Over the past three decades, GPR has been implemented for various applications in civil engineering infrastructure, including predicting in-situ density of AC pavements. Given the sensitivity of asphalt concrete (AC) layer density to compaction effort during construction, real-time AC density monitoring is invaluable. Unlike traditional approaches that estimate in-situ AC density (e.g., extracted cores and nuclear gauge measurements), ground penetrating radar (GPR) technology may be used for real-time prediction of AC density, allowing modification of compaction pattern and effort on the fly. A new GPR mount prototype was designed, allowing direct installation on various roller compactor types. The design was field validated.

Electromagnetic mixing theory is used, and bulk AC dielectric constant is related to its components’ dielectric constants and their corresponding volumetric proportions. The AC volumetrics could be determined from the job mix formula. The relative dielectric constants of binder and air are known to be approximately 3 and 1, respectively. However, cores are usually required to back-calculate the aggregates’ dielectric constant, which depends on aggregate minerology. In this study, aggregate dielectric constant values were used from an established aggregate dielectric constant database for Illinois.

Six stone matrix asphalt (SMA) test sections were constructed using different aggregate types at the Illinois Center for Transportation. GPR data were collected for each roller pass during compacting the test sections. An automated density prediction tool was developed to provide the roller operator with real-time prediction of the AC density. The AC predicted densities were compared to values obtained from nuclear gauge measurements and extracted cores densities. The GPR-predicted AC densities were better correlated to ground truth core densities than those by a nuclear gauge. In addition to ensuring AC quality when the introduced system is used, cost savings and emission reduction could be realized when compaction effort is optimized. 

How to cite: Abufares, L. and Al-Qadi, I.: Asphalt Concrete Density’s Monitoring during Construction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2382, https://doi.org/10.5194/egusphere-egu24-2382, 2024.

Effective forestry and urban park management, and disease detection strongly depend on tree trunks’ interior health conditions. At present, traditional destructive methods, such as coring, are used to analyse internally the tree structures. However, these are time-consuming, laborious, potentially harmful to the tree, and they only provide local information on the actual trunk conditions.

In recent years, Ground Penetrating Radar (GPR) has been extensively employed as a non-destructive, fast, and cost-effective method to map internal structures of tree trunks [1].

The visual interpretation of conventional GPR maps generated by a common offset antenna array is frequently characterised by non-uniqueness and ambiguity. However, when utilising alternative GPR antenna arrays that can return information e.g., velocity, permittivity, and electrical conductivity in media, the data collection process becomes time-consuming. The importance of investigation time is often underscored in tree assessment surveys, especially across extensive areas. In this research, it is proposed to employ GPR attributes to enhance the interpretation of results achieved with common offset antenna array systems, specifically concerning tree trunks.

Attribute analysis, a technique employed in seismic studies since the 1970s [2], is applied here to extract GPR attributes – i.e., quantities derived from GPR data and related with the characteristics of the tree trunk, such as moisture content and decay. To this purpose, the following sequential steps are followed:

• Data Acquisition: High-resolution GPR and Light Detection and Ranging (LiDAR) data are acquired by scanning the tree trunks. The GPR technique allows for the penetration of electromagnetic waves into the trunk, capturing reflections from internal structures. LiDAR can precisely locate GPR A-scans in their actual positions, rectifying GPR data distortion over complex tree trunk surface geometries [3].
• Attribute Analysis: Attributes are extracted both before and after the GPR data processing, depending on their type. The extracted attributes are then correlated with the trunk properties, such as decay position and size, and moisture-related information.
• Validation: The outcomes of the proposed method are validated and assessed with the output of other conventional and non-destructive methods, including LiDAR observations made on test trees.

The results of this study demonstrate a good correlation between the extracted attributes and the observed factors. Findings will have a substantial influence on the implementation of forestry and urban park management strategies, facilitating the adoption of well-informed decisions in the pursuit of sustainable forestry and conservation goals.

Keywords: Ground Penetrating Radar (GPR), tree management practices, tree trunk assessment, signal attribute analysis.

Acknowledgements

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

How to cite: Parnow, S., Lantini, L., Uzor, S., and Tosti, F.: Enhancing Tree Management Practices by Extracting GPR Attributes for the Evaluation of Tree Trunk Internal Structures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11475, https://doi.org/10.5194/egusphere-egu24-11475, 2024.

