Discontinuity surfaces are well known to influence the mechanical behaviour of rocks under compression. Non-destructive techniques, such as ultrasonic pulse velocity and sclerometers, are increasingly used to estimate uniaxial compressive strength of rocks. In this study, several core samples derived from the doubling works of the railway network near Genga (Marche Region, Central Italy) were analysed in order to assess the influence of the structural geological context (proximity to folds, faults etc.) and tectonic deformation on rock strength. Tests were conducted in rock specimens through: i) conventional uniaxial compressive experiment, ii) non-destructive rebound-based methods such as Schmidt Hammer and Equotip  and iii) ultrasound. In this way, it was possible to make a critical analysis of the use of these techniques in the estimation of the uniaxial compressive strength (considering also information about discontinuity type, orientation and nature of the filling). Finally, a petrographic analysis using optical microscope has been undertaken as a support to the observations derived from the analysis at the sample scale. The results indicate that there are two main factors influencing the strength at the scale of the specimen. The first and most decisive factor is the presence of natural pre-existing fractures. The second is the tectonic deformation ratio: the greater the deformation is, the little the strength. Furthermore, through the combined use of uniaxial compressive experiment, non-destructive rebound-based methods and ultrasounds it was possible to highlights the advantages and limitations of each technique and define/propose new guidelines for their use. 

How to cite: Mammoliti, E., Gironelli, V., Jablonska, D., Mazzoli, S., Ferretti, A., Morici, M., and Francioni, M.: Influence of tectonic deformation on the mechanical properties of calcareous rocks: drawbacks of the non-destructive techniques , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14658, https://doi.org/10.5194/egusphere-egu23-14658, 2023.

Non-destructive and high-resolution hyperspectral analyses are widely used in planetary and environmental sciences and in mining exploration. In recent years, the scanning method was applied to lacustrine sediment cores in complement to XRF core scanning. However, this approach was rarely applied to marine sediments. The Gaoping canyon, located south of Taiwan island, is connected to the Gaoping River and is a very active canyon with large sediment transfer capacity. In particular, about 4 typhoon-driven hyperpycnal flows have been recorded by mooring systems in every recent year. Studying their frequency and intensity responding to past climate and environmental changes is a key to understand future tropical storm frequency and related climate variability. Core MD18-3574 was collected on the western levee of the Gaoping canyon and displays numerous fine laminations (millimetric to centimetric) recording the deposition of the gravity flows occurring in the canyon and on the slope. In this study, we combined non-destructive analyses such as XRF core scanning and hyperspectral imaging with high-resolution grain size and XRD bulk mineralogy analyses to understand the sedimentological and geochemical variations at the scale of the laminae. Core MD18-3574 sediments consist mainly of fine silt, presenting an alternance of fine-grained and coarse-grained laminations. The average mean grain size is 13.4 µm ranging from 9 to 20.5 µm. Thick coarser grained laminations are showing grain size distributions and asymmetric sorting of typical turbidite sequence. Grain size and bulk mineralogy display great visual and statistical correlation with XRF (Fe/Ca, Si/Al) and hyperspectral proxies (sediment darkness (Rmean), Clay_R2200). Principal component analyses (PCA) demonstrates that darker laminae are composed of coarser sediments with high Si/Al (quartz and feldspar-rich) and Clay_R2200 values and low Fe/Ca (calcite-rich) resulting from gravity flows.  Inversely, lighter laminae consist of finer sediments with low Si/Al (muscovite and illite-rich), Clay_R2200 and high Fe/Ca resulting from hemipelagic deposition. Thus, such interpretation was extended to the core scale to identify gravity flows deposits layers. Moderate intensity tropical storm frequency is decreasing since the last 4 ka in response to the sea surface temperature (SST) decrease and enhanced East Asian winter monsoon since the middle Holocene. Tropical storm intensity increased after 2 ka in La Niña like periods indicating that the surge of super-typhoons hitting Taiwan could be triggered by El Niño Southern Oscillation (ENSO) state and variability. We can then assess that tropical storm activity is controlled by SST, monsoon system and ENSO conditions. This study brings new insights in the prediction of the ongoing climate change impacts on storms activity in the western Pacific Ocean.

How to cite: Bertaz, J., Jacq, K., Colin, C., Liu, Z., debret, M., Zhao, H., and Lin, A. T.-S.: High-resolution grain-size analysis and non-destructive hyperspectral imaging of sediments from the Gaoping canyon levee to establish past typhoon and monsoon activities affecting Taiwan during the late Holocene, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13163, https://doi.org/10.5194/egusphere-egu23-13163, 2023.

