GI2.2

Seismic and ultrasound tomography can provide rich information about spatial variations of elastic properties inside a material rendering this method ideal for non-destructive testing. These tomographic methods primarily use direct and reflected waves, but are also strongly affected by waves scattering at small-scale structures below the resolution limit. As a consequence, conventional tomography has the ability to unveil the deterministic large-scale structure only, rendering scattered waves imaging noise. To image scattering and absorption properties, we presented the adjoint envelope tomography (AET) method that is based on a forward simulation of wave envelopes using Radiative Transfer Theory and an adjoint (backward) simulation of the envelope misfit, in full analogy to full-waveform inversion (FWI). In this algorithm, the forward problem is solved by modelling the 2-D multiple nonisotropic scattering in an acoustic medium with spatially variable heterogeneity and attenuation using the Monte-Carlo method. The fluctuation strength ε and intrinsic quality factor Q^-1 in the random medium are used to describe the spatial variability of scattering and absorption, respectively. The misfit function is defined as the differences between the full squared observed and modelled envelopes. We derive the sensitivity kernels corresponding to this misfit function that is minimized during the iterative adjoint inversion with the L-BFGS method. This algorithm has been applied in some numerical tests (Zhang et al., 2021). In the present work, we show real data results from an ultrasonic experiment conducted in a reinforced concrete specimen. The later coda waves of the envelope processed from the 60 KHz ultrasonic signal are individually used for intrinsic attenuation inversion whose distribution has similarity to the temperature distribution of the concrete block. Based on the inversion result of intrinsic attenuation, scattering strength is inverted from early coda waves separately, which successfully provides the structure of the small-scale heterogeneity in the material. The resolution test shows that we recover the distribution of heterogeneity reasonably well.

How to cite: Zhang, T., Sens-Schönfelder, C., Epple, N., and Niederleithinger, E.: Ultrasonic Scattering and Absorption Imaging for the Reinforced Concrete using Adjoint Envelope Tomography, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5731, https://doi.org/10.5194/egusphere-egu22-5731, 2022.

The permanent scatterer synthetic aperture radar interferometry (PS-InSAR) technique can detect the permanent scatterers(PSs) on the ground. But the deformation of PSs can’t be used to analyze the deformation of underground buildings below the ground surface directly, such as tunnels. In this paper, the process of tunnel deformation analysis using PSs data and stress-area method is proposed. The deformation data of PSs are used to fit the surface deformation of tunnel by kriging interpolation method. The stress area method is used to calculate the deformation of the soil above the tunnel, then the deformation of tunnel can be acquired. This process was applied to calculate the deformation of a tunnel in Shanghai, China. The results show that the fitted surface deformation rate data are accurate, with the maximum absolute difference of 1.45mm/y and the minimum difference of 0.11mm/y compared with the level monitoring data. The tunnel deformation rate calculated by this process is close to the measured deformation rate of the tunnel with error level in millimeters. The surface and tunnel deformation rate curves are similar in the tunnel extension direction. PS-InSAR technique has the advantages of acquiring large area, historical data of surface deformation. Combined with the process proposed in this paper, Large-scale tunnel deformation analysis can be achieved.

How to cite: Chai, L., Xie, X., Li, P., Zhou, B., and Zeng, L.: Tunnel deformation rate analysis based on PS-InSAR technique and stress-area method , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13401, https://doi.org/10.5194/egusphere-egu22-13401, 2022.

Monitoring of bridges and viaducts has become a priority for asset owners due to progressive infrastructure ageing and its impact on safety and management costs. Advancement in data processing and interpretation methods and the accessibility of Synthetic Aperture Radar (SAR) datasets from different satellite missions have contributed to raise interest for use of near-real-time bridge assessment methods. In this context, the Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) space-borne monitoring technique has proven to be effective for detection of cumulative surface displacements with a millimetre accuracy [1-3].

This research aims to investigate the viability of using satellite remote sensing for structural assessment of the Rochester Bridge in Rochester, Kent, UK. To this purpose, high-resolution SAR datasets are used as the reference information and complemented by additional data from different sensing technologies (e.g., medium-resolution SAR datasets and ground-based (GB) non-destructive testing (NDT)). In detail, high-resolution SAR products of the COSMO-SkyMed (CSK) mission (2017-2019) provided by the Italian Space Agency (ASI) in the framework of the Project “Motib - ID 742”, approved by ASI, are processed using a MT-InSAR approach.

The method allowed to identify several Persistent Scatterers (PSs) – which have been associated to different structural elements (e.g., the bridges piers) over the four main bridge decks – and monitor bridge displacements during the observation time. The outcomes of this study demonstrate that information from the use of high-resolution InSAR data can be successfully integrated to datasets of different resolution, scale and source technology. Compared to stand-alone technologies, a main advantage of the proposed approach is in the provision of a fully-comprehensive (i.e., surface and subsurface) and dense array of information with a larger spatial coverage and a higher time acquisition frequency. This results in a more effective identification and monitoring of decays at reduced costs, paving the way for implementation into next generation Bridge Management Systems (BMSs).

Acknowledgements: This research is supported by the Italian Ministry of Education, University and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”,attributed to the Department of Engineering of Roma Tre University, is acknowledged.Authors would also like to acknowledge the Rochester Bridge Trust for supporting research discussed in this paper. The COSMO-SkyMed (CSK) products - ©ASI- are provided by the Italian Space Agency (ASI) under a license to use in the framework of the Project “ASI Open-Call - Motib (ID 742)” approved by ASI.

References

[1] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Alani A. M., Tosti F., Battagliere M. L., Benedetto A., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”, Proc. SPIE 11525, SPIE Future Sensing Technologies. 2020. doi: 1117/12.2579700

[2] Jung, J.; Kim, D.-j.; Palanisamy Vadivel, S.K.; Yun, S.-H. "Long-Term Deflection Monitoring for Bridges Using X and C-Band Time-Series SAR Interferometry". Remote Sens. 2019

[3] Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6, doi: 10.1109/RadarConf2043947.2020.9266336

How to cite: Gagliardi, V., Bianchini Ciampoli, L., D’Amico, F., Battagliere, M. L., Threader, S., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Bridges by Satellite Remote Sensing Using Multi-Source and Multi-Resolution Data Integration Techniques: a Case Study of the Rochester Bridge, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2341, https://doi.org/10.5194/egusphere-egu22-2341, 2022.