Buried-pipe leakage is a common issue in urban water distribution systems worldwide.  Apart from environmental problems such as water waste and pollution, leakage can lead to serious soil erosion and, in some cases, road collapse if not detected and repaired promptly. To date, various approaches for leakage detection and localization have been developed, including ground microphone, district metered areas, closed-circuit television, infrared thermal imaging, and Ground Penetrating Radar (GPR). Among these approaches, ground microphone uses listening devices to detect and localize the pipeline leakage by directly tracing the sound emitted at the point of leakage. However, the accuracy of ground microphone method heavily relies on the experience of the operator, as the sound signal is often contaminated by ambient noise. To address this issue, we combine the noise-resistant GPR method with the ground microphone method. With advantage of high resolution and efficiency, GPR has been widely applied in the localization of buried pipelines and thus has great potential in the detection of leaks.

This paper proposes a combined approach to detect and localize the leaks of buried pipelines. Firstly, the ground microphone method is used to collect acoustic data above the buried pipelines. During this step, the acoustic signals are processed to improve the signal-to-noise radio by using wavelet analysis [1] and loudness units referenced to digital full scale. Then, the relationship between the accuracy and the recognition precision of ground microphone data is analyzed. In the next step, a machine learning-based classifier [2] is established based on the features of acoustic data of buried pipelines, enabling automatic recognition of leaks. Finally, 3D GPR investigation is performed and a relative wavelet entropy (RWE) [3] method is introduced to localize the leakage point.

A laboratory and two filed experiments were carried out to validate the proposed approaches. In the laboratory experiment, we tested the RWE method, and the results show that the method can accurately localize the leaky point from 3D GPR data. Then, the results of two filed tests indicated that the combined approach effectively combines the advantages of ground microphone and GPR, which can efficiently and accurately detect and localize the buried pipeline leaks. The proposed approaches can benefit the health operation of water distribution system in urban cities.

References:

[1] G. Mallat, "A theory for multiresolution signal decomposition: the wavelet representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 11, no. 7, pp. 674-693, 1989, doi:10.1109/34.192463.

[2] Qu, H. Feng, Z. Zeng, J. Zhuge and S. Jin, "A SVM-based pipeline leakage detection and pre-warning system," Measurement, vol. 43, no. 4, pp. 513-519, 2010, doi:10.1016/j.measurement.2009.12.022.

[3] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schürmann and E. Başar, "Wavelet entropy: a new tool for analysis of short duration brain electrical signals," Journal of Neuroscience Methods, vol. 105, no. 1, pp. 65-75, 2001, doi:10.1016/s0165-0270(00)00356-3.

How to cite: Deng, X., Liu, H., Wang, Y., and Meng, X.: Buried pipe leak detection and localization via ground microphone and GPR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13911, https://doi.org/10.5194/egusphere-egu24-13911, 2024.

The deterioration of civil infrastructures such as bridges, roads, and buildings poses substantial economic and safety threats. The timely identification and remediation of defects in concrete structures can mitigate costlier future repairs and prevent catastrophic failures [1], [2].

While reinforced concrete is favoured for its strength and affordability, the embedded steel is vulnerable to corrosion induced by environmental exposure and ageing. Current evaluation methods only detect severe corrosion levels, providing insufficient warning for preventative action [3].

Within this context, this research focuses on developing non-destructive testing techniques for the early detection of steel reinforcement corrosion. This project investigates the application of emerging technologies - ground-penetrating radar (GPR) and photogrammetry - to enable earlier corrosion identification in concrete specimens.

The initial phase of the proposed research was dedicated to establishing testing capabilities and workflows to continuously monitor corrosion progression. Concrete samples with rebar were then subjected to accelerated corrosion in a controlled lab environment. Subsequently, GPR was employed to capture subsurface information related to rebar deterioration, while photogrammetry quantified 3D surface cracks and damage. The investigation data will have the potential to characterise corrosion severity versus concrete and rebar conditions to formulate new corrosion rating guidelines in the future.

The outcomes of this preliminary research have the potential to facilitate shifting infrastructure maintenance from reactive to proactive strategies, potentially reducing repair costs and promoting resilience.