The hydrogen isotope fractionation between leaf wax compounds and source water, the apparent fractionation (ε_app), necessary for the reconstruction of hydrogen isotopic composition (δD) of precipitation, is mainly assessed through field and transect studies. The current ε_app dataset, however, exhibit a bias toward mid-latitude regions of the Northern Hemisphere. Here we report the results of an outdoor experiment wherein four evergreen and three deciduous species were grown with water of known δD value (-1.8‰) in a tropical semi-arid monsoon region. This allowed us to estimate ε_app more accurately and also quantify ε_app variability within a species and among different species. Among-species ε_app values varied by -119 ± 23‰ (for n-alkane of chain length n-C₃₁) and -126 ± 27‰   (for n-alkanoic acid of chain length n-C₃₀). The similarity of the among-species variability in ε_app reported here and that observed in field and transect studies suggested the species-effect, rather than uncertainty in δD of source water, control the uncertainty in community-averaged ε_app. The fractionation of  δD between n-C₂₉ alkane and n-C₃₀ alkanoic acid (ε_29/30) and between n-C₃₁ alkane and n-C₃₂ alkanoic acid (ε_31/32) were 7 ± 25‰ and 6 ± 15‰, respectively, suggesting minimal fractionation of hydrogen isotopes during decarboxylation. Further, as we did not observe a systematic difference between the ε_app of deciduous and evergreen species; changes in the relative proportion of this vegetation in a community might not affect its ε_app value.

How to cite: Saishree, A., Managave, S., and Sarangi, V.: Hydrogen isotope fractionation between leaf wax compounds and source water in tropical angiosperms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16471, https://doi.org/10.5194/egusphere-egu23-16471, 2023.

Geological mixtures having endmembers mixed at a fine scale pose a challenge to estimating their fractional abundances. Light incident on these mixtures interacts both at multilayered and surface levels, resulting in volumetric and albedo scattering, respectively. Accounting for these effects necessitates a nonlinear spectral mixing model approach rather than conventional linear mixing. In this study, we evaluate the performances of linear and various nonlinear spectral mixing models for an intimately mixed geological mixture, i.e., a banded hematite quartzite (BHQ) sample. The BHQ sample with distinct endmembers of hematite and quartzite facilitated our study of the behavior of light on two-component nonlinear mixtures. In a laboratory-based experimental setup, we used a spectroradiometer of full spectral range in the visible and near-infrared regions (350 to 2500nm) to acquire a hyperspectral image of the BHQ sample. It was followed by the identification of nonlinearly mixed regions and inferring changes in their spectral features. The nonlinearity induced in these regions was attributed to two significant causes- (1) the fine scale of spectral mixing and (2) the spectroradiometer sensor’s limited ability to spatially distinguish between focused and neighboring points, thereby producing a point spread effect. We observed the effects of nonlinear spectral mixing for our sample by changing the sensor’s height from 1mm to 5mm, to simulate fine and coarse-resolution images, respectively. The spectral mixing was modeled using the existing mapped ground truth fractional abundances and library endmembers’ spectra by linear mixing and established nonlinear techniques of the generalized bilinear model (GBM), polynomial post-nonlinear model (PPNM), kernel-based support vector machines (k-SVMs). The evaluated performance metric of reconstruction error revealed the nonlinearity effect in image pixels through statistical tests and nonlinearity parameters used in these models. It was further observed that the associated nonlinearity increases from fine to coarse-resolution images. The minimum error of image reconstruction was observed for the polynomial post-nonlinear model, with a single nonlinearity parameter and an average reconstruction error (ARE) of 0.05. Our study provided insights into the nature of nonlinear mixing with endmember composition and particle sizes.

How to cite: Sahoo, M. M., Rajendran, K., Pattathal Vijayakumar, A., Mathew, S. K., and Porwal, A.: Evaluation of Spectral Mixing Techniques for Geological Mixture in a Laboratory Setup: Insights on the nature of mixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12226, https://doi.org/10.5194/egusphere-egu23-12226, 2023.

Ground subsidence is a typical geological hazard in urban areas. It endangers the safety of infrastructures, such as subways. In this study, the ground subsidence risk of Shanghai metro lines was mapped and assessed. Firstly, PS-InSAR was used for the ground subsidence survey, and subsidence intensity was divided into five classes according to subsidence velocity. 10 subsidence causal factors were collected and the frequency ratio method was applied to analyze the correlation between subsidence and its causal factors. Then LightGBM model was used to generate a ground subsidence susceptibility map. And receiver operating characteristic curve and area under the curve (AUC) were adopted to assess the model. And AUC is 0.904, which suggests the model's performance is excellent. Finally, a risk matrix was introduced to consider the intensity and susceptibility of ground subsidence. The risk of ground subsidence was mapped and classified into five levels: R1 (very low), R2 (low), R3 (medium), R4 (high), and R5 (very high). The results showed that the risk of subway ground subsidence exhibited a regional-related characteristic. Metro lines located in areas with higher ground subsidence risk levels also had higher ground subsidence risk levels. Meanwhile, the statistical results of subway ground subsidence risk levels showed that subway stations were safer than sections.

How to cite: Chai, L., Xie, X., Zhou, B., and Zeng, L.: Ground subsidence risk mapping and assessment along Shanghai metro lines by PS-InSAR and LightGBM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10874, https://doi.org/10.5194/egusphere-egu23-10874, 2023.