In recent years, great interest has been paid to the risk that hydrogeological instability causes to the territory, especially in densely populated and geologically fragile areas. 
The forests, exerting a natural restraint, play an important protective function for the infrastructures and settlements underneath from the danger of falling rocks that fall from the rocky walls. This protective action is influenced not only by issues related to the vegetation itself but also by the morphology of the terrain, as a steeply sloping land surface can significantly increase the momentum of the rolling rock.
The aim of our work is to design a methodology based on the integration of remote sensing data, in detail optical satellite images and LiDAR data acquired by UAVs, to identify areas most prone to natural rockfall retention [1]. The results could then be used to identify areas that need to be reinforced artificially (rockfall nets) and naturally (protective forests).
The test area is located near Monte San Liberatore in the Campania region (Italy), which was affected in 1954 by a disastrous flood, in which heavy rains induced the triggering of a few complex landslides in a region that was almost geomorphologically susceptible.  Indeed, there are several areas subject to high risk of rockfalls, whose exposed value is represented by a complex infrastructural network of viaducts, tunnels, and galleries along the north-west slope of the mountain, which is partly covered by thick vegetation, which reduces the rolling velocity of rocks detaching from the ridge. 
According to the Carta della Natura, the vegetation most present in the area is the holm oak (Quercus Ilex), an evergreen, long-lived, medium-sized tree. Its taproot makes it resistant and stable, able to survive in extremely severe environments such as rocky soils or vertical walls, so it is ideal for slope protection.
The first processing step involved the multispectral analysis on Pleiades 1A four-band (RGB +NIR) high-resolution satellite images (HRSI). The computed vegetation indices (NDVI, RVI and NDWI) were used to assess the vegetation health status and its presumed age; thus, the most resilient areas of the natural compartment in terms of robustness and vigor were identified. The average plant height was determined using the normalized digital surface model (nDSM).
Next, starting from the Digital Terrain Model (DTM), we derived the morphometric features suitable for the description of the slope dynamics: slope gradient, exposure with respect to the North direction, plane, and convexity profile. The DTM and the DSM were created by interpolating on a grid the LiDAR point cloud acquired via UAV. Classification of areas having similar characteristics was made using SOM (Self-Organizing Maps), based on unsupervised learning.
The classified maps obtained delimit the similar areas from a morphological and vegetation point of view; in this way, all those areas that tend to have a higher propensity for rock roll reduction were identified.

[1] Fanos, Ali Mutar, and Biswajeet Pradhan. "Laser scanning systems and techniques in rockfall source identification and risk assessment: a critical review." Earth Systems and Environment 2.2 (2018): 163-182.

How to cite: Di Benedetto, A., Ambrosino, A., and Fiani, M.: Integrating Remote Sensing data to assess the protective effect of forests on rockfall:The case study of Monte San Liberatore (Campania, Italy), EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12743, https://doi.org/10.5194/egusphere-egu22-12743, 2022.

The near-surface geophysical methods have been widely applied to investigations of shallow targets for scientific and engineering research. Various data processing algorithms are available to help visualize targets, data interpretation, and finally, achieve research goals.

Most of the available algorithms are Fourier-based with linear stationary assumptions. However, the real data are rarely the case and should be treated as nonlinear and non-stationary. In recent decades, a few newer algorithms are proposed for processing non-stationary, or nonlinear and non-stationary data, for instance, wavelet transform, curvelet transform, full-waveform inversion, Hilbert-Huang transform, etc. This progress is encouraging, but conventional algorithms still have many advantages, like strong theoretical bases, fast, and easy to apply, which the newer algorithms are short of.

In this study, we try to fuse both conventional and contemporary algorithms in near-surface geophysical methods. A cost-effective ground-penetrating radar (GPR) data processing scheme is introduced in shallow depth structure mapping as an example. The method integrates a nonlinear filtering technique, natural logarithmic transformed ensemble empirical mode decomposition (NLT EEMD), with the conventional pseudo-3D GPR data processing methods including background removal and migration to map the subsurface targets in 2D profile. The finalized pseudo-3D data volume is constructed by conventional linear interpolation. This study shows that the proposed technique could be successfully employed to locate the buried targets with minimal survey effort and affordable computation cost. Furthermore, the application of the proposed method is not limited to GPR data processing, any geophysical/engineering data with the similar data structure are applicable.

How to cite: Jeng, Y., Chen, C.-S., and Yu, H.-M.: Algorithms fusion for near-surface geophysical survey, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6251, https://doi.org/10.5194/egusphere-egu22-6251, 2022.

Maintenance of airport runways is crucial to comply with strict safety requirements for airport operations and air traffic management [1]. Therefore, monitoring pavement surface defects and irregularities with a high temporal frequency, accuracy and spatial density of information becomes strategic in airport asset management [2-3]. In this context, Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques are gaining momentum in the assessment and health monitoring of infrastructure assets, proving their viability for the long-term evaluation of ground scatterers. However, the implementation of C-band SAR data as a routine tool in Airport Pavement Management Systems (APMSs) for the accurate measurement of differential displacements on runways is still an open challenge [4]. This research aims to demonstrate the viability of using medium-resolution (C-band) SAR products and their contribution to improve current maintenance strategies in case of localised foundation settlements in airport runways. To this purpose, Sentinel-1A SAR products, available through the European Space Agency (ESA) Copernicus Program, were acquired and processed to monitor displacements on “Runway n.3” of the “L. Da Vinci International Airport” in Fiumicino, Rome, Italy.A geostatistical study is performed for exploring the spatial data structure and for the interpolation of the Sentinel-1A SAR data in correspondence of ground control points.The analysis provided ample information on the spatial continuity of the Sentinel 1 data, also in comparison with the high-resolution COSMO-SkyMed and the ground-based topographic levelling data, taken as the benchmark.Furthermore, a comparison between the MT-InSAR outcomes from the Sentinel-1A SAR data, interpolated by means of Ordinary Kriging, and the ground-truth topographic levelling data demonstrated the accuracy of the Sentinel 1 data. Results support the effectiveness of using medium-resolution InSAR data as a continuous and long-term routine monitoring tool for millimetre-scale displacements in airport runways. Outcomes of this study can pave the way for the development of more efficient and sustainable maintenance strategies for inclusion in next-generation APMSs.  

Acknowledgments and fundings: The authors acknowledge the European Space Agency (ESA), for providing the Sentinel 1 SAR products for the development of this research. The COSMO-SkyMed Products—©ASI (Italian Space Agency)- are delivered by ASI under the license to use.This research falls within the National Project “EXTRA TN”, PRIN 2017, supported by MIUR. The authors acknowledge funding from the MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University

 References

[1]Gagliardi V., Bianchini Ciampoli L., D'Amico F., Tosti F., Alani A. and Benedetto A. “A Novel Geo-Statistical Approach for Transport Infrastructure Network Monitoring by Persistent Scatterer Interferometry (PSI)”. In: 2020 IEEE Radar Conference, Florence, Italy, 2020, pp. 1-6

[2]Gagliardi V, Bianchini Ciampoli L, Trevisani S, D’Amico F, Alani AM, Benedetto A, Tosti F. "Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis". 2021; 21(17):5769. https://doi.org/10.3390/s21175769

[3]Gao, M.; Gong, H.; Chen, B.; Zhou, C.; Chen, W.; Liang, Y.; Shi, M.; Si, Y. "InSAR time-series investigation of long-term ground displacement at Beijing Capital International Airport, China". Tectonophysics 2016, 691, 271–281.

[4]Department of Transportation Federal Aviation Administration (FAA), Advisory Circular 150/5320-6F, Airport Pavement Design and Evaluation, 2016

How to cite: Gagliardi, V., Trevisani, S., Bianchini Ciampoli, L., D’Amico, F., Alani, A. M., Benedetto, A., and Tosti, F.: Monitoring of Airport Runways by Satellite-based Remote Sensing Techniques: a Geostatistical Analysis on Sentinel 1 SAR Data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2533, https://doi.org/10.5194/egusphere-egu22-2533, 2022.