Keywords: Concrete; Corrosion Detection; Photogrammetry; Ground Penetrating Radar; Infrastructure Maintenance

References

[1]     S. Ahmad, “Reinforcement corrosion in concrete structures, its monitoring and service life prediction––a review,” Cement and Concrete Composites, vol. 25, no. 4-5, pp. 459-471, 2003. 
[2]     T. Bachiri, A. Khamlichi and M. Bezzazi, “Detection of rebar corrosion in bridge deck by using GPR,” International Conference on Non-Destructive Evaluation of Composite Structures (NDECS 2017), vol. 191, 2018. 
[3]     J. E. Ramón-Zamora, J. R. Lliso-Ferrando, A. Martínez-Ibernón and J. M. Gandía-Romero, “Corrosion Assessment in Reinforced Concrete Structures by Means of Embedded Sensors and Multivariate Analysis—Part 1: Laboratory Validation,” Sensors, vol. 23, no. 21, 2023. 

How to cite: Keihani, R. and Tosti, F.: Early Detection of Reinforcement Corrosion in Concrete Structures: A Preliminary Investigation Using Non-Destructive Testing Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6601, https://doi.org/10.5194/egusphere-egu24-6601, 2024.

Restoration or maintenance of historical monuments and buildings require profound knowledge of the history of the structure and its internal properties. This task requires interdisciplinary efforts that minimize destructive approaches.

Particularly moisture intrusions often pose significant threats for historic buildings. Thus, knowledge of the mechanisms of moisture ingress is of crucial importance for the realization of a sustainable and resource-saving restoration.  A usual way to get information about the inner conditions of the examined object is by removing material selectively by core drillings. The drillings are supplemented by a visual assessment and evaluation of the changes near the surface. However, this method is limited not only in the scale of the investigated area but also with respect to the depth of the investigated structure. Therefore, methods are needed allowing non-destructive and ideally rapid assessment of larger parts of the structure. An integrative approach is essential, not only to remedy acute damage, but also to take preventive measures and ensure the long-term preservation of historic buildings. The aim of this contribution is to demonstrate the feasibility of non-destructive geophysical measurements techniques for moisture detection during the renovation process, including Infrared Thermography (IR), Ultrasound (US) and Ground-Penetrating-Radar (GPR).  The focus is on adapting measurement principles from the field of geophysics to transfer them into practicable measurement and evaluation methods for historic buildings and detection of moisture.         
We report on results obtained by geophysical investigations of objects in Northern Germany. Results show that it is possible to identify moisture and classify the non-visible internal properties of both brick masonry and timber structures non-destructively.  The combination of Ultrasound, GPR and Thermography allow to quantify and monitor material properties during restoration by repeated measurements. 

How to cite: Esel, Y., Schulte-Kortnack, D., Erkul, E., and Meier, T.: Non-destructive moisture monitoring of historical load-bearing structures with Thermography, Ultrasound and Ground Penetrating Radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15482, https://doi.org/10.5194/egusphere-egu24-15482, 2024.

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. The research work of the Geophysical Laboratory of University of Ferrara is committed to the study of RC corrosion. Several laboratory tests (Fornasari et al., 2022; Fornasari et al., 2023; Fornasari et al., 2024) were performed on a reinforced concrete beam where a corrosion was induced. In detail, this paper describes the results coming from Ground Penetrating Radar (GPR) and Ultrasonic Pulse Velocity (UPV) methods on induced corrosion experiment. A reinforced concrete sample of about 50 cm x 30 cm was used in this experiment. The concrete beam has a central ribbed steel rebar of 10 mm diameter and partially covered with epoxy resin, in order to focalize the corrosion only along the exposed part of the rebar (8cm). 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 concrete sample was partially exposed (1cm) to a salty water 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 used instruments were GPR Proceq GP8000 with 2GhZ antenna and UPV Cronosonic MAE with Tx-Rx at 55KhZ. The NDT acquisitions were carried out along the same line across the central part of the buried rebar during the accelerated corrosion test. The GPR elaboration (Hilbert function) highlighted an increase of the envelope factor values with time corrosion of the rebar. In contrast, ultrasound data obtained from the rebar revealed a decrease in velocity as corrosion increased. Over the past few years, numerous experiments have been conducted using various NDT methods, each capable of illustrating signal variations during the corrosion phenomena. These results emphasize the sensitivity of NDT methods in detecting rebar corrosion. The use of multi-sensor tools serves as the starting point for integrated observation, facilitating the transition from qualitative assessments to monitoring the evolving corrosion phenomenon on reinforced steel rebars. This approach aims to establish a quantitative analysis of the observed phenomena.