Satellite sensors need to make a trade-off between revisit frequency and spatial resolution. This work presents a spatio-temporal image fusion method called Unpaired Spatio-Temporal Fusion of Image Patches (USTFIP). This method combines data from different multispectral sensors and creates images combining the best of each satellite in terms of frequency and resolution. It generates synthetic images and selects optimal information from cloud-contaminated images, to avoid the need of cloud-free matching pairs of satellite images. The removal of this restriction makes it easier to run our fusion algorithm even in the presence of clouds, which are frequent in time series of satellite images. The increasing demand of larger datasets makes necessary the use of computationally optimized methods. Therefore, this method is programmed to run in parallel reducing the run-time with regard to other methods. USTFIP is tested through an experimental scenario with similar procedures as Fit-FC, STARFM and FSDAF. Finally, USTFIP is the most robust, since its prediction accuracy deprecates at a much lower rate as classical requirements become progressively difficult to meet.

How to cite: Goyena, H., Pérez-Goya, U., Montesino-San Martín, M., F. Militino, A., Atkinson, P. M., and Ugarte, M. D.: Relaxing requirements for spatio-temporal data fusion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6795, https://doi.org/10.5194/egusphere-egu23-6795, 2023.

Continual monitoring of tree roots, which is essential when considering tree health and safety, is possible using a digital model. Non-destructive techniques, for instance, laser scanning, acoustics, and Ground Penetrating Radar (GPR) have been used in the past to study both the external and internal physical dimensions of objects and structures [1], including trees [2,3]. Recent studies have shown that GPR is effective in mapping the root system's network in street trees [3]. Light Detection and Ranging (LiDAR) technology has also been employed in infrastructure management to generate 3D data and to detect surface displacements with millimeter accuracy [4]. However, scanning such structures using current state-of-the-art technologies can be expensive and time consuming. Further, continual monitoring of tree roots requires multiple visits to tree sites and, oftentimes, repeated excavations of soil.

This work proposes a Virtual Reality (VR) system using smartphone-based LiDAR and GPR data to capture ground surface and subsurface information to monitor the location of tree roots. Both datasets can be visualized in 3D in a VR environment for future assessment. LiDAR technology has recently become available in smartphones (for instance, the Apple iPhone 12+) and can scan a surface, e.g., the base of a tree, and export the data to a 3D modelling and visualization application. Using GPR data, we combined subsurface information on the location of tree roots with the LiDAR scan to provide a holistic digital model of the physical site. The system can provide a relatively low-cost environmental modelling and assessment solution, which will allow researchers and environmental professionals to a) create digital 3D snapshots of a physical site for later assessment, b) track positional data on existing tree roots, and c) inform the decision-making process regarding locations for potential future excavations.

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. The Authors would also like to thank Mr Dale Mortimer (representing the Ealing Council) and the Walpole Park for facilitating this research.

References

[1] Alani A. M. 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] Ježová, J., Mertens, L., Lambot, S., 2016. “Ground-penetrating radar for observing tree trunks and other cylindrical objects,” Construction and Building Materials (123), 214-225.

[3] Lantini, L., Alani, A. M., 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.

[4] 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., Lantini, L., and Tosti, F.: Low-cost assessment and visualization of tree roots using smartphone LiDAR, Ground-Penetrating Radar (GPR) data and virtual reality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6908, https://doi.org/10.5194/egusphere-egu23-6908, 2023.

Measuring soil reflectance in the field, rather than in a laboratory setting, can be very useful when it comes to numerous applications such as mapping the distribution of various soil properties, especially when prompt estimations are needed.  Recent advances in spectroscopy, and specifically in the development of low-cost Micro-Electro-Mechanical-Systems (MEMS) based spectrometers, pave the way for developing real-time applications in agriculture and environmental monitoring. Compared to high-end spectrometers, whose spectral range extends from Visible (VIS) and Near-InfraRed (NIR) to Shortwave InfraRed (SWIR), MEMS cover limited parts of the electromagnetic spectrum resulting in missing important information. In parallel, new space missions such as Planet Fusion are operationally ready and provide optical imagery (RGB and NIR) with high spatial (3m) and temporal (daily) resolution. To this end, we assessed the potential of augmenting the bands captured from a commercial MEMS sensor (Spectral Engines Nirone S2.2 @ 1750 – 2150 nm) by adjoining the Planet Fusion bands at the exact sampling date and location that in-situ scans originate.

Employing the above, a set of portable MEMS was used at a pilot area in Cyprus (Agia Varvara, Nicosia district) to develop a regional in-situ Soil Spectral Library (SSL). A set of 60 distinct locations were selected for capturing in situ spectral reflectance after the stratification of Planet Fusion pixels of the pilot area, while a physical soil sample was analyzed at the laboratory for the determination of Soil Organic Carbon (SOC) content. During the visit, topsoil moisture was also measured.

The resulting SSL, containing the in-situ spectra, SOC, and moisture content was further augmented by the 4 bands of Planet Fusion imagery acquired on the exact date of the field visit. At this stage, three Random Forest models for SOC content estimation were fitted using as explanatory variables initially only the MEMS data with moisture content, then Planet Fusion bands, and finally all three available inputs.