Water use efficiency (WUE) plays a vital role in planning and management of irrigation strategies. Considering the spatial scale, WUE can be quantified at scales ranging from leaf to whole-plant to ecosystem to region. However, the inter-relation and their associate is poorly understood. This study is aimed at stimulating WUE of irrigated cotton at leaf () and further investigate the role of environmental and biophysical conditions on WUE dynamics. This study was conducted in an agricultural croplands located in Sangareddy district, about 70 km west of Hyderabad, the capital city of southern state Telangana, India. Ground based observation were made such as soil moisture, photosynthetic parameters and meteorological parameters. Modelling leaf water use efficiency has been established. The stomatal conductance  and  of cotton leaves exposed to ambient CO₂ were simulated using Ball-Berry (mBB) model. Moreover, the stomatal conductance  and  of Cotton leaves exposed to ambient CO₂ is simulated using modified Ball-Berry model, with instantaneous gas exchanges measured around noon used to parameterize and validate the model. We observed a large diurnal (4.3±1.9 mmolCO₂ mol^-1H₂O) and seasonal (5.16±1.51 mmolCO₂ mol^-1H₂O) variations in  during the crop period. Model simulated  and  are in agreement with the measurements (R²>0.5, RMSE<0.3). Our results conclude that WUE_L­ is ruled by climatic as well as vegetative factors respectively, and are largely controlled by changes in transpiration over photosynthesis. This needs further investigation with extensive analysis by building library of in-situ measurements.

Keywords: Cotton, WUE, Irrigation, Stomatal conductance, Ball Berry Model

How to cite: Moharana, S. and Kambhammettu, P. B.: Water use efficiency (WUE) Modeling at Leaf level of Cotton (Gossypium hirsutum L.) in Telangana, India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9441, https://doi.org/10.5194/egusphere-egu22-9441, 2022.

In the last years, unmanned surface vehicles (USVs) in marine environment have attracted considerable interest since they are flexible observation platforms suitable to operate in remote areas on demand. Accordingly, their usage has been proposed in several contexts such as research activities, military operations, environmental monitoring and oil exploration [1]. However, most of current USV remote control techniques are based on human-assisted technology thus a fully autonomous USV system is still an open issue [2].

The safety of the vehicle and the ability to complete the mission depends crucially on the capability of detecting objects on the sea surface, which is necessary for collision avoidance. Anti-collision systems for USVs typically require measurements collected from multiple sensors (e.g. Lidar, cameras, etc.), where each sensor has its own advantages and disadvantages in terms of resolution, field of view (FoV), operative range and so on [3].

Among the available sensing technologies, radar is capable of operating regardless of weather and visibility conditions, has moderate costs and can be easily adapted to operate within the marine environment. Furthermore, radar is characterized by an excellent coverage and high resolution along the range coordinate and it is also able to guarantee a 360° FoV in the horizontal plane.

Nautical radars are the most popular solutions to detect floating targets on the sea surface; however, they are bulky and not always effective in detecting small objects located very close to the radar.

This contribution investigates the applicability of a compact and lightweight 24 GHz multiple-input multiple-output (MIMO) radar originally developed for automotive applications to localize floating targets at short ranges (from tens to few hundreds of meters). In this frame, we propose an ad-hoc signal processing strategy combining MIMO technology, detection, and tracking algorithms to achieve target localization and tracking in a real-time mode. A validation of the proposed signal processing chain is firstly performed thanks to numerical simulations. After, preliminary field tests carried out in the marine environment are presented to assess the performance of the radar prototype and of the related signal processing.

References

• [1] Zhixiang et al. "Unmanned surface vehicles: An overview of developments and challenges", Annual Reviews in Control, vol. 41, pp. 71-93, 2016
• [2] Caccia, M. Bibuli, R. Bono, G. Bruzzone, “Basic navigation, guidance and control of an unmanned surface vehicle”, Autonomous Robots, vol. 25, no. 4, pp. 349-365, 2008
• [3] Robinette, M. Sacarny, M. DeFilippo, M. Novitzky, M. R. Benjamin, “Sensor evaluation for autonomous surface vehicles in inland waterways”, Proc. IEEE OCEANS 2019, pp. 1-8, 2019.

How to cite: Ludeno, G., Gennarelli, G., Noviello, C., Esposito, G., Catapano, I., and Soldovieri, F.: A 24 GHz MIMO radar for the autonomous navigation of unmanned surface vehicles, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4826, https://doi.org/10.5194/egusphere-egu22-4826, 2022.

Road pavements play a crucial role in the development of any construction as they provide safe surface on which vehicles can travel comfortably [1]. Pavements are multi-layered structures of processed and compacted materials in different thicknesses and in both unbound and bound forms with the function of supporting vehicle loads as well as providing a smooth riding quality. The condition of road pavement structures is susceptible to the impact of uncertain environmental factors and traffic loads, resulting in pavement deterioration over time. Therefore, the mechanical properties of pavements (such as strength, stiffness, etc.) need to be monitored on a regular basis to make sure that the pavement condition meets its prescribed threshold. The ground-penetrating radar (GPR) and deflection-based methods (e.g., the falling weight deflectometer (FWD)) are the most popular non-destructive testing (NDT) methods in pavement engineering science that are often used in combination to evaluate the damage and strength of pavements [2-4]. The layer thickness data from GPR scans are used as an input for deflection-based measurements to back-calculate the elastic moduli of the layers [2]. During the recent years, problems concerning the automatic interpretation of data from NDTs have received good attention and have simulated peer to peer interests in many industries like transportation. The use of Artificial Intelligence (AI) and Machine Learning (ML) techniques for the interpretation of NDT data can offer many advantages such as the improved speed and accuracy of analysis, especially for large-volume datasets. This study aims to train a dataset collected from GPR (2 GHz horn antenna) and the Curviameter deflection-based equipment using AI and ML algorithms to classify road flexible pavements based on their mechanical properties. Curviameter data are used as ground-truth measurements of pavement stiffness, whereas the GPR data provide geometric and physical attributes of the pavement structure. Several methods such as support vector machine (SVM), artificial neural network (ANN), and k nearest neighbours (KNN) are proposed and their performance in terms of accuracy of estimation of the strength and deformation properties of pavement layers are compared with each other as well as with the classical statistical methods. The results of this study can help road maintenance officials to identify and prioritise pavements at risk and make cost-effective and informed decisions for maintenance.

References

[1] Tosti, F., Bianchini Ciampoli, L., D’Amico, F. and Alani, A.M. (2019). Advances in the prediction of the bearing capacity of road flexible pavements using GPR. In: 10th International Workshop on Advanced GPR, European Association of Geoscientists & Engineers, pages 1-5.

[2] Plati, C., Loizos, A. & Gkyrtis, K. Assessment of Modern Roadways Using Non-destructive Geophysical Surveying Techniques. Surv Geophys 41, 395–430 (2020). 

[3] A. Benedetto, F. Tosti, Inferring bearing ratio of unbound materials from dielectric properties using GPR, in: Proceedings of the 2013 Airfield and Highway Pavement Conference: Sustainable and Efficient Pavements, June 2013, pp. 1336–1347.

[4] Tosti, F., Bianchini Ciampoli, L., D’Amico, F., Alani, A.M., Benedetto, A. (2018). An experimental-based model for the assessment of the mechanical properties of road pavements using GPR. Construction and Building Materials, Volume 165, pp. 966-974.

How to cite: Dinmohammadi, F., Bianchini Ciampoli, L., Tosti, F., Benedetto, A., and Alani, A. M.: On the use of Artificial Intelligence for classification of road pavements based on mechanical properties using ground-penetrating radar and deflection-based non-destructive testing data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1849, https://doi.org/10.5194/egusphere-egu22-1849, 2022.

Corrosion is one of the most critical issues leading to damage in reinforced concrete structures. In most cases, the detection of corrosion damage is performed by visual inspection. Other techniques (drilling cores with petrography or chemical examination, potential measurements, and resistivity measurements) require minimum destruction since they can be utilized by reaching the reinforcement bar [1]. Recently, there has been an increasing trend to use Ground Penetrating Radar (GPR) as one of the emerging non-destructive testing (NDT) techniques in the diagnosis of corrosion [2].

