Data fusion, integration, correlation and advances of non-destructive testing methods and numerical developments for engineering and geosciences applications


Data fusion, integration, correlation and advances of non-destructive testing methods and numerical developments for engineering and geosciences applications
Convener: Andrea Benedetto | Co-conveners: Morteza (Amir) Alani, Andreas Loizos, Francesco Soldovieri, Fabio TostiECSECS
vPICO presentations
| Tue, 27 Apr, 15:30–17:00 (CEST), Wed, 28 Apr, 09:00–09:45 (CEST)

vPICO presentations: Tue, 27 Apr

Chairpersons: Andrea Benedetto, Francesco Soldovieri, Fabio Tosti
Data Analytics, Modelling and Applications of Non-Destructive Testing Methods
Benjamin Moreno-Torres, Christoph Völker, and Sabine Kruschwitz

Non-destructive testing (NDT) data in civil engineering is regularly used for scientific analysis. However, there is no uniform representation of the data yet. An analysis of distributed data sets across different test objects is therefore too difficult in most cases.

To overcome this, we present an approach for an integrated data management of distributed data sets based on Semantic Web technologies. The cornerstone of this approach is an ontology, a semantic knowledge representation of our domain. This NDT-CE ontology is later populated with the data sources. Using the properties and the relationships between concepts that the ontology contains, we make these data sets meaningful also for machines. Furthermore, the ontology can be used as a central interface for database access. Non-domain data sources can be integrated by linking them with the NDT ontology, making them directly available for generic use in terms of digitization. Based on an extensive literature research, we outline the possibilities that result for NDT in civil engineering, such as computer-aided sorting and analysis of measurement data, and the recognition and explanation of correlations.

A common knowledge representation and data access allows the scientific exploitation of existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis or material characterization) and simplifies bidirectional knowledge and data transfer between engineers and NDT specialists.

How to cite: Moreno-Torres, B., Völker, C., and Kruschwitz, S.: An Ontology-based approach to enable data-driven research in the field of NDT in Civil Engineering, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12125,, 2021.

Christoph Völker, Sabine Kruschwitz, and Philipp Benner

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,, 2021.

Alessandro Di Benedetto, Salvatore Barba, Margherita Fiani, Marco Limongiello, and Anna Sanseverino

The use of Building Information Modeling (BIM) is certainly increasing, especially in the field of Civil Engineering and Architecture. In recent years, research for new solutions has focused on the integration of BIM and GIS (Geographic Information System), referred to as GeoBIM. Most applications focus on issues related to the import and interoperability of BIM data into a GIS environment and vice versa. Data integration in a well-designed GeoBIM should address the following aspects: i) data harmonization and consistency (e.g., accuracy estimation, geometric and semantic representation, amount of detail, geo-referencing); ii) interoperability of data coming from different sources; iii) transformation of a set of data into a standardized format. One of the most evident inconsistencies if working with BIM or GIS is in the georeferencing of data: BIM designers work in a local Cartesian system while the terrain morphology is referred to a Geodetic Reference System, in the case of Europe, and therefore also for Italy, such system is the ETRS89, realization ETRF2000. The objective of this work is to achieve a true integration between BIM and GIS through the use and combination of the strengths of both technologies: the semantic and spatial component of GIS with the 3D and detailed information coming from the BIM model. A model that meets these requirements will allow a management of the structure and / or infrastructure in a wider and more complete context; therefore, not only at the local level but will be applicable to structures that have a strong impact with the territory and located in areas subject to hydrogeological risk. One of the innovative aspects of the study is the integration of the regional Topographic Database (TDB) with the altimetric component extracted automatically from LiDAR data; the process aims to allow the reconstruction of the volumes in an automated way of each object to define the 3D spatial attribute for the purposes of three-dimensional modeling. The study area is located near the “Monti Lattari” in the Campania Region, in southern Italy. The whole area consists of areas exposed to high hydrogeological risk, characterized by the presence of a complex infrastructural network (railway, highway, national and provincial roads), rich in viaducts, tunnels and galleries. In details, the GeoBIM model of a viaduct (Olivieri Viaduct), built between the years ‘50 and ‘60, has been made. The main structure is a Maillart-arch-type bridge, made of reinforced concrete with a continuous frame deck and two access viaducts. The structural model has been generated from the point cloud acquired by Terrestrial Laser Scanner (TLS). The BIM model has been realized by using Revit software package (Autodesk), which allowed to organize the information useful to define the entire viaduct: each virtual element has been “informed” with all the parameters and characteristics of the structural elements. The next work phase was addressed to the design of a workflow able to combine the BIM model into a GIS developed by using ESRI tools. So, the parametric model produced in Revit is transformed into a GeoDatabase.