How to cite: Rizzo, E., Fornasari, G., Polastri, N., and Mariacci, T.: NDT methods (GPR and UPV) for steel rebar corrosion monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5339, https://doi.org/10.5194/egusphere-egu24-5339, 2024.

The growing importance of monitoring and preserving natural resources underscores the need for effective tree root assessment, particularly in the context of sustainable urban planning and ecosystem management. Tree roots, vital yet elusive plant organs, pose a significant challenge for accurate evaluation [1].

Ground-penetrating radar (GPR) has emerged as a valuable tool in this regard. Recent applications have focused on developing methodologies for tree root assessment in challenging conditions, such as the use of frequency-based spectrogram imagery for the assessment of urban trees [2], and the use of deep learning methods for the automatic recognition of tree roots [3].

Acknowledging the critical role of tree roots and the challenges associated with their assessment, the need for a method that balances precision with practicality needs to be addressed. To this end, this study presents a comparative analysis of two distinct scanning patterns—semi-circular and grid-shaped—to evaluate their efficiency and accuracy in GPR-based tree root assessment.

The methodology involved a data collection around a lime tree using both scanning patterns. The semi-circular scanning pattern, known for its detailed data acquisition, was contrasted with the grid-shaped pattern, which offers a potentially more time-efficient and practical alternative. The datasets were then subjected to thorough analysis, encompassing root detection, resolution, and overall efficacy.

This comparative analysis contributes to informing practitioners and researchers about the compromises between detailed root insights and the practical constraints of time and resources. The results of this study not only contribute to the optimisation of GPR-based tree root assessments but also aid in decision-making for urban planners and arborists seeking a balance between precision and efficiency in managing urban green spaces.

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., Zou, L., Bianchini Ciampoli, L., Alani, A. M., 2021. Advances in the use of the Short-Time Fourier Transform for assessing urban trees’ root systems. In Earth Resources and Environmental Remote Sensing/GIS Applications XII (Vol. 11863, pp. 212-219), SPIE.

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

How to cite: Lantini, L. and Tosti, F.: Efficiency and Accuracy in GPR-Based Tree Root Assessment: A Comparative Analysis of Scanning Patterns, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4142, https://doi.org/10.5194/egusphere-egu24-4142, 2024.

Airports form a core part of the built environment serving fast and safe transportation of human beings and freights, thereby fostering economic development at both national and international levels. Maintaining resiliency in the airport pavement infrastructures is a critical task for airport stakeholders. The first and foremost action to achieve this goal is to ensure construction sufficiency. Because of their significance, airfield pavements need to be designed and constructed as high-quality and low-risk structures. In the unlike event of construction malpractices, robust decisions are needed. Nondestructive testing methods and advanced monitoring systems offer unique opportunities for a non-invasive inspection of the built structures that help airport stakeholders to draw their strategy.

This study proposes a best practice approach in the form of a diagnostic framework developed and applied for the inspection of construction problems along the concrete runway pavement of a regional airport that exhibited cracking and deterioration in the early post-construction period. Traditional coring and laboratory testing on the concrete material did not yield any considerable findings. On the contrary, the use of the Falling Weight Deflectometer (FWD) helped (i) to comparatively assess the deflectometric response of individual concrete slabs, (ii) to assess the load transfer efficiency at cracks and joints, and (iii) to estimate durability levels of each slab’ s area. The collected data from this three-pillar framework enabled a full mapping of the concrete slab condition that helped to discriminate which slabs required minor maintenance treatments and/or complete reconstruction. As such, the inclusion of nondestructive assessment methods for site investigation contributed to an optimized and cost-effective action plan for the preservation of durable and sustainable airfield structures.