This paper focuses on a series of GPR tests on specimens constructed from poor-quality concrete and plain round bar. These specimens were subjected to accelerated corrosion tests under laboratory conditions. The corrosion intensity of those specimens is non-destructively assessed with GPR, by collecting data before and after corrosion tests. For GPR tests, the IDS Aladdin system was used with a double polarized 2 GHz antenna. Based on GPR measurement, Relative Dielectric Permittivity (RDP) values of concrete, are calculated based on the known dimension of specimens and two-way travel time (twt) values obtained from A-scans. The change in RDP values of specimens before and after exposure to corrosion is then computed. Moreover, amplitude change and variation in frequency spectrum before and after corrosion exposure are analyzed.

The results of this experimental study thus indicate that corrosion damage in reinforced concrete can be determined by using several GPR signal attributes. More laboratory tests are required for better quantification of the impact of the corrosion phenomenon in reinforced concrete.

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).

[1]        V. Sossa, V. Pérez-Gracia, R. González-Drigo, M. A. Rasol, Lab Non Destructive Test to Analyze the Effect of Corrosion on Ground Penetrating Radar Scans, Remote Sensing. 11 (2019) 2814. https://doi.org/10.3390/rs11232814.

[2]        K. Tešić, A. Baričević, M. Serdar, Non-Destructive Corrosion Inspection of Reinforced Concrete Using Ground-Penetrating Radar: A Review, Materials. 14 (2021) 975. https://doi.org/10.3390/ma14040975.

How to cite: Artagan, S., Borecky, V., Yurdakul, Ö., and Luňák, M.: Experimental assessment of corrosion influence in reinforced concrete by GPR, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-74, https://doi.org/10.5194/egusphere-egu22-74, 2022.

Street trees are a critical asset for the urban environment due to the variety of environmental and social benefits provided [1]. However, the conflicting coexistence of tree root systems with the built environment, especially with road infrastructure, frequently results in extensive damage, such as the uplifting and cracking of sidewalks and curbs, endangering pedestrians, cyclists, and road drivers’ safety.

Within this context, ground penetrating radar (GPR) is gaining recognition as an accurate non-destructive testing (NDT) method for tree roots’ assessment and mapping [2]. Nevertheless, the investigation methods developed so far are often inadequate for application on street trees, as these are often difficult to access. Recent studies have focused on implementing new survey and processing techniques for rapid tree root assessment based on combined time-frequency analyses of GPR data [3].  

This research also explores the adoption of a geostatistical approach for the spatial data analysis and interpolation of GPR data. The radial development of roots and the complexity of root network constitute a challenging setting for the spatial data analysis and the recognition of specific spatial features.

Preliminary results are therefore presented based on a geostatistical analysis of GPR data. To this end, 2-D GPR outputs (i.e., B-scans and C-scans) were analysed to quantify the spatial correlation amongst radar amplitude reflection features and their anisotropy, leading to a more reliable detection and mapping of tree roots. The proposed processing system could be employed for investigating trees difficult to access, such as road trees, where more comprehensive analyses are difficult to implement. Results' interpretation has shown the viability of the proposed analysis and will pave the way to further investigations.

Acknowledgements

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.

References

[1]         Tyrväinen, L., Pauleit, S., Seeland, K., & de Vries, S., 2005. "Benefits and uses of urban forests and trees". In: Urban Forests and Trees. Springer, Berlin, Heidelberg.

[2]         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.

[3]         Lantini, L., Tosti, F., Zou, L., Ciampoli, L. B., & Alani, A. M., 2021. "Advances in the use of the Short-Time Fourier Transform for assessing urban trees’ root systems." Earth Resources and Environmental Remote Sensing/GIS Applications XII. Vol. 11863. SPIE, 2021.

How to cite: Lantini, L., Trevisani, S., Gagliardi, V., Tosti, F., and Alani, A. M.: An investigation into road trees’ root systems through geostatistical analysis of GPR data, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6168, https://doi.org/10.5194/egusphere-egu22-6168, 2022.

In recent years, China's construction demand for shield tunnel in soft soil continues to increase, and the control of ground settlement in tunnel boring process affects the safety of the tunnel itself and its superstructure directly. Paying close attention to controlling the strata loss and the ground settlement by multiple means is important to ensure construction safety. In this paper, the intelligent real-time monitoring system with dual-frequency ground penetrating radar (GPR) is used to detect the quality of back-fill grouting of shield tunnel, while monitoring points are arranged on the ground surface to acquire the settlement values in real time. The collaborative analysis of ground and underground monitoring results reveals the relationship between grouting and settlement values, and realizes the dynamic guidance on grouting operation, which helps to achieve the purpose of controlling ground settlement better. Last but not least, this paper proposes an outlook on a multiple-data fusion system based on cloud computing platform to adapt to more complex and multiple data in the future, so as to achieve the higher accuracy, efficiency and intelligence of monitoring data analysis.

How to cite: Li, K., Xie, X., Zhang, X., Zhou, B., Qu, T., and Zeng, L.: Collaborative use of ground monitoring and GPR data for the control of ground settlement in shield tunnel in soft soil, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13441, https://doi.org/10.5194/egusphere-egu22-13441, 2022.

To the waterborne ground-penetrating radar detection, reverse-time migration (RTM) method can image the structure of the bottom of the water and locate the buried bodies. However, the image quality is limited by the attenuation of electromagnetic waves. How to compensate the attenuation becomes a critical problem. Some RTM methods related to the attenuation-compensated have been developed in recent years. We use the attenuation-compensated RTM based on the minus conductivity. However, the method is limited by the estimation of the attenuation coefficient. Here, we propose an attenuation-coefficient estimation method based on the centroid frequency downshift method (CFDS). In EM attenuation tomography, the centroid frequency downshift method works for attenuation estimation. Compared with the CFDS method in tomography, our proposal is based on the centroid frequency of the bottom-interface of water instead of the source wavelet. Thus, we can avoid the problem of the unknown source wavelet. The method is based on two assumptions: 1) GPR data can be regarded as zero-offset records. 2) Reflections from underwater interfaces are independent of frequency. In addition, the formula about the attenuation coefficient shows when the ratio between the conductivity and the product of the dielectric constant and the angular frequency is greater than one, the attenuation coefficient tends to be a constant. This does not meet the assumption that the attenuation coefficient is linearly related to frequency. We will select a proper frequency range to meet the linear relation by the spectral ratio method. Because the ratio of the signal spectrum of the bottom interface to the spectrum of the underwater interface is consistent with the change of the attenuation coefficient with frequency. Then, the CFDS method will acquire a linear attenuation coefficient with the frequency. Finally, we choose half of the central frequency to acquire the estimated attenuation coefficient. We design a layered waterborne GPR detection model, the conductivity of the silt layer varies between 0.1 and 0.01. The error of the conductivity estimation is below 10%. After acquiring the attenuation coefficient, the attenuation-compensated RTM works correctly and effectively.

How to cite: Shen, R., Zhao, Y., Cheng, H., and Ge, S.: Attenuation-compensated reverse-time migration of waterborne GPR based on attenuation coefficient estimation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2166, https://doi.org/10.5194/egusphere-egu22-2166, 2022.