How to cite: Di Benedetto, A., Barba, S., Fiani, M., Limongiello, M., and Sanseverino, A.: BIM and GIS integration for infrastructure analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8293,, 2021.

Ekaterina Chuprikova, Abraham Mejia Aguilar, and Roberto Monsorno

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,, 2021.

Giacomo Fornasari, Luigi Capozzoli, Gregory De Martino, Valeria Giampaolo, and Enzo Rizzo

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,, 2021.

Ilaria Catapano, Luigi Capozzoli, Giovanni Ludeno, Gianluca Gennarelli, Gregory De Martino, Enzo Rizzo, Francesco Uliano Scelza, Gabriel Zuchtriegel, and Francesco Soldovieri

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).


[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,, 2021.

Luca Bianchini Ciampoli, Andrea Benedetto, Alessandra Ten, Carla Maria Amici, and Roberta Santarelli

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.



[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,, 2021.

Hossain Zadhoush, Antonios Giannopoulos, and Iraklis Giannakis

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.



[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,, 2021.

Salih Serkan Artagan, Özgür Yurdakul, Vladislav Borecky, and Miroslav Lunak

There are certain situations where concrete structures are required to resist high temperatures. This applies to cases where exposure to high temperature is expected due to the special character of buildings or where the concrete structure is required to resist severe conditions caused by traffic accidents, terrorist attacks, or natural disasters (earthquakes, fires, etc.). Under such applications, the effect of elevated temperature on mechanical and physical properties may determine whether the concrete element or structure will maintain its structural integrity or not. In this context, fire resistance is defined as the ability to withstand exposure to fire without loss of load-bearing function or ability to act as a barrier to spread a fire. In most cases, structural health monitoring of concrete structures is performed as the visual appraisal of the external characteristics of structures or destructive testing (e.g., concrete coring), and little use has been made of the modern non-destructive testing (NDT) techniques including Ground Penetrating Radar (GPR). GPR, emitting short pulses of electromagnetic energy into the material, is primarily used for location of rebar, estimation of rebar size, industrial quality control, defect and decay detection, and measurement of electrical properties, in case of concrete diagnostics.

This paper comprises a series of GPR and core compressive strengths on low-strength concrete samples. The samples were produced and tested by GPR before and after extreme temperature exposure in an electric furnace at the following temperature levels: 300, 400, 500, 600, and 700 ℃. Then, the compressive test results of the cores taken from the specimens are compared with the GPR data for each temperature level. For GPR tests, the IDS Aladdin system was used with a double polarized 2 GHz antenna. For compressive strength tests, a compression test machine with a capacity of 3000 kN was used.

Based on GPR measurement, Relative Dielectric Permittivity (RDP) values were calculated based on known dimensions of samples and two-way travel time (twt) values obtained from A-scans. The change in RDP values of samples before and after exposure to extreme temperature was then calculated. This variation was then correlated with the change of compressive strength values with regard to the applied temperature levels. This experimental study thus gives an insight into the potential use of GPR, as an NDT tool, in estimating the strength loss in concrete structural elements exposed to aggressive fire.