How to cite: Gkyrtis, K., Armeni, A., Plati, C., and Loizos, A.: An efficient approach for NDT diagnosis of defect causes in airfield concrete pavements , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8322, https://doi.org/10.5194/egusphere-egu24-8322, 2024.

Airports necessitate specialized approaches in design, construction, and management due to their unique characteristics. They typically comprise diverse structures, each requiring distinct handling techniques. Specifically, land-side components (e.g., terminals, technical facilities, real estate areas) are often treated as building structures, while air-side elements (e.g., runways, aprons) adhere to specific pavement engineering standards and procedures. Designers and maintenance operators encounter challenges at interfaces between these areas, navigating different methodologies simultaneously.

Complicating matters further, the airport environment is subject to numerous constraints and regulations for safety and security. Consequently, a substantial volume of remote sensing and non-destructive survey data is regularly collected at airports to support decision-making by facility managers concerning both airport expansion projects and the maintenance of existing assets.

In this context, the recent proliferation of digital modeling and digital twinning approaches in design and management processes for transport infrastructures is opening up new possibilities. Given the intricacy of airport operations, digital environments capable of consolidating land- and air-side data, monitoring survey reports, and addressing limitations in various asset areas are expected to achieve the following:

• Improve accuracy at land-side/air-side interfaces.
• Enhance efficiency in planning actions through integrated monitoring datasets management.
• Strengthen the effectiveness of constraints and limitations by simulating interference between worksite activities and obstacle-free volumes.

This study explores the potential and challenges associated with a digital model at a land-side/air-side interface area in an airport. Specifically, the focus is on the digitalization of a multi-source non-destructive testing (NDT) survey conducted over the apron 700 area Rome Fiumicino International Airport, where extensive ground penetrating radar (GPR), heavy weight deflectometer (HWD) and visual inspections have been conducted.

Acknowledgements

This research is supported by the Projects “PIASTRE” accepted and funded by the Lazio Region, Italy (PR FESR Lazio 2021-2027 – "Riposizionamento Competitivo RSI"). Authors express their gratitude to Aeroporti di Roma Ingegneria (ADR Ingegneria) S.p.a. and GRS s.r.l. for the assistance in survey data acquisition.

How to cite: Pinto, R., Bianchini Ciampoli, L., and Benedetto, A.: Digital management of airport apron pavement surveys, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18240, https://doi.org/10.5194/egusphere-egu24-18240, 2024.

Ensuring safety of civil infrastructure is a crucial goal in structural health monitoring (SHM). Amongst the variety of monitoring sensors, the Ground-Based Interferometric Radar (GBIR) systems have recently gained attention for their advantages such as the very high resolution and fast data collection, as opposed to other conventional methods [1]. However, this technology suffers from precise target location when the acquisition is carried out in dynamic conditions. For this purpose, external reflectors need to be installed in the portion of the structure under investigation, to which then the signal response is assumed to be related.

Considering this, the present research focuses on the investigation of the dynamic response of structures using GBIR aided with augmented reality (AR) [2]. AR assisted in controlling the position of the targets inside the radar’s beam of radiation and creating different acquisition scenarios in the same range based on a combination of their number and position. Dynamic excitations were applied in the field using light weight deflectometer (LWD) [3], and their effects on the collected signal were investigated using empirical mode decomposition (EMD) signal processing technique across the different scenarios. This allowed to have a better understanding of the signal response for multiple targets or at the boundaries of the signal footprint.

Results show that for data capturing using GBIR systems, AR can enhance the data quality by better controlling the collection phase. In addition, the use of multi-dimensional signal processing techniques, such as the EMD, facilitated a more comprehensive understanding of the signal response in complex scenarios.

Keywords: Structural health monitoring (SHM), Ground-based interferometric radar (GBIR), Augmented reality (AR), dynamic excitation, Empirical mode decomposition (EMD).

Acknowledgements

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

References

[1] M. Pieraccini, M. Fratini, F. Parrini, C. Atzeni, and G. Bartoli, “Interferometric radar vs. accelerometer for dynamic monitoring of large structures: An experimental comparison,” NDT and E International, vol. 41, no. 4, pp. 258–264, Jun. 2008, doi: 10.1016/j.ndteint.2007.11.002.