The results presented an observable decrease in RMSE of SOC content estimations when fusing in-situ with spaceborne data, highlighting the importance of the information contained at VIS-NIR when modeling SOC. On the other hand, the synergy of the two sensors is mutually beneficial; SOC absorption bands can also be found in the SWIR region and are hard to detect with remote sensing means since they fall within the strong water absorption region (around 1950 nm). MEMS-based systems operating at the SWIR part can support this process, and if combined with ancillary environmental measurements such as soil moisture, can provide a cost-effective solution for measuring SOC and other soil-related parameters. To loosen the necessity of laboratory analysis, it is necessary to establish protocols and guidelines for spectral data collection and management to ensure that the data collected is consistent and of high quality and develop representative SSLs that can be used to serve different modeling scenarios. 

How to cite: Karyotis, K., Tsakiridis, N., and Zalidis, G.: Fusion of in-situ and spaceborne sensing for environmental monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14899, https://doi.org/10.5194/egusphere-egu23-14899, 2023.

Geoelectric non-destructive imaging and monitoring of the earth's subsurface requires robust and adaptable numerical methods to solve the governing differential equation. Most of the time, the DC data is acquired along a straight line. Hence, we solve the DC problem for the 2D case. But the source for the DC method exhibits a 3D nature. To account for the source's 3D nature, the 2D DC resistivity modeling is often carried out in the wavenumber domain. There have been studies that suggest ways for the selection of optimum wavenumbers and weights. But, this does not guarantee a universal choice of wavenumbers. The chosen wavenumbers and related weights strongly influence the precision of the resulting solution in the space domain. Many forward modeling studies demonstrate that selecting effective wavenumbers is challenging, especially for complicated models with topography, anisotropy, and significant resistivity differences. Moreover, forward modeling requires many wavenumbers as the models get more complex. 

This study focuses on developing a method that can completely omit wavenumbers for 2D DC resistivity modeling. The present work finds its motivation in a numerical experiment on a simple half-space model. Since the analytical response for such a model can be easily calculated, we match the analytical solution against the responses obtained from various wavenumbers and weights used in the literature. All the responses deviated from the analytical solution after a certain distance, and none of them were found to be accurate for large offsets. It was discovered after thorough testing of the numerical scheme that the wavenumbers selected for the forward modeling significantly impacted how practical the approach is for large offsets. 

To overcome this problem, a new boundary condition is derived and implemented in the existing numerical scheme. The numerical scheme chosen to perform the forward modeling is Mimetic Finite Difference Method (MFDM). We consider that the source is placed on the origin of the coordinate system. This removes the dependency of the source term, expressed in the Fourier domain, on the wavenumber. The solution obtained by solving the resulting equation will be an even function of the wavenumber and be real-valued. This ensures that the potential in the space domain for the 2D model will also be a real-valued even function with a symmetry about a plane perpendicular to the strike direction and passing through the origin. Because the first-order derivative of an even function at the plane of symmetry vanishes, mathematically, it can be expressed as a Neumann boundary condition at the considered plane. Therefore, we propose a scheme to solve the 2D resistivity problem in the space domain using the boundary condition mentioned here.

The developed algorithm is tested on isotropic and anisotropic two-layer models with large contrasts. It is found that the numerical solutions obtained using the modified boundary condition described above show considerable accuracy even for large offsets when compared with the analytical solution. On the other hand, the results obtained using available wavenumbers in the literature are also compared and are found to deviate considerably from the analytical solution at large offsets.

How to cite: Suryavanshi, D. and Dehiya, R.: Development of a flexible 2D DC Resistivity modelling technique for use in space domain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16632, https://doi.org/10.5194/egusphere-egu23-16632, 2023.

As the Earth's lungs, trees are a natural resource that provide, amongst others, food, lumber, and oxygen. Therefore, monitoring these wooden structures with non-destructive testing (NDT) techniques such as ground penetrating radar (GPR) and ultrasound can provide valuable information about inner flaws and decays, which is an essential step for tree conservation.  

In recent years, GPR and ultrasound have been used to delineate the interior architecture of tree trunks [1-3]. However, more research is required to improve results and consequently have a more reliable interpretation. Due to limitations in depth penetration and signal-to-noise ratio [4], these approaches have a limited capacity for resolving features. The use of gain functions and higher frequencies to compensate for wave attenuation may exaggerate events and reduce resolution, respectively.

In this context, an integration between GPR multi-frequency and ultrasound data can be used to address this issue. Data were collected on a tree trunk log at the Faringdon Centre for Non-Destructive Testing and Remote Sensing using two high-frequency GPR systems (2GHz and 4GHz central frequencies) and an ultrasound (supporting a wide range of transducers from 24 kHz up to 500 kHz) testing equipment. Internal features of interest in terms of extended perimetric air gaps at the bark-wood interface, natural cracks and small artificial cavities were investigated through electromagnetic and mechanical waves. After compilation of data, a joint interpretation strategy for data analysis is developed. The processed data were mapped against the cut sections of the tree for validity purposes.