Modern roadways provide road users with both a comfortable and safe ride to their destinations. Increases in traffic demands and maximum allowable loads imply that roadway authorities should also care for the structural soundness of pavements. In parallel, budgetary limitations and frequent road closures for rehabilitation activities, especially in heavy-duty motorways, might guide the related authorities to focus their strategies on the preservation of pavements functional performance. However, structural issues concerning pavement damage remain on the forefront, as pavement’s service life extends beyond its design life; thus structural condition assessment is required to ensure pavement sustainability in the long-term.

Non-Destructive Testing (NDT) has played a major role during condition monitoring and evaluation of rehabilitation needs. Together with input from visual inspections and/or sample destructive testing (e.g. coring), NDT data help to define indicators and threshold values that assist the related decision-making for pavement condition assessment. The most indicative tool for structural evaluation is the Falling Weight Deflectometer (FWD) that senses roadway surfaces through geophones recording load-induced deflections at various locations. Additional geophysical inspection data with the Ground Penetrating Radar (GRP) is used to estimate pavement’s stratigraphy. Integrating the above sensing data enables the estimation of pavement’s performance and its damage potential.

To this end, a major challenge that pavement engineers face, concerns the assumptions made about the mechanical characterization of pavement materials. Asphalt mixtures, located on the upper pavement layers, behave in a viscoelastic mode because of temperature- and loading frequency- dependency, whereas in the contrary, simplified assumptions for linear elastic materials are most commonly made during the conventional NDT analysis. In this research, an integration of mainly NDT data and sample data from cores extracted in-situ is followed to comparatively estimate the long-term pavement performance through internationally calibrated damage models considering different assumptions for asphalt materials. Two damage modes are considered including bottom-up and top-down fatigue cracks that are conceptually perceived as alligator cracks and longitudinal cracks respectively alongside a roadway’s surface. As part of an ongoing research for the long-term pavement condition monitoring, data from a new pavement was considered at this stage indicating a promising capability of NDT data towards damage assessment.

Overall, this study aims to demonstrate the power of pavement sensing data towards structural health monitoring of roadways pinpointing the significance of database development for a rational management throughout a roadway’s service life. Furthermore, data from limited destructive testing enriches the pavement evaluation processes with purely mechanistic perspectives thereby paving the way for developing integrated protocols with improved accuracy for site investigations, especially at project-level analysis, where rehabilitation design becomes critical.

How to cite: Gkyrtis, K., Loizos, A., and Plati, C.: Sensing roadway surfaces for a non-destructive assessment of pavement damage potential, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4914, https://doi.org/10.5194/egusphere-egu22-4914, 2022.

As a recognised non-destructive testing (NDT) tool, Ground Penetrating Radar (GPR) is becoming increasingly common in the field of environmental engineering [1]-[3]. GPR uses electromagnetic (EM) waves which travel at specific velocity determined by the permittivity of the material. With the development of new GPR signal processing methodologies, finding information on the physical properties of hidden targets has become a key target. Currently, only three types of approach could be applied for the quantitative estimation of permittivity from GPR data, i.e., hyperbola curve fitting, common middle point (CMP) velocity analysis and full-waveform inversion. However, the main challenges for the estimation of permittivity from GPR backscattered signals are to provide effective and accurate strategy for prediction.

In this research, we used a dual-polarimetric GPR system to estimate the dielectric constant of targets. The system is equipped with two 2GHz antennas polarised perpendicularly each to one another (HH and VV). The dual polarisation enables deeper surveying, providing images of both shallow and deeper subsurface features. Polarimetry is a property of EM waves that generally refers to the orientation of the electric field vector, which plays here an important role as it allows either direct or parameterisation permittivity effects within the scattering problem in the remote sensing [4].

The aim of this research is to provide a novel and more robust approach for dielectric constant prediction using a dual-polarimetric GPR system. To this extent, the relationship between the relative permittivity and the polarimetric alpha angle have been investigated based on data collected by a GPR system with dual-polarised antennas. The approach was then assessed by laboratory experiments where different moisture sand targets (simulating the effect of different relative permittivity targets) were measured. After signal processing, a clear relationship between the alpha angle and the relative permittivity was obtained, proving the viability of the proposed method.

Acknowledgements

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.

References

[1] Zou, L. et al., 2020. Mapping and Assessment of Tree Roots using Ground Penetrating Radar with Low-Cost GPS. Remote Sensing, vol.12, no.8, pp:1300.

[2] Zou, L. et al., 2020. On the Use of Lateral Wave for the Interlayer Debonding Detecting in an Asphalt Airport Pavement Using a Multistatic GPR System. IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 6, pp. 4215-4224.

[3] Zou, L. et al., 2021. Study on Wavelet Entropy for Airport Pavement Debonded Layer Inspection by using a Multi-Static GPR System. Geophysics, vol. 86, no. 3, pp. WB69-WB78.

[4] J. Lee and E. Pottier, Polarimetric Imaging: From Basics to Applications, FL, Boca Raton: CRC Press, 2009.

How to cite: Zou, L., Tosti, F., and Alani, A. M.: Dielectric Constant Estimation through Alpha Angle with a Polarimetric GPR System, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1544, https://doi.org/10.5194/egusphere-egu22-1544, 2022.

In the VHF frequencies, the sensitivity of the reflection coefficient at the air-soil interface with respect to the soil electromagnetic properties, i.e., the dielectric permittivity and electrical conductivity, varies with frequency. The lower the frequency is, the lower the sensitivity to permittivity is and the larger the sensitivity to conductivity is. In this study, we investigated low-frequency drone-borne ground-penetrating radar (GPR) and full-wave inversion for soil surface electrical conductivity characterization. In order to have a good sensitivity to electrical conductivity, we operated in the 15-45 MHz frequency range. We conducted both numerical and field experiments, under the assumptions that the soil magnetic permeability is equal to the magnetic permeability of free space, and that the soil permittivity and conductivity are frequency-independent. Through the numerical experiments, we analyzed the sensitivity of the soil permittivity and electrical conductivity by plotting the objective function in the inverse problem. In addition, we analyzed the effects of modelling errors on the retrieval of the permittivity and conductivity. The results show that the soil electrical conductivity is sensitive enough to be characterized by the low-frequency drone-borne GPR. The depth of sensitivity was found to be around 0.5-1 m in the 15-45 MHz frequency range. Yet, the effects of permittivity cannot be neglected totally, especially for relatively wet soils. For validating our approach, we conducted field measurements with the drone-borne GPR and we compared results with electromagnetic induction (EMI) measurements considering two different offsets, i.e., 0.5 and 1 m, respectively. The lightweight GPR system consists of a handheld vector network analyzer (VNA), a 5-meter half-wave dipole antenna, a micro-computer stick, a GPS receiver, and a power bank. The good agreement in terms of absolute values and field structures between the GPR and EMI maps demonstrated the feasibility of the proposed low-frequency drone-borne GPR method, which appears thereby to be promising for precision agriculture applications.

How to cite: Wu, K. and Lambot, S.: Analysis of low-frequency drone-borne GPR for soil surface electrical conductivity mapping, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8594, https://doi.org/10.5194/egusphere-egu22-8594, 2022.

Rebar Corrosion is one of the main causes of deterioration of engineering reinforced structure. This degradation reduces the service life and durability of the structures. Such degradation can result in the collapse of engineering structures. When the first cracks are noticed on the concrete surface, corrosion has generally reached an advanced stage and maintenance action is required. The early detection of rebar corrosion of bridges, tunnel, buildings and other civil engineering structures is important to reduce the expensive cost to repair the deteriorated structure. Several techniques have been developed for understanding the mechanism and kinetics of the corrosion of rebar, but the paper defines the interest of combining several NDT for field inspection to overcome the limitation of measuring instantaneous corrosion rates and to improve the estimation of the service life of RC structures. Non-destructive testing and evaluation of the rebar corrosion is a major issue for predicting the service life of reinforced concrete structures.