[2] S. Sotoudeh, F. Benedetto, S. Uzor, L. Lantini, K. Munisami, and F. Tosti, “A study into the integration of AR-based data collection and multi-dimensional signal processing methods for GB-SAR target detection,” in Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), M. Bilal and F. Tosti, Eds., SPIE, Aug. 2023, p. 49. doi: 10.1117/12.3007430.

[3] F. Tosti, S. Adabi, L. Pajewski, G. Schettini, and A. Benedetto, “Large-scale analysis of dielectric and mechanical properties of pavement using GPR and LFWD,” in Proceedings of the 15th International Conference on Ground Penetrating Radar, IEEE, Jun. 2014, pp. 868–873. doi: 10.1109/ICGPR.2014.6970551.

How to cite: Sotoudeh, S., Uzor, S., Munisami, K., Benedetto, F., and Tosti, F.: Investigating the Dynamic Behaviour of Civil Structures by Integration of Ground-Based Interferometric Radar and Augmented Reality , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10261, https://doi.org/10.5194/egusphere-egu24-10261, 2024.

In a context of European rail traffic growing rapidly, infrastructure managers are pushed to develop reliable solutions to improve safety and operational performance. This is a challenging task given 229853 km of network (28070 km in France only) [1]. For instance, in 2018, the total maintenance and renewable expenditure exceeds €20.6 billion in Europe (€5.4 billion for France) [2] and this is continuing to rise.
In the present study we focus on the bogie component, a complex and important element in rail-road cars. Overheated rail-road car wheels and bearings known as hot boxes, are a major threat for any railway operation. Extensive research  have been done, where remote and contactless condition monitoring technologies have been developed [3]. Among them, a class of system are based on thermal sensors, such as hot box detector (HBD) currently installed in the European railway. Such systems involve a high installation and maintenance cost. Furthermore, they are dependent to other facilities, like triggers to activate and deactivate the system. So, cost-less, robust and easy to maintain critical systems monitoring solutions have to be investigated.
With the advancements in both image sensor technology and processing capabilities, machine vision-based techniques may provide cost-effectiveness and easier solution for hot wheels and hot axle bearings detection.
In this research work, automatic detection,tracking and counting of hot boxes is addressed through the implementation of way side infrared thermal cameras. First a discussion on thermal cameras required performances will be proposed and new uncooled fast pixel sensors will be introduced.  Implementation on a real railway will be presented and discussed. Then, some image processing methods ([4], [5]) developed and studied in this work will be presented and applied to infrared thermal images (IRTIs) taken by different wayside camera models. Finally, the advantages of remote way-side thermal cameras with deep learning techniques will be discussed and perspectives will be proposed.

Acknowledgments
SNCF Reseau and BRIGHTER project. BRIGHTER has received funding from the KDT Joint Undertaking (JU) under grant agreement No 101096985. The JU receives support from the European Union’s Horizon Europe research and innovation program and France, Belgium, Portugal, Spain, Turkey.

References
[1] EU Commission. “COMMISSION STAFF WORKING DOCUMENT, REPORT FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT AND THE COUNCIL. Seventh monitoring report on the development of the rail market under Article 15(4) of Directive 2012/34/EU of the European Parliament and of the Council”. In: (2021).
[2] IRG Rail. Ninth Annual Market Monitoring Report. 2021.
[3] Amir Falamarzi, Sara Moridpour, and Majidreza Nazem. “A review on existing sensors and devices for inspecting railway infrastructure”. In: Journal Kejuruteraan 31.1 (2019), pp. 1–10.

[4] Thibaud Toullier, Jean Dumoulin, Vincent Bourgeois. “Comparative study of moving train hot boxes predetection and axles counting by in-situ implementation of two infrared cameras”. In: QIRT Asia 2019 Conference. 2019.
[5] Boualem Merainani, Thibaud Toullier, Jean Dumoulin. “Moving train wheel axles automated detection, counting, and tracking by combining AI with Kalman filter applied to thermal infrared image sequences”. In: SPIE Optical Metrology 2023. Proceedings. Munich, Germany: SPIE, June 2023. doi: 10.1117/12.2675719.

How to cite: Dumoulin, J., Merainani, B., and Toullier, T.: Study of hot box detection on moving targets using way side thermal infraredcamera and image processing methods : application to railway infrastuctures, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16481, https://doi.org/10.5194/egusphere-egu24-16481, 2024.