Although study of stand tree trunks would be more challenging, the findings of this research may be applied for wood timbers and pave the way to future research for living tree trunks.

Acknowledgements

This research was funded by the Vice-Chancellor’s PhD Scholarship at the University of West London.

References

[1] Arciniegas, A., et al., Literature review of acoustic and ultrasonic tomography in standing trees. Trees, 2014. 28(6): p. 1559-1567. 

[2] Giannakis, I., et al., Health monitoring of tree trunks using ground penetrating radar. IEEE Transactions on Geoscience and Remote Sensing, 2019. 57(10): p. 8317-8326.

[3] Espinosa, L., et al., Ultrasound computed tomography on standing trees: accounting for wood anisotropy permits a more accurate detection of defects. Annals of Forest Science, 2020. 77(3): p. 1-13.

[4] Tosti, F., et al., The use of GPR and microwave tomography for the assessment of the internal structure of hollow trees. IEEE Transactions on Geoscience and Remote Sensing, 2021. 60: p. 1-14.

How to cite: Parnow, S., Lantini, L., Uzor, S., Alani, A. M., and Tosti, F.: Joint Interpretation of Multi-Frequency Ground Penetrating Radar and Ultrasound Data for Mapping Cracks and Cavities in Tree Trunks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8762, https://doi.org/10.5194/egusphere-egu23-8762, 2023.

Health monitoring and disease mitigation of trees are essential to ensure the sustainability of wood industry, safety of ecosystems, and maintenance of climatic conditions. Several non-destructive testing methods have been applied to monitor and detect the decays inside the trunks. Among them, ground penetrating radar (GPR) has gained recognition due to its high efficiency and good resolution. However, due to the wide beam width of the antenna pattern and the complicated scattering caused by the trunk structure, the recorded GPR profile is far from the actual geometry of the tree trunk. Moreover, the irregular contour of the tree trunk makes traditional data processing algorithms difficult to be performed. Therefore, an efficient migration algorithm with high resolution, as well as a high accuracy survey-line positioning method for curved contour of the trunk should be developed.

In this paper, a combined approach is proposed to image the inner structures inside the irregular-shaped trunks. In the first step, the 3D contour of the targeted tree trunk is built up by a 3D point cloud technique via photographing around the trunk at various angles. Subsequently, the 2D irregular contour of the cross-section of trunk at the position of the GPR survey line is extracted by the Canny edge detection method to locate the accurate position of each GPR A-scans [1]. Thirdly, the raw GPR profile is pre-processed to suppress undesired noise and clutters. Then, an RTM algorithm based on the zero-time imaging condition is applied for image reconstruction using the extracted 2D contour [2]. Lastly, a denoising method based on the total variation (TV) regularization is applied for artifact suppression in the reconstructed images [3].

Numerical, laboratory and field experiments are carried out to validate the applicability of the proposed approach. Both numerical and laboratory experimental results show that the RTM can yield more accurate and higher resolution images of the inner structures of the tree cross section than the BP algorithm. The proposed approach is further applied to a diseased camphor tree, and an elliptical decay defect is found the in the migrated GPR image. The results are validated by a visual inspection after the tree trunk was sawed down.

Fig. 1 Field experiment. (a) Geometric reconstruction result using point cloud data, (b) migrated result by the RTM algorithm and (c) bottom view of the tree trunk after sawing down. The red and yellow ellipses indicate the cavity and the decay region in the trunk, respectively.

References:

[1] Canny, "A Computational Approach to edge detection," IEEE Transactions on Pattern Analysis and Machine Interllgent, vol. PAMI-8, no. 6, pp. 679-698, 1986, doi: 10.1109/TPAMI.1986.4767851.

[2] S. Chattopadhyay and G. A. McMechan, "Imaging conditions for prestack reverse-time migration," Geophysics, vol. 73, no. 3, pp. S81-S89, 2008, doi: 10.1190/1.2903822.

[3] L. I. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992, doi: 10.1016/0167-2789(92)90242-F.

How to cite: Chen, Z., Liu, H., Xu, M., Yue, Y., and Zhang, B.: Decay diagnosis of tree trunks using 3D point cloud and reverse time migration of GPR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4861, https://doi.org/10.5194/egusphere-egu23-4861, 2023.

Monitoring and protection of natural resources have grown increasingly important in recent years, since the effect of emerging illnesses has caused serious concerns among environmentalists and communities. In this regard, tree roots are one of the most crucial and fragile plant organs, as well as one of the most difficult to assess [1].

Within this context, ground penetrating radar (GPR) applications have shown to be precise and effective for investigating and mapping tree roots [2]. Furthermore, in order to overcome limitations arising from natural soil heterogeneity, a recent study has proven the feasibility of deep learning image-based detection and classification methods applied to the GPR investigation of tree roots [3].