This paper introduces a laboratory test, that was performed at Geophysical Laboratory of Ferrara University. The test consisted in a multisensor application concerning rebar corrosion monitoring using different geophysical methods on a concrete sample of about 50 x 30 cm with one steel rebar of 10 mm diameter. An accelerating reinforcement bar corrosion using direct current (DC) power supply with 5% sodium chloride (NaCl) solution was used to induce rebar corrosion. The 2GHz GPR antenna by IDS, the ERT with Abem Terrameter and Self-Potential with Keithley multivoltmeter at high impedance were used for rebar corrosion monitoring. A multisensor approach should reduce the errors resulting from measurements, and improve synergistically the estimation of service life of the RC.

Each technique provided specific information, but a data integration method used in the operating system will further improve the overall quality of diagnosis. The collected data were used for an integration approach to obtain an evolution of the phenomenon of corrosion of the reinforcement bar. All the three methods were able to detect the physical parameter variation during the corrosion phenomena, but more attention is necessary on natural corrosion, that is a slow process and the properties of the experimental steel–concrete interface may not be representative of natural corrosion. However, each of these geophysical methods possesses certain advantages and limitations, therefore a combination of these geophysical techniques, with an multisensor approach is recommended to use to obtain the corrosion condition of steel and the condition of concrete cover.  Moreover, extrapolating laboratory results performed with a single rebar to a large structure with interconnected rebars thus remains challenging. Therefore, during the next experiments, special care must be taken regarding the design and preparation of the samples to obtain meaningful information for field application.

How to cite: Rizzo, E., Fornasari, G., Capozzoli, L., De Martino, G., and Giampaolo, V.: Rebar corrosion monitoring with a multisensor non-destructive geophysical techniques., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4437, https://doi.org/10.5194/egusphere-egu22-4437, 2022.

Maintenance and rehabilitation policies represent a task of paramount importance for managers and administrators of railway networks to maintain the highest standards of transport safety while limiting as much as possible the costs of maintenance operations.

To this effect, high-productivity survey methods become crucial as they allow for timely recognition of the quality of the asset elements, among which the ballast layers are the most likely to undergo rapid deterioration processes. Particularly, Ground Penetrating Radar (GPR) has received positive feedback from researchers and professionals due to the capability of detecting signs of deterioration within ballasted trackbeds that are not recognizable by a visual inspection at the surface, through high-productivity surveys. On the other hand, satellite-based surveys are nowadays being increasingly applied to the monitoring of transport assets. Techniques such as Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) allows evaluating potential deformations suffered by railway sections and their surroundings by analyzing phase changes between multiple images of the same area acquired at progressive times. 

For both of these techniques, despite the wide recognition by the field-related scientific literature, survey protocols and data processing standards for the detection and classification of the quality of ballast layers are still missing. In addition, procedures of integration and data fusion between GPR and InSAR datasets are still very rare.

The present study aims at demonstrating the viability of the integration between these two survey methodologies for a more comprehensive assessment of the condition of ballasted track-beds over a railway stretch. Particularly, a traditional railway section going from Cava de’ Tirreni to Salerno, Campania (Italy), was subject to both GPR and MT-InSAR inspections. An ad hoc experimental setup was realized to fix horn antennas with different central frequencies to an actual inspection convoy that surveyed the railway stretch in both the travel directions. Time-frequency methods were applied to the data to detect subsections of the railway affected by the poor quality of ballast (i.e. high rate of fouling). In parallel, a two-years MT-InSAR analysis was conducted to evaluate possible deformations that occurred to the railway line in the period before the GPR test. In addition, results from both the analyses were compared to the reports from visual inspections as provided by the railway manager.

The results of the surveys confirm the high potential of GPR in detecting the fouling condition of the ballast layers at various stages of severity. The integration of this information to the outcomes of InSAR analysis allows for identifying whether the deterioration of the track-beds is related to poorly bearing subgrades or rather to excessive stresses between the aggregates resulting in their fragmentation.

Acknowledgments

This research is supported by the Italian Ministry of Education, University, and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. Funding from MIUR, in the frame of the“Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged. The authors would also like to express their gratitude to RFI S.p.a. in the person of Eng. Pasquale Ferraro for the valuable support to the tests.

How to cite: Bianchini Ciampoli, L., Gagliardi, V., D'Amico, F., Clementini, C., Latini, D., and Benedetto, A.: Quality assessment in railway ballast by integration of NDT methods and remote sensing techniques: a study case in Salerno, Southern Italy, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2712, https://doi.org/10.5194/egusphere-egu22-2712, 2022.

It is a truism that pavements deteriorate due to the combined effects of traffic loads and environmental conditions. The manner or ability of a road to meet the demands of traffic and the environment and to provide at least an acceptable level of performance to road users throughout its life is referred to as pavement performance. An important indicator of pavement performance is ride quality. This is a rather subjective measure of performance that depends on (i) the physical properties of the pavement surface, (ii) the mechanical properties of the vehicle, and (iii) the acceptance of the perceived ride quality by road users. Due to the subjectivity of ride quality assessment, many researchers have worked in the past to develop an objective indicator of pavement quality. The International Roughness Index (IRI) is considered a good indicator of pavement performance in terms of road roughness. It was developed to be linear, transferable, and stable over time and is based on the concept of a true longitudinal profile. Following the identification and quantification of ride quality by the IRI, pavement activities include the systematic collection of roughness data in the form of the IRI using advanced laser profilers, either to "accept" an as-built pavement or to monitor and evaluate the functional condition of an in-service pavement.

On the other hand, pavement performance can vary significantly due to variations in layer thickness, primarily due to the construction process and quality control methods used. Even if a uniform design thickness is specified for a road section, the actual thickness may vary. It is expected that the layer thickness will have some probability distribution, with the highest density being around the target thickness. Information on layer thickness is usually obtained from as-built records, from coring or from Ground Penetrating Radar (GPR) surveys. GPR is a powerful measurement system that provides pavement thickness estimates with excellent data coverage at travel speeds. It can significantly improve pavement structure estimates compared to data from as-built plans. In addition, GPR surveys are fast, cost effective, and non-destructive compared to coring.

The present research developed a sensing approach that extends the capability of GPR beyond its ability to estimate pavement thickness. Specifically, the approach links GPR thickness to IRI based on the principle that a GPR system and a laser profiler are independent sensors that can be combined to provide a more complete image of pavement performance. To this end, field data collected by a GPR system and a laser profiler along highway sections are analyzed to evaluate pavement performance and predict future condition. The results show that thickness variations are related to roughness levels and specify the deterioration of the pavement throughout its lifetime.

How to cite: Plati, C., Loizos, A., and Georgouli, K.: An approach to integrate GPR thickness variability and roughness level into pavement performance evaluation, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-2253, https://doi.org/10.5194/egusphere-egu22-2253, 2022.

This paper deals with the evaluation of four measuring systems for the detection of potential deformations that can occur during structural rehabilitation measures. For this purpose, a test object resembling the shape of a tunnel structure was constructed. The structural properties of this test object are discussed in the related paper by Strauss et. al submitted for the same session.

In the paper, the installed measuring systems are presented first. These are a lamella system based on fibre optics, an array of accelerometers, a digital image correlation system and a profile laser scanner based system. The operating principles of the systems are briefly introduced.