Crack detection and measurement are standard procedures during inspections of infrastructures. Traditionally, these activities are only visually performed. To accomplish this, infrastructures must be closed and inspections are carried out at night to minimise the impact of the infrastructure downtime. The limited time and the length of the system make it impossible to inspect the infrastructure in detail, which increases the risk that cracks are not detected. In the last decades, image-based techniques such as Digital Image Correlation (DIC) have been used to measure deformation and cracks in image time series. Unfortunately, the main limitation is the requirement of collecting images with a fixed camera (fixed between each inspection/frame), which represents a strong limitation for long-term monitoring. Recently, inspections have also been carried out with Mobile Mapping Systems (MMSs) that can capture the scene using a set of geomatic sensors. Specifically, images collected with MMSs are used for finding cracks and monitoring their extent over time. Unfortunately, due to the limitations of standard DIC techniques, crack propagation cannot be measured with DIC using images collected from different points of view since the MMS camera position between the inspections differs. 

In this work, we present a methodology (Crack Monitoring from Motion - CMfM) that integrates deep learning methods (Convolutional Neural Networks) with photogrammetric techniques for automatic detection and monitoring of cracks using a series of images collected with not fixed cameras [1]. Unlike conventional and image-based techniques, CMfM does not require fixed artificial targets and overcomes the DIC limitations of using fixed cameras, which opens up new possibilities for automatically monitoring crack propagation using images collected with MMS or standard cameras. The method can enable automatic monitoring of infrastructures, increasing the efficiency of the monitoring process and decreasing the risk that cracks are not found. The widespread adoption of CMfM can lead to significant improvements in the field of infrastructural monitoring and maintenance. Here, we present the results of crack detection and measurement during three-point bending tests on concrete beams. During the experiments, we used both fixed and not fixed cameras for collecting images and we processed the data with CMfM. We validated our methodology with comparisons with the standard DIC technique and local sensors such as Linear Variable Differential Transformers. We demonstrated that our algorithm can compute the crack width with an accuracy of a few hundredths of a millimetre compared to the adopted local sensor, demonstrating the possibility of measuring the crack evolution over time using non-fixed cameras. This work is part of the international TACK (Tunnel Automatic CracK Monitoring using Deep Learning) project [2].

[1] Belloni et al., Crack Monitoring from Motion (CMfM): Crack detection and measurement using cameras with non-fixed positions, Automation in Construction, Volume 156, 2023, 105072, ISSN 0926-5805, https://doi.org/10.1016/j.autcon.2023.105072.

[2] Belloni et al., TACK project: tunnel and bridge automatic crack monitoring using deep learning and photogrammetry, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 741–745, https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-741-2020, 2020.

How to cite: Belloni, V., Sjölander, A., Ravanelli, R., Crespi, M., and Nascetti, A.: Crack Monitoring from Motion (CMfM): crack detection and measurement using cameras with non-fixed positions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19948, https://doi.org/10.5194/egusphere-egu24-19948, 2024.

The deformation analysis of infrastructural objects like dams, bridges or tunnels is a dominant topic in engineering geodesy. Employing cost-efficient devices that react sensitively to deformations in the sub-millimeter range enables a more efficient geodetic monitoring.

To achieve this objective, the performance of a profile laser scanner, which was manufactured for use in the automation technology, was examined and analyzed for the use in geodetic monitoring.

The promising results of laboratory and large-scale field tests performed on concrete test specimens show the high sensitivity of the profile laser scanner in detecting deformations. Reference measurements with a high-precision distance measuring device (laser tracker) underline the detection of deformations in the sub-millimeter range by the tested profile laser scanner.

How to cite: Kostjak, V. and Neuner, H.: Investigating the sensitivity of a profile laser scanner, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18401, https://doi.org/10.5194/egusphere-egu24-18401, 2024.