All GPR tests were conducted in Educational and Research Centre in Transport; Faculty of Transport Engineering; University of Pardubice. This work is supported by the University of Pardubice (Project No: CZ.02.2.69/0.0/0.0/18_053/0016969).

How to cite: Artagan, S. S., Yurdakul, Ö., Borecky, V., and Lunak, M.: A GPR based estimation of concrete strength changes under extreme temperatures exposure: An experimental study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8845,, 2021.

Mezgeen Rasol, Vega Perez-Gracia, and Sonia Santos

Ground Penetrating Radar was used in this study as a non-destructive geophysical method. The main objective of this research is focused on enhancing the local seismic soil site analysis. The study employs GPR images to determine changes in the ground that can be associated with changes on the seismic soil response. To determine the GPR capacity in detecting changes in the ground materials and improve new methodologies of the radar data processing.

Results could be used to improve the selection of areas for more intensive scrutiny, enhancing the analysis of local seismic behaviour studies. Soil site studies are crucial in the analysis of seismic hazard in populated areas. This study and analysis will be carried out in an urban environment at the Sant Pau Hospital in Barcelona city (Spain). Data were acquired in the field along with two different directions: parallel and perpendicular to the coastline of the Mediterranean Sea in Barcelona city.

The procedure is based in integrated data from the laboratory experiments by using 1600 MHz centre frequency and obtaining real GPR field images in the field by using 25 MHz centre frequency antenna in the Sant Pau Hospital. Therefore, radar data will be first processed using the commercial software ReflexW, followed by a more specific processing sequence (both in amplitude and frequency domains) with a specific algorithm developed with MATLAB.

Finally, the mathematical processing of the radargrams in terms of water content compared to the information based on historical maps. Results show that GPR is a promising method and compared to previous studies a good agreement was observed in this specific case study. 

How to cite: Rasol, M., Perez-Gracia, V., and Santos, S.: New methodologies of GPR Assessment for analysing water content in sedimentary deposits; Application to the Hospital Sant Pau Urban Area in Barcelona, Spain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-363,, 2021.

Davide Campo

Signal polarity is an attribute that can be used as additional key element to reduce ambiguities and pitfalls in the data interpretation step.

A theoretical analysis of the reflection and transmission phenomena for parallel and perpendicular polarization of the electric field was carried out highlighting that polarity changes (180-degree phase shifts) are caused only by reflection phenomena in specific conditions.

Numerical modelling, through the Finite Difference Time Domain (FDTD) method, helped visualize the theoretical findings and was employed to reproduce the GPR response in two simple contexts (high permittivity layer embedded in a lower permittivity material and vice versa). The findings showed the expected theoretical polarity of multiple reflections providing a tool to effectively recognise them along with travel time information and reflection shapes.

The FDTD technique was also used to analyse the polarity response of regular geometrical shaped air-filled cavities (circle, square and arched roof square), in lossless and lossy conditions. The output was then compared with real radargrams concluding that A-scan assessment should be considered when pronounced scattering and attenuation phenomena are experienced (although polarity analysis may not be possible in very complex environments) and that the shape of the target may affect the resulting signal polarity due to interference with other wave fields.

Polarity analysis should be carried out by comparing the direct wavelet with the signal pattern of interest to assess if a phase shift occurred: attention should be paid to the GPR system used as not all the GPR antennas record the direct wavelet.

How to cite: Campo, D.: On GPR signal polarity: a comparison between theoretical findings and real case-studies, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9234,, 2021.

Bo Li and Yonghui Zhao

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,, 2021.

Raffaele Persico and Gianfranco Morelli

In this contribution we will propose the combination of migration results achieved from the same GPR dataset, aimed to mitigate the effects of the nonuniformity of the propagation velocity of the waves throughout the investigated domain. The nonuniformity of the propagation velocity can be appreciated from the diffraction hyperbolas [1] possibly present in the data, or directly from the results of the focusing [2] achieved from different trial values of the propagation velocity. In ref. [3] an algebraic combination of two (but theoretically even more) migration results achieved from different migration parameters applied to the same data has been shown. In that paper, the case of a horizontal variation and the case of a vertical variation of the propagation velocity of the electromagnetic waves in the soil were considered. Here, we will consider the case of a layered medium with non-flat interface between two adjacent layers, which is a case of interest in several practical application, and is a case where we have both a vertical and a horizontal variation of the parameters. Analogously to ref. [3], we will consider both the aspect of the focusing and that of the combined time-depth conversion.




[1] R. Persico G. Leucci, L. Matera, L. De Giorgi, F. Soldovieri, A. Cataldo, G. Cannazza, E. De Benedetto, Effect of the height of the observation line on the diffraction curve in GPR prospecting, Near Surface Geophysics, Vol. 13, n. 3, pp. 243-252, 2015.

[2]G. Gennarelli, I. Catapano, F. Soldovieri, R. Persico, On the Achievable Imaging Performance in Full 3-D Linear Inverse Scattering, IEEE Trans. on Antennas and Propagation,  vol. 63, n. 3, pp. 1150-1155, March 2015.

[3] R. Persico, G. Morelli, Combined Migrations and Time-Depth Conversions in GPR Prospecting: Application to Reinforced Concrete, Remote Sens. 2020, Volume 12, Issue 17, 2778, open access, DOI 10.3390/rs12172778



How to cite: Persico, R. and Morelli, G.: Combined Migration of GPR data for layered media, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12852,, 2021.

Tan Qin, Thomas Bohlen, and Yudi Pan

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,, 2021.

Livia Lantini, Fabio Tosti, Luca Bianchini Ciampoli, and Amir M. Alani

Monitoring and protecting natural assets is increasingly important today, as aggressive pathogens are negatively impacting the trees' survival. In this regard, root systems are affected by fungal infections that cause roots’ rot and eventually lead to trees' death. Such disease can spread rapidly to the adjacent trees and affect larger areas. Since these decays generally do not display visible signs, early identification is the key to tree preservation.

Within this context, non-destructive testing (NDT) methods are becoming popular, being more versatile than destructive methods. Specifically, ground penetrating radar (GPR) is emerging as an accurate geophysical method for tree root mapping. Recent research has focused on implementing automated algorithms for 3D root mapping, improving root detection through advanced GPR signal processing and the estimation of tree roots' mass density [1]. Also, recent studies have proven that GPR is effective in mapping the root system's architecture of street trees [2].

The present research reports the preliminary results of an experimental study, conducted to investigate the feasibility of a novel tree root assessment methodology based on the analysis of GPR data both in time and frequency domain. To this end, data were processed using a short-time Fourier transform (STFT) approach [3], which allows the evaluation of how the frequency spectrum changes across the signal propagation time window. The suggested processing system may be implemented for expeditious analyses or on trees challenging to access, such as in urban environments, where more comprehensive survey methods are not applicable. The objectives of this study, therefore, are to investigate how different features (i.e., roots, layers) affect the time-frequency analysis of GPR data, and to identify recurring patterns in the results to set a coherent data processing methodology.

Results' interpretation has shown the viability of the presented approach in recognising the influence of different features on the analysis of GPR data as it changes over time. This also allowed the detection of recurring patterns in the analysed data, proving that this method is worthy of further investigations.

The authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.