A long-term measurement on the object in an unloaded state, which extended over several weeks, enables statements about the capturing of temperature-related deformations, the temperature dependence of the measured values and drift effects of the investigated systems. Selective loading of the test object was generated via four screw rods and applied both in the elastic as well as in the plastic deformation range. This enabled knowledge gain regarding the precision and the sensitivity of the analysed measuring systems.

Environmental conditions may have a strong influence on the measurement values. The former can be determined by permanent installations on the structure and its operating conditions as well as by the undertaken rehabilitation measures. Representative for the first category we investigated the influence of magnetic fields and light conditions on the measuring systems. For the second category, strong dust formation and increased humidity were generated during a test procedure.

An assessment regarding data handling, including storage, transfer and processing, completes the investigation of the four measuring systems. A summarising evaluation concludes the article.

How to cite: Neuner, H., Kostjak, V., Linzer, F., Loderer, W., Seywald, C., Strauss, A., Rigler, M., and Polt, M.: Assessing Deformation Monitoring Systems For Supporting Structural Rehabilitation under Harsh Conditions, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-7547, https://doi.org/10.5194/egusphere-egu22-7547, 2022.

The identification of the processes underlining natural systems often requires the adoption of multiple investigation techniques for the assessment of the sites under study. In this work, the combination of information derived from non-invasive sensing techniques, such as geophysics, remote sensing and hydrogeochemistry, highlights the possible influence of global climate change on the future water availability related to an aquifer in a peculiar glacier context, located in central Ecuador. In particular, we show that the Chambo aquifer, which supplies potable water to the region, does not contain fossil water, and it’s instead recharged over time. Indeed, the whole Chambo river basin is affected by the Chimborazo volcano, which is a glacerised mountain located in the inner tropics, one of the most critical places  to be observed in the frame of climate impact on water resources. Thanks to the infomation gathered by the various surveying techniques, numerical modelling permitted an estimate of the recharge, which can be fully originated by the runoff from Chimborazo melting glaciers. Actually, the retreat of the glaciers on top of the Chimborazo is an ongoing process presumably related to global climate change.

How to cite: Scozzari, A., Catelan, P., Chidichimo, F., de Biase, M., Mendoza Trujillo, B. G., Carrettero Poblete, P. A., and Straface, S.: Integration of multiple geoscientific investigation methods for a better understanding of a water system: the example of Chimborazo glaciers melting effects on the Chambo aquifer, Ecuador, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13153, https://doi.org/10.5194/egusphere-egu22-13153, 2022.

Monitoring of critical civil engineering infrastructures, and especially viaducts and bridges, has become a priority nowadays as the ageing of construction materials may cause damages and collapses with dramatic consequences. Following recent bridge collapses, specific guidelines on risk classification and management, safety assessment and monitoring of existing bridges have been issued in Italy, by the Minister of Infrastructure as a mandatory code [1]. Accordingly, several laws and regulations have been issued on the same topic, emphasizing the crucial role of BIM-based procedures for the design and management of civil infrastructures [2, 3]. Within this context, monitoring operations are generally conducted by on-site inspections and specialized operators, and rarely by high-frequency ground-based Non-Destructive Testing methods (NDTs). Furthermore, the implementation of satellite-based remote sensing techniques, have been increasingly and effectively used for the monitoring of bridges in the last few years [4]. Generally, these crucial pieces of information are analyzed separately, and the implementation of a multi-scale and multi-source interoperable BIM platform is still an open challenge [5].

This study aims at investigating the potential of an interoperable and upgradeable BIM platform supplemented by non-destructive survey data, such as Mobile Laser Scanner (MLS), Ground Penetrating Radar (GPR) and Satellite Remote Sensing Information (i.e. InSAR). The main goal of the research is to contribute to the state-of-the-art knowledge on BIM applications, by testing an infrastructure management platform aiming at reducing the limits typically associated to the separate observation of these assessments, to the advantage of an integrated analysis including both the design information and the routinely updated results of monitoring activities.

The activities were conducted in the framework of the Project “M.LAZIO”, approved by the Lazio Region, with the aim to develop an informative BIM platform of the investigated bridges interoperable within a Geographic Information System (GIS). As on-site surveys are carried out , a preliminary multi-source database of information  is created, to be operated as the starting point for the integration process and the development of  the infrastructure management platform. Preliminary results have shown promising viability of the data management model for supporting asset managers in the various management phases, thereby proving this methodology to be worthy for implementation in infrastructure integrated monitoring plans.

Acknowledgements

This research is supported by the Project “M.LAZIO”, accepted and funded by the Lazio Region, Italy. Funding from MIUR, in the frame of the “Departments of Excellence Initiative 2018–2022”, attributed to the Department of Engineering of Roma Tre University, is acknowledged.

References

[1] MIT, 2020. Ministero delle Infrastrutture e dei Trasporti, DM 578/2020

[2] EU, 2014. Directive 2014/24/EU of the European Parliament and of the Council of 26 February 2014 on public procurement and repealing Directive 2004/18/EC.

[3] MIMS, 2021. Ministero delle Infrastrutture e della Mobilità Sostenibile, DM 312/2021

[4] Gagliardi, V. et al., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”. In SPIE Future Sensing Technologies; https://doi.org/10.1117/12.2579700

[5] D'Amico F. et al., "A novel BIM approach for supporting technical decision-making process in transport infrastructure management", Proc. SPIE 11863;  https://doi.org/10.1117/12.2600140

How to cite: Bertolini, L., Napolitano, A., Diezmos Manalo, J., Gagliardi, V., Bianchini Ciampoli, L., and D'Amico, F.: Implementation of an interoperable platform integrating BIM and GIS information for network-level monitoring and assessment of bridges, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9845, https://doi.org/10.5194/egusphere-egu22-9845, 2022.

Knowledge of the monument for its conservation is the result of a multidisciplinary work based on the integration of different data sources obtainable from historical research, architectural survey, the use of different imaging technologies. The latter are increasingly within the reach of conservators, architects and restoration companies thanks to the reduction of costs and to the effort to produce increasingly user-friendly imaging technologies both in terms of data acquisition and processing. The critical element is the interpretation of the results on which depends the effectiveness of these technologies in answering various questions that the restoration poses. Scientific literature suggests different approaches aimed at making the interpretation of imaging diagnostics easier, particularly by means of : i) the comparison between direct data (carrots, visual inspection) and results from non-invasive tests; ii) the use of specimens or laboratory test beds; iii) Virtual and Augmented reality (VR/AR) to be used as a work environment to facilitate the interpretation of non invasive imaging investigations. In particular, the reading and visualization of multiparametric information using VR/AR contents increases the standard modes for the transmission of knowledge of physical characteristics and state of conservation of the architectural heritage. This approach represents an effective system for storing and analysing heterogeneous data derived from a number of diverse non invasive imaging techniques, including Ground Penetrating radar (GPR) at high frequency, Infrared Thermography (IRT), Seismic tomography and other diagnostics techniques. In the context of Heritage Within Project, a VR/AR platform to interrelate heterogeneous data derived from GPR, IRT, Ultrasonic and sonic measurements along with  results finite element computations has been developed and applied to the Convent of Our Lady of Mount Carmel  in Lisbon to understand cause-and-effect mechanisms between the constructive characteristics, degradation pathologies and stress/deformation maps.