The present research proposes an analysis of the effect of root orientation on the GPR detection of tree root systems. To this end, a dedicated survey methodology was developed for compilation of a database of isolated roots. A set of GPR data was collected with different incidence angles with respect to each investigated root. The GPR signal is then processed in both temporal and frequency domains to filter out existing noise-related information and obtain spectrograms (i.e. a visual representation of a signal's frequency spectrum relative to time). Subsequently, an image-based deep learning framework is implemented, and its performance in recognising outputs with different incidence angles is compared to traditional machine learning classifiers. The preliminary results of this research demonstrate the potential of the proposed approach and pave the way for the use of novel ways to enhance the interpretation of tree root systems.

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. The Authors would also like to thank the Ealing Council and the Walpole Park for facilitating this research.

References

[1] Innes, J. L., 1993. Forest health: its assessment and status. CAB International.

[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., Massimi, F., Tosti, F., Alani, A. M. and Benedetto, F. "A Deep Learning Approach for Tree Root Detection using GPR Spectrogram Imagery," 2022 45th International Conference on Telecommunications and Signal Processing (TSP), 2022, pp. 391-394.

How to cite: Lantini, L., Massimi, F., Sotoudeh, S., Mortimer, D., Benedetto, F., and Tosti, F.: A Study on the Effect of Target Orientation on the GPR Detection of Tree Roots Using a Deep Learning Approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8384, https://doi.org/10.5194/egusphere-egu23-8384, 2023.

This paper describes laboratory tests performed with an NDT geophysical methods: Ground Penetrating Radar (GPR), Self Potential (SP) and Direct Current (DC) methods in order to monitor the corrosion of a rebar embedded in concrete. Even if the GPR is a common geophysical method for reinforced concrete structures, the SP and DC techniques are not widely used. Rebar corrosion is one of the main causes of deterioration of engineering reinforced structures and this degradation phenomena reduces their service life and durability. Non-destructive testing and evaluation of the rebar corrosion is a major issue for predicting the service life of reinforced concrete structures.

Several new experiments were performed at Applied Geophysical laboratory of University of Ferrara, following the experiences coming from previous tests (Fornasari et al., 2022), where two reinforced concrete samples of about 50 cm x 30 cm were cast, with a central ribbed steel rebar of 10 mm diameter and 35 cm long, were partially immersed in a plastic box with salty and distilled water. In this experiment, we applied a new protocol, where an epoxy resin was used in order to focalize the corrosion only along the exposed part of the rebar. The steel rebar was partially painted with a waterproof resin in order to leave only the central part uncovered for a length of 8 cm. The same waterproof epoxy resin was applied on part of the concrete sample, in order to have a specific chlorides diffusion across a freeway zone of about 10cm x 8cm defined below the exposed rebar.

The experiments were carried out on two identically constructed reinforced concrete samples, exposed to distilled water (sample “A”) and the second, exposed to a salty water with chlorides (sample “B”). Both samples were partially immersed for only 1 cm form the lower surface. The sample B was immersed in a salty water plastic box with different NaCl concentrations. An initial NaCl concentration of 0.1 % was adopted for 7 days, then the concentration was increased to 1% and finally to 3.5% for further 7 days. The experiment was set up in two phases. In the first phase of this study, we monitored the "natural" corrosion occurred on sample "B" due to the diffusion of chlorides towards the steel rebar comparing the obtained data with those of sample "A" exposed to distilled water. In the second phase of the study, accelerated corrosion was applied to sample "B" in order to induce an increment of the corrosion phenomena. The accelerated corrosion was designed in order to reach different theoretical levels of mass weight loss in the steel rebar, which were of 2%, 5%, 10% and 20%. During the experiments, 2GHz C-Thrue GPR antenna, Multivoltmeter with non-polarized calomel referenced electrode for SP and ABEM Terrameter LS for resistivity data, were used to monitor the rebar corrosion monitoring. The collected data were used for an integration observation to detect the evolution of the corrosion phenomenon on the reinforcement steel rebar and to define a quantitative analysis of the phenomena.

How to cite: Fornasari, G., Zanotto, F., Balbo, A., Grassi, V., and Rizzo, E.: Combined use of NDT methods for steel rebar corrosion monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13329, https://doi.org/10.5194/egusphere-egu23-13329, 2023.

The possibility to estimate accurately the subsurface electric properties of the pavements from ground-penetrating radar (GPR) signals using inverse modeling is obstructed by the appropriateness of the forward model describing the GPR subsurface system. In this presentation, we improved the recently developed approach of Lambot et al. whose success relies on a stepped-frequency continuous-wave (SFCW) radar combined with an off-ground monostatic transverse electromagnetic horn antenna. The deep-learning based method were adopted to train an intelligent model including the waveform of the Green’s functions. The method was applied and validated in laboratory conditions on a tank filled with a two-layered sand subject to different water contents. Results showed agreement between the predictions of measured Green’s functions deep-learning model and the measured ones. Model inversions for the dielectric permittivity and heights of antenna further demonstrated for a comparison of presented method.

How to cite: Zeng, L., Zhou, B., Xie, X., and Lambot, S.: Pavements Layered Media Characterizations using deep learning-based GPR full-wave inversion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13934, https://doi.org/10.5194/egusphere-egu23-13934, 2023.