[1]     Lantini, L., Tosti, F., Giannakis, I., Zou, L., Benedetto, A. and Alani, A. M., 2020. "An Enhanced Data Processing Framework for Mapping Tree Root Systems Using Ground Penetrating Radar," Remote Sensing 12(20), 3417.
[2]     Lantini, L., Alani, A., Giannakis, I., Benedetto, A. and Tosti, F., 2020. "Application of ground penetrating radar for mapping tree root system architecture and mass density of street trees," Advances in Transportation Studies (3), 51-62.
[3]     Bianchini Ciampoli, L., Calvi, A. and D'Amico, F., 2019. "Railway Ballast Monitoring by GPR: A Test Site Investigation," Remote Sensing 11(20), 238

How to cite: Lantini, L., Tosti, F., Bianchini Ciampoli, L., and Alani, A. M.: On the Use of Short-Time Fourier Transform for the Analysis of Tree Root Systems using Ground Penetrating Radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13463,, 2021.

Jingxuan Geng, Chunhua Yang, Lijuan Lan, Jie Han, Fengxue Zhang, and Yonggang Li

The online automatic detection for the concentration of total nitrogen (TN) is a critical problem in wastewater treatment plants (WWTPs). The over-discharge of TN can cause severe environmental problems such as aquatic eutrophication and ecosystem dysfunction, and the TN concentration in each wastewater treatment process can also reflect the processing statement of WWTPs and ensure its stable operation. However, determining the TN concentration timely is always a difficult task. According to the traditional TN detection approach, the concentration of TN is determined after the oxidative digestion process, which is a complex chemical reaction process and usually requires 30 minutes to 1 hour. Considering the actual operation situation, this traditional method can hardly satisfy the real-time requirement of WWTPs, which can only be used as a kind of validation approach. To solve this problem, in this paper, we design a novel automatic detection prototype of TN. Instead of determining the concentration of TN after the process of oxidative digestion, the ultraviolet spectrum is used to non-destructive detect the concentration of nitrate during the whole oxidative digestion process. Based on the principle of competitive response and chemical reaction kinetics, for different water samples with different TN concentrations, their oxidative digestion processes are different even in the early reaction stage. Therefore, we can use the early reaction properties to determine the TN concentration, thereby shortening the necessary detection time. Based on experimental data collected from real water samples, our prototype can not only efficiently shorten the detection time of the TN concentration, but also ensure satisfactory detection accuracy.

How to cite: Geng, J., Yang, C., Lan, L., Han, J., Zhang, F., and Li, Y.: A rapid total nitrogen determination prototype for wastewater treatment plants online detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14834,, 2021.


vPICO presentations: Wed, 28 Apr

Chairpersons: Andreas Loizos, Francesco Soldovieri, Fabio Tosti
Non-Destructive Testing Technologies in Civil Engineering and Geosciences
Tim Klewe, Christoph Strangfeld, Tobias Ritzer, and Sabine Kruschwitz

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,, 2021.

Anthony Alongi

Chlorides from deicing salts attack the steel reinforcement in bridge decks which can ultimately cause delamination and deterioration of the concrete. For transportation agencies, the repair cost from these defects are estimated to exceed $5B per year in USA and make up between 50% - 85% of bridge maintenance budgets. While, the removal and replacement of chloride contaminated concrete is the most long-lasting and cost-effective remediation, few methods exist to determine chloride content in bridge decks. This research describes an entirely new method for determining chloride quantity in bridge decks using ground penetrating radar (GPR) technology and establishes and quantifies the relationship between chlorides in concrete (which cause corrosion of reinforcing steel and delamination of concrete) and the effect on GPR signal propagation. Specifically, it shows that there is a deterministic relationship between radar signal attenuation and the amount of chloride and moisture in bridge deck concrete, and that when moisture content is known it is possible to estimate chloride quantity based on signal loss or attenuation measurements. Our research also demonstrates the practical application of this concept by utilizing GPR along with limited coring (three or more core samples) and laboratory chloride measurements to produce an accurate and quantitative, spatial mapping of chlorides in bridge decks.

How to cite: Alongi, A.: Determining Bridge Deck Chloride Quantities Using Ground Penetrating Radar, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13472,, 2021.

Hai Liu, JianYing Lin, Xu Meng, and Yanliang Du

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.