References

Gabellone F., Leucci G., Masini N., Persico R., Quarta G., Grasso F. 2013. Non-destructive prospecting and virtual reconstruction of the chapel of the Holy Spirit in Lecce, Italy. Near Surface Geophysics, doi: 10.3997/1873-0604.2012030

Gabellone F., Chiffi M., “Linguaggi digitali per la valorizzazione”, in F. Gabellone, M. T. Giannotta, M. F. Stifani, L. Donateo (a cura di), Soleto Ritrovata. Ricerche archeologiche e linguaggi digitali per la fruizione. Editrice Salentina, 2015. ISBN 978-88-98289-50-9

Masini N., Nuzzo L., Rizzo E., GPR investigations for the study and the restoration of the Rose Window of Troia Cathedral (Southern Italy), Near Surface Geophysics, 5 (5)(2007), pp. 287-300, ISSN: 1569-4445; doi: 10.3997/1873-0604.2007010 

Masini N., Soldovieri F. (Eds) (2017). Sensing the Past. From artifact to historical site. Series: Geotechnologies and the Environment, Vol. 16. Springer International Publishing, ISBN: 978-3-319-50516-9, doi: 10.1007/978-3-319-50518-3, pp. 575

Javier Ortega, Margarita González Hernández, Miguel Ángel García Izquierdo, Nicola Masini, et al. (2021). Heritage Within. European Research Project, ISBN: 978-989-54496-6-8, Braga 2021.

How to cite: Masini, N., Gabellone, F., and Ortega, J.: VR/AR based approach for the diagnosis of the state of conservation of the architectural heritage. The case of the Convento do Carmo in Lisbon, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10538, https://doi.org/10.5194/egusphere-egu22-10538, 2022.

Detecting decay in tree trunks is essential in considering tree health and safety. Continual monitoring of tree trunks is possible using a digital model, which can contain incremental assessment data on tree health. Researchers have previously employed non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) to study both the external and internal physical dimensions of objects and structures [1], including tree trunks [2]. Light Detection and Ranging (LiDAR) technology is also continually employed in infrastructure and asset management to generate models and to detect surface displacements with millimeter accuracy [3]. Nevertheless, the scanning of structures using these existing state-of-the-art technologies can be time consuming, technical, and expensive.

This work investigates the design and implementation of a smartphone app for scanning tree trunks to generate a 3D digital model for later visualization and assessment. The app uses LiDAR technology, which has recently become available in smart devices, for instance, the Apple iPhone 12+ and the iPad Pro. With the prevalence of internet-of-things (IoT) sensors, digital twins are being increasingly used in a variety of industries, for example, architecture and manufacturing. A digital twin is a digital representation of an existing physical object or structure. With our app, a digital twin of a tree can be developed and maintained by continually updating data on its dimensions and internal state of decay. Further, we can situate and visualize tree trunks as digital objects in the real world using augmented reality, which is also possible in modern smart devices. We previously investigated tree trunks using GPR [2] to generate tomographic maps, to denote level of decay. We aim to adopt a data integration and fusion approach, using such existing (and incremental GPR data) and an external LiDAR scan to gain a full 3D ‘picture’ of tree trunks.

We intend to validate our app against state-of-the-art techniques, i.e., laser scanning and photogrammetry. With the ability to scan tree trunks within reasonable parameters of accuracy, the app can provide a relatively low-cost environmental modelling and assessment solution for researchers and experts.

Acknowledgments: Sincere thanks to the following for their support: 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.

References

[1] Alani A. et al., Non-destructive assessment of a historic masonry arch bridge using ground penetrating radar and 3D laser scanner. IMEKO International Conference on Metrology for Archaeology and Cultural Heritage Lecce, Italy, October 23-25, 2017.

[2] Tosti et al., "The Use of GPR and Microwave Tomography for the Assessment of the Internal Structure of Hollow Trees," in IEEE Transactions on Geoscience and Remote Sensing, Doi: 10.1109/TGRS.2021.3115408.

[3] Lee, J et al., Long-term displacement measurement of bridges using a LiDAR system. Struct Control Health Monit. 2019; 26:e2428.

How to cite: Uzor, S., Tosti, F., and Alani, A. M.: Low-cost scanning of tree trunks for analysis and visualization in augmented reality using smartphone LiDAR and digital twins, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3247, https://doi.org/10.5194/egusphere-egu22-3247, 2022.

DIARIT^Sup is a chain of various softwares following the concept of ”system of systems”. It interconnects hardware and software layers dedicated to in-situ monitoring of structures or critical components. It embeds data assimilation capabilities combined with specific Physical or Statistical models like inverse thermal and/or mechanical ones up to the predictive ones. It aims at extracting and providing key parameters of interest for decision making tools. Its framework natively integrates data collection from local sources but also from external systems [1, 2]. DIARIT^Sup is a milestone in our roadmap for SHM Digital Twins research framework. Furthermore, it intends providing some useful information for maintenance operations not only for surveyed targets but also for deployed sensors.

Thanks to its Model-view-controller (MVC) design pattern, DIARIT^Sup can be extended, customized and connected to existing applications. Its core component is made of a supervisor task that handles the gathering of data from local sensors and external sources like the open source meteorological data (observations and forecasts) from Météo-France Geoservice [4] for instance. Meanwhile, a recorder manage the recording of all data and metadata in the Hierarchical Data Format (HDF5) [6]. HDF5 is used to its full potential with its Single-Writer-Multiple-Readers feature that enables a graphical user interface to represent the saved data in real-time, or the live computation of SHM Digital Twins models [3] for example. Furthermore, the flexibility of HDF5 data storage allows the recording of various type of sensors such as punctual sensors or full field ones. Finally, DIARIT^Sup is able to handle massive deployment thanks to Ansible [5] automation tool and a Gitlab synchronization for automatic updates. An overview of the developed software with a real application case will be presented. Perspectives towards improvements on the software with more component integrations (Copernicus Climate Data Store, etc.) and a more generic way to configure the acquisition and model configuration will be finally discussed.

References
[1] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Infrared thermography applied to the study of heated and solar pavement: from numerical modeling to small scale laboratory experiments”. In: SPIE - Thermosense: Thermal Infrared Applications XXXIX. Anaheim, United States, Apr. 2017. url: https://hal.inria.fr/hal-01563851.
[2] Thibaud Toullier, Jean Dumoulin, and Laurent Mevel. “Study of measurements bias due to environmental and spatial discretization in long term thermal monitoring of structures by infrared thermography”. In: QIRT 2018 - 14th Quantitative InfraRed Thermography Conference. Berlin, Germany, June 2018. url: https://hal.inria.fr/hal-01890292.
[3] Nicolas Le Touz, Thibaud Toullier, and Jean Dumoulin. “Study of an optimal heating command law for structures with non-negligible thermal inertia in varying outdoor conditions”. In: Smart Structures and Systems 27.2 (2021), pp. 379–386. doi: 10.12989/sss.2021.27.2.379. url: https://hal.inria.fr/hal-03145348.
[4] Météo France. Données publiques Météo France. 2022. url: https://donneespubliques.meteofrance.fr.
[5] Red Hat & Ansible. Ansible is Simple IT Automation. 2022. url: https://www.ansible.com/.
[6] The HDF Group. Hierarchical Data Format, version 5. 1997-2022. url: https://www.hdfgroup.org/HDF5/.

How to cite: Dumoulin, J., Toullier, T., Simon, M., and Andrade-Barroso, G.: DIARITSup: a framework to supervise live measurements, Digital Twins modelscomputations and predictions for structures monitoring., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-11201, https://doi.org/10.5194/egusphere-egu22-11201, 2022.