One of the main priorities for road administrations and stakeholders is the management and monitoring of critical infrastructures, especially transportation infrastructures. In this context, Building Information Modeling (BIM) can be one of the more effective methodologies to be used to optimize the management process. In Italy, several laws and regulations have been issued, making the use of BIM procedures mandatory for the design of new infrastructures and emphasizing its role in the management of existing civil works [1, 2].

Monitoring operations of transportation infrastructures are generally conducted by on-site surveys. Non-Destructive Testing methods (i.e., GPR, LiDAR, Laser Profilometer, InSAR, etc.) have been used to perform these inspections as their outputs have been proven to be effective in determining the conditions of the infrastructure and its assets [3]. Moreover, BIM methodology could prove a valuable tool to manage the data provided by these surveys, as it consists in the creation of digital models capable of containing information related to the object that they are representing. These models can be used to store over time the different information obtained by the NDT surveys to carry out integrated analysis on the conditions of the infrastructure [4].

This study aims to analyze a potential BIM process capable of integrating different NDT surveys’ outputs, to generate an informative digital model of an infrastructure and its assets. The proposed methodology is then able to merge the data provided by the inspections, which is typically obtained by different operators and comes in different file formats, in a single BIM model. The main goal of the research is to provide a process to optimize the management procedures of transportation infrastructures, by creating digital models capable of reducing the problems typically associated with the monitoring and maintenance of these critical civil works. By merging different information in a single environment and relying on survey data that are commonly analyzed separately, an integrated analysis of the infrastructure can be carried out and data loss can be reduced.

The study was developed by relying on real data, obtained from on-site surveys carried out over Italian infrastructures. As different outputs have been collected, BIM models of different assets of the analyzed infrastructures were defined. Preliminary results have shown that the proposed methodology can be a viable tool for optimizing the management process of these critical civil works.

Acknowledgements

The research is supported by the Italian Ministry of Education, University and Research under the National Project “Extended resilience analysis of transport networks (EXTRA TN): Towards a simultaneously space, aerial and ground sensed infrastructure for risks prevention”, PRIN 2017. Prot. 20179BP4SM.

References

[1] MIT, 2018. Ministero delle Infrastrutture e dei Trasporti, D. Lgs 109/2018

[2] MIT, 2021. Ministero delle Infrastrutture e dei Trasporti, D.M. 312/2021

[3] D’Amico F. et al., 2020. Integration of InSAR and GPR Techniques for Monitoring Transition Areas in Railway Bridges. NDT&E Int

[4] D’Amico, F. et al., 2022. Integrating Non-Destructive Surveys into a Preliminary BIM-Oriented Digital Model for Possible Future Application in Road Pavements Management. Infrastructures 7, no. 1: 10

How to cite: Bertolini, L., D'Amico, F., Napolitano, A., Manalo, J. R. D., and Bianchini Ciampoli, L.: Combined NDT data for road management through BIM models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14846, https://doi.org/10.5194/egusphere-egu23-14846, 2023.

Building Information Modeling is a software-based parametric design approach that allows a full interoperability between the various actors involved in a design or management process. Notwithstanding It has been specifically created for buildings projects, its use has been adapted to a wide range of applications, including transport infrastructure design and, more recently, cultural heritage. In regard to this field, it has been mainly applied to raise accuracy and effectiveness of restoring and stabilization activities for historical architectures.
The present study aims at demonstrating how the use of BIM may return remarkable outcomes in improving the current quality level of digital valorisation and virtual reconstructions of historical structures, especially when their rate of conservation is limited. Indeed, even though current digital reconstruction models are, usually, verified under an archaeological perspective, their structural consistency is never tested. This involves that many virtual reconstruction models are likely to represent structures that are historically accurate but that have no structural sense as, according to their geometric features and the construction materials/techniques, they would not stand their weight.
In this perspective, this study proposes a novel BIM-based methodology capable of both driving the archaeological reconstruction hypotheses and testing the reconstruction hypotheses on a structural basis. The model can be schematically represented by the following process:
1- Survey of the emerging: acquisition of data from superficial archaeological surveys (topographic data, laser scanner, aero photogrammetry, satellite images)
2- Survey of the hidden: acquisition of data from hypogeal surveys (georadar, electrical tomography, magnetometry);
3- Mechanical characterization: gathering of information concerning the materials of the find, proven in their mechanic qualities also through load stress tests;
4- Virtual reconstruction: proposal of a possible hypothesis of virtual reconstruction linked to structural and morphological features known to be present in the referred historical periods;
5- Structural test: engineering and structural confirmation of the forwarded hypothesis by means of finite element algorithms.
The proposed methodology was tested on the archaeological area of the Villa and Circus of Maxentius along the Ancient Appian Way in Rome; all the planned activities have been shared and authorized by the Sovrintendenza Capitolina ai Beni Culturali, within the context of the Project BIMHERIT, funded by Regione Lazio (DTC Lazio Call, Prot. 305-2020-35609).

How to cite: Santarelli, R. and Ten, A.: Integration of non-destructive surveys for BIM-based and structural-verified digital reconstruction of archaeological sites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17489, https://doi.org/10.5194/egusphere-egu23-17489, 2023.