With the majority of Italy's civil infrastructure, notably bridges, reaching the end of their designed lifespan, urgent attention for inspection and management is required. The alarming degradation and recent collapses underscore the critical need for robust protocols. This study addresses the challenges posed by aging bridges, emphasizing the imperative for proactive inspection and maintenance. Examining Italy's evolving approach post bridge collapses, the paper explores innovative methodologies, including laser scanning monitoring, to optimize assessment processes. The Atina Bridge in the MLazio Project serves as a pertinent case study, illustrating the potential impact of advanced technologies. The MLazio project aims to establish an innovative and efficient methodology, allowing the Lazio Region to develop a comprehensive plan for maintenance, management, evaluation, and monitoring of the safety of bridge structures within the regional territory.

This study focuses on the real-world case of the Atina Bridge, spanning the Mollarino stream and situated on the Regional rural road #509, chosen as a case study within the MLazio project. During the bridge inspection days, alongside routine assessments aided by ByBridge system, an innovative tool was employed: Polaris Long Range Laser Scanner, made available by the department. In less than half a day, a comprehensive scan of the Atina Bridge was executed. This enabled precise millimeter-level geometric measurements, crucial for subsequent post-processing and potentially capturing measurements of any defects not assessed on-site.
The utilization of this methodology allows for a detailed analysis of the point cloud data collected during the scan. Processing this data is facilitated by information such as the reflectance of the structural elements' materials, aiding in the identification of defects not necessarily evident during on-site inspections. The point cloud, offering both geometric and material information, becomes foundational for constructing the Atina Bridge's Building Information Modeling (BIM) model.

Infact, the construction of the Atina Bridge's BIM model, based on the acquired point cloud, represents a significant step toward optimizing inspection and maintenance processes, offering a comprehensive platform for data integration and analysis collected by the MLazio project during its inspection. This holistic approach, blending advanced scanning technology and BIM modeling, underscores the potential of innovative methodologies in streamlining the inspection and maintenance of bridges and viaducts throughout the Lazio Region. The seamless integration of advanced technologies, the creation of BIM models, and the detailed analysis of material reflectance collectively contribute to a heightened understanding of structural conditions. This comprehensive insight not only facilitates informed decision-making but also supports the formulation and execution of preventive maintenance strategies. The study conclusively underscores the significance of embracing cutting-edge methodologies, not only as a means to ensure immediate safety but as a proactive approach shaping the future management of bridges and viaducts.

Acknowledgements

This research is supported by the Projects “SIMICOM” accepted and funded by the Lazio Region, Italy (PR FESR Lazio 2021-2027 – "Riposizionamento Competitivo RSI"). In addition, this study was funded by Regione Lazio through the Project “M.Lazio”.

How to cite: Manalo, J. R. D., Napolitano, A., Bertolini, L., Gagliardi, V., and D'Amico, F.: Optimizing the Process of Bridge Inspection and Monitoring using Laser Scanner: Case Study of the Atina Bridge, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17938, https://doi.org/10.5194/egusphere-egu24-17938, 2024.

Continual monitoring of civil structures (e.g., bridges) is essential to maintenance and ensuring safety and integrity. 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]. Light Detection and Ranging (LiDAR) technology has also been used in infrastructure monitoring to capture structural 3D information and to detect displacements in surfaces with millimeter accuracy [2]. Some other technologies, such as the Ground-Based Interferometric Radar (GBIR), suffer from precise target detection when monitoring objects and require installation of dedicated reflectors. Scanning structures using these existing state-of-the-art technologies can be expensive and time-consuming. Recently, visualization technologies such as Augmented Reality (AR) have been utilized with GBIR to solve target location uncertainties by making the radar’s beam of radiation interact with the investigated structure [3].

This work proposes the use of head-mounted Augmented Reality (AR) to visualize and support the monitoring of bridge structures. First, to overcome limitations of the HoloLens depth sensing technology, we used smartphone-based LiDAR (Apple iPhone 14 Pro) to capture and export a 3D model of the shape of the structure of interest. We then imported this model into the HoloLens application so that it could be overlaid and adjusted to match the physical bridge structure. Second, a digital component model was aligned with the position and orientation of the antenna. The beam of radiation is estimated in the visualization application using the method described in our previous work [3]; then, it is displayed as a frustrum determined by an equation according to this method. Since this method does not rely on real-time LiDAR or depth mapping, we are able to visualize the projected beam of radiation beyond the usual range limitations of up to 7 meters. Furthermore, this method can be used effectively in outdoor locations, which can be challenging for infrared-based depