-offset GPR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14803, https://doi.org/10.5194/egusphere-egu21-14803, 2021.

In 2019, 3.1 billion Euro of damage was caused by piped water, accounting for the largest share (53%) of building insurance claims in Germany. In the event of damage, the accurate determination and localization of water ingress is essential to plan for and perform efficient renovations. Neutron probes are already applied successfully on building floors to localize the source of damage and other affected areas. However, additional information about the depth of moisture penetration can only be obtained by the destructive extraction of drilling cores, which is a time- and cost-intensive procedure. With its high sensitivity to water and fast measurement procedure, Ground Penetrating Radar (GPR) can serve as a suitable extension to the neutron probe, enabling more precise characterization of common forms of moisture damage.

In this research project, we study the influence of common types of moisture damage in differing floor constructions using GPR and a neutron probe. A measurement setup with interchangeable layers is used to vary the screed material (cement or anhydrite) and insulation material (Styrofoam, Styrodur, glass wool, perlite), as well as the respective layer thickness. Every configuration is measured for the following main cases: 1) dry state; 2) with a damaged insulation layer and 3) a damaged screed layer.

The evaluation is focused on the extraction of distinctive signal features for GPR, which can be used to classify the underlying case of damage. Furthermore, possible combinations of these features are investigated using multivariate data analysis and machine learning in order to evaluate the influence of different floor constructions.

To validate the developed methods, practical measurements on real damage cases in Germany are carried out and compared to reference data obtained from drilling cores.

How to cite: Klewe, T., Strangfeld, C., Ritzer, T., and Kruschwitz, S.: Classification of moisture damage in layered building floors with GPR and neutron probe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7437, https://doi.org/10.5194/egusphere-egu21-7437, 2021.

Abstract—Metro traffic in subsurface tunnels is under a rapid development in many cities in the recent decades. However, the voids and other concealed defects inside and/or behind the tunnel lining pose critical threat to the safety of the operating metro tunnels. Ground penetrating radar (GPR) is a non-destructive geophysical technique by transmitting electromagnetic (EM) waves and receiving the reflected signals. GPR has proved its capability in the detection of the existence of tunnel structural defects and anomalies. However, the voids are still hard to be recognized in a GPR image due to the strong scattering clutter caused by the dense steel bars reinforced inside the concrete lining [1]. In this paper, we analyze the propagations of EM waves through reinforce concrete segments of shield tunnels by finite difference time domain (FDTD) simulations and model test.  Firstly, a series of simulations results we have done, indicates that the center frequency of GPR ranges from 400 MHz to 600 MHz has a good penetration through the densely reinforced concrete lining. And the distance between the antennas and the surface of shield tunnel segments should be less than 0.2 m to ensure a good coupling of incident electromagnetic energy into the concrete structure. Then, to image the geometric features of the void behind the segment, reverse-time migration method is applied to the simulated GPR B-scan profile, which presents higher resolution results than the results by using the traditional diffraction stack migration (Figure 1) [2]. Finally, the field experiment results prove that a commercial GPR system operating at a center frequency of 600 MHz do detect a void behind the shield tunnel (Figure 2). The reflection from the void, which starts from the back interface of the segments and lasts over 20 ns, are significantly different from the reflections from the rebars (Figure 3). In summary, GPR has potential in the detection of voids behind the shield tunnel segment. More simulations and field experiments will be performed in the future.

Keywords—ground penetrating radar (GPR); shield tunnel; voids; reverse time migration (RTM)

Acknowledgement—this work was supported by Shenzhen Science and Technology program (grant number:KQTD20180412181337494).

Fig. 1 Numerical simulation of two segments of 2D shield tunnel. (a) numerical model, (b) simulated GPR B-scan profile, (c) migrated profile by using diffraction stack migration and (d) migrated profile by using reverse-time migration.

Fig. 2 One photo of the field experiment.

Fig. 3 GPR reflections from a void behind the segment of a subway tunnel

References

[1]     H. Liu, H. Lu, J. Lin, F. Han, C. Liu, J. Cui, B. F. Spencer, “Penetration Properties of Ground Penetrating Radar Waves through Rebar Grids” , IEEE Geoscience and Remote Sensing Letters ( DOI: 10.1109/LGRS.2020.2995670)

[2]          H. Liu, Z. Long, F. Han, G. Fang, Q. H. Liu, “Frequency-Domain Reverse-Time Migration of Ground Penetrating Radar Based on Layered Medium Green's Functions”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 11, no. 08, pp. 2957-2965, 2018.

How to cite: Liu, H., Lin, J., Meng, X., and Du, Y.: Detection of voids behind segments in the shield tunnels by using Ground Penetrating Radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14103, https://doi.org/10.5194/egusphere-egu21-14103, 2021.

Urban Resilience represents the capability of an urban system to preserve its features (in terms of public and private qualities and services) when shock events occur [1]. This topic is receiving an increasing interest for the climate change emergency which require innovative strategies for preserving the natural and anthropic resources present in the subsoil. In this framework, the Urban Geophysics could give a strong contribution to improving the knowledge of the critical issues affecting urban area [2].

One of the most interesting challenges is represented by the detection of ground collapse phenomena that can hardly reduce the safety and reliability of civil structures and infrastructures, as clearly demonstrated by the ground occurred in the Ospedale del Mare car park (Naples, Italy) [3] during the COVID-19 emergency that has brought the light on the weakness of the planning processes of the public authorities when fast decisions are required. Indeed, decision making in urban planning can be effectively supported by rational and reasoned use of the geophysical technologies able to reduce the risks imputable to the activities and decision required by the emergency planning in urban contexts.

This work focuses its attention on the capability of geophysical methodologies to detect, characterize and monitoring the presence of buried sinkholes, collapses, voids within the subsoil able to cause severe structural stability problems with rapid and non-invasive applications based on the use of Ground Penetrating Radar and Electrical Resistivity Tomographies. The studied cases showed how the cooperative use of the geoelectrical and electromagnetic methods can identify and monitor potential risks of collapses highlighting the pros and cons of the two techniques in terms of resolution and depth of study.

REFERENCES

[1] Lapenna V. (2016) Resilient and sustainable cities of tomorrow: the role of applied geophysics. Bollettino di Geofisica Teorica ed Applicata 58(4):237–251. https ://doi.org/10.4430/bgta0204

[2] Capozzoli L., De Martino G., Polemio M. et E. Rizzo, Surveys in Geophysics 2019, Geophysicaltechniquesfor monitoring settlement phenomena occurring in reinforced concrete buildings, Surveys in Geophysics, DOI: 10.1007/s10712-019-09554-8;

[3] Borghese L.,  Mortensen A. and R. Picheta, https://edition.cnn.com/2021/01/08/europe/italy-hospital-sinkhole-scli-intl/index.html (latest access 01/20/2021, January 9, 2021

How to cite: Capozzoli, L., De Martino, G., Fornasari, G., Giampaolo, V., and Rizzo, E.: ERT and GPR surveys for the detection of incipient collapse areas in urban environment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15100, https://doi.org/10.5194/egusphere-egu21-15100, 2021.

Highway pavements serve the need for safe transportation of human being and freights, so their condition deserves continuous monitoring and assessment. However, pavements are most often monitored in terms of their surface performance evaluation. Either with or without surface distresses, excessive pavement unevenness and/or texture loss may lead to a reduced road users’ satisfaction. Most often, the pavement surface condition is sensed through laser profilers that operate at traffic speeds. Once detected through the stand-alone use of laser profilers, pavement roughness along a pavement surface may be of major concern for the related agencies, since the root causes of roughness issues are in most cases unknown.

Excessive unevenness might sometimes be interrelated with structural issues within one or more pavement layers or even issues within the pavement foundation support. Traditionally, coring and boreholes are considered suitable to detect the condition of pavement surface layers and pavement substructure respectively. However, these processes are destructive and time-consuming. On the contrary, Non-Destructive Testing

(NDT) can be alternatively used to rapidly evaluate potential structural problems at areas with roughness issues and identify areas for further investigation. A popular method to assess the pavement structural integrity is the use of nondestructive deflectometric tests, including the Falling Weight Deflectometer (FWD). This kind of testing outperforms the traditional approach; thus it is both desirable and practical.

On these grounds, related research is pursued towards integrating pavement profile and deflectometric data in order to further evaluate indications of increased pavement roughness. In particular, Long Term Pavement Performance (LTPP) data including deflectometric and pavement profile data is used. Additional sensing data through geophysical inspections with the Ground Penetrating Radar (GRP) is used to assist the overall pavement assessment. The study demonstrates the power of pavement sensing data in order to provide the related agencies with cost-effective and reliable evaluation methods and approaches.

How to cite: Plati, C., Gkyrtis, K., and Loizos, A.: Nondestructive data analysis for pavement profile evaluation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7920, https://doi.org/10.5194/egusphere-egu21-7920, 2021.

As an array antenna ground penetrating radar (GPR) system electronically switches any antenna combinations sequentially in milliseconds, multi-offset gather data, such as common mid-point (CMP) data, can be acquired almost seamlessly. However, due to the inflexibility of changing the antenna offset, only a limited number of scans can be obtained. The array GPR system has been used to collect time-lapse GPR data, including CMP data during the field infiltration experiment (Iwasaki et al., 2016). CMP data obtained by the array GPR are, however, too sparse to obtain reliable velocity using a standard velocity analysis, such as semblance analysis. We attempted to interpolate the sparse CMP data based on projection onto convex sets (POCS) algorithm (Yi et al., 2016) coupled with NMO correction to automatically determine optimum EM wave velocity. Our previous numerical study showed that the proposed method allows us to determine the EM wave velocity during the infiltration experiment.

The main objective of this study was to evaluate the performance of the proposed method to interpolate sparse array antenna GPR CMP data collected during the in-situ infiltration experiment at Tottori sand dunes. The interpolated CMP data were then used in the semblance analysis to determine the EM wave velocity, which was further used to compute the infiltration front depth. The estimated infiltration depths agreed well with independently obtained depths. This study demonstrated the possibility of developing an automatic velocity analysis based on POCS interpolation coupled with NMO correction for sparse CMP collected with array antenna GPR.

How to cite: Oikawa, K., Saito, H., Kuroda, S., and Takahashi, K.: Determining optimum wave velocity from sparse CMP automatically by coupling POCS interpolation and NMO correction: application to array antenna GPR data collected during in-situ infiltration test, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9074, https://doi.org/10.5194/egusphere-egu21-9074, 2021.