Nowadays, there is an emerging demand from public authorities and managing bodies, to evaluate the overall health of infrastructures and identify the most critical transport assets. Considering the national-scale level, thousands of transport infrastructure are in critical conditions and require urgent maintenance actions. Currently, most of available Digital Twins (DT) allow to explore and visualize data including limited kind of information. This issue still limits the operative and practical use by infrastructure owners, that require fast solutions for managing several amount of data. Moreover, this idea is perfectly in line with European and national actions related to the development of a DT of the earth’s systems, including the “DestinE” programme of the European Commission by EUSPA and the European Space Agency (ESA). For this purpose, a dynamic DT model of a critical infrastructure is developed, using the available data about design information, historical maintenance operations and monitoring surveys based on satellite imageries.

In this context, this study presents an innovative concept of Digital Twin, which integrates all the details coming from NDTs surveys, on-site inspections and satellite-based information, to store, manage and visualize valuable information. This is made possible by analysing the main several gaps and limitations of existing platforms, providing a viable integrated solution developing an upgradable strategic analysis tool. To this purpose, remote sensing methods are identified as viable technologies for continuous monitoring operations. More specifically, data coming from satellites and the processing techniques, such as the Multi-Temporal SAR Interferometry approach, are strategic for the continuous monitoring of the displacements associated to transport infrastructures. An advantage of these techniques is the lighter data-processing required for the assessment of displacements and the detection of critical areas [1, 2].

The study introduces two main levels of innovation. The first one is associated to the integrated approach for transportation planning, integrating quantitative data from multi-sources, into the more traditional territorial analysis models. The second one is related to the technological engineering discipline, and it consists of the fusion of observation data from multi-source, with the last-generation dynamic data connected to the environment.

Acknowledgements

This research is supported by the Project “M.LAZIO”, accepted and funded by the Lazio Region, Italy.

References

[1] D'Amico, F. et al., “Implementation of an interoperable BIM platform integrating ground based and remote sensing information for network-level infrastructures monitoring”, Spie Remote Sensing 2022.

[2] Gagliardi, V. et al., “Bridge monitoring and assessment by high-resolution satellite remote sensing technologies”, Spie Future Sensing Technologies 2020.

How to cite: Napolitano, A., Gagliardi, V., Bertolini, L., Manalo, J. R. D., Calvi, A., and Benedetto, A.: Implementation of a Digital Twin integrating remote sensing information for network-level infrastructure monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14981, https://doi.org/10.5194/egusphere-egu23-14981, 2023.

Infrastructure networks are crucial elements to ensure the sustainability of the current development model in which the movement of people and goods is essential. On the other hand, transport assets are increasingly exposed to several issues, including climatic conditions changing, vulnerability and exposure to natural hazards such as hydraulic, geomorphological, landslides and seismic phenomena, which can affect the structural integrity causing damages and deteriorations. The context is made even more serious by the degradation of materials and the progressive ageing of infrastructure, often accelerated by environmental conditions and inadequate, or not always effective, maintenance actions. This requires the investigation of novel methods for the large-scale detection of network-scale linear infrastructures, and simultaneously, of detail to diagnose causes and determine the priorities for the most effective countermeasures.

The proposed solution is based on a Data-Fusion approach, merging data coming from multi-source and multi-scale data, to enhance the interpretation process in a holistic sense. The information comes from spaceborne Multi-temporal SAR Interferometry, complemented by more detailed aerial data, detected by UAVs and Ground Based Non-Destructive Testing Methods, including laser scanner surveys for resolution and digital integrability, high-resolution camera measurements assisted by artificial intelligence for the surface degradation and from prospecting data collected by Ground Penetrating Radar technology. All these data can be simultaneously analyzed into a comprehensive digital platform, providing a useful tool to support operators and public bodies to prioritize maintenance actions.

The digital platform can be investigated also using augmented reality tools, capable of generating and reproducing the Digital Twin of the inspected infrastructure into a real environment. This allows any monitoring evaluation through a diagnostic technique that integrates spatial, aerial, ground-based and geophysical surveys, allowing navigation within the infrastructure. Potential applications are numerous, ranging from mapping of wide areas affected by potential criticality to the definition of the main vulnerabilities related to the seismic and hydraulic risks, the analysis of land changes surrounding the assets following extreme natural events, and the reconstruction of historical deformative trends of roads, railways and bridges through the interpretation of SAR data.

Acknowledgments

This research is supported by the Italian Ministry of Education, University, and Research under the National Project “EXTRA TN”, PRIN2017, Prot. 20179BP4SM. In addition, this research is supported by the Project “MLAZIO” funded by Lazio Region (Italy).

How to cite: Gagliardi, V., Bianchini Ciampoli, L., D'Amico, F., Calvi, A., and Benedetto, A.: Novel perspectives in transport infrastructure management: Data-Fusion, integrated monitoring and augmented reality, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15542, https://doi.org/10.5194/egusphere-egu23-15542, 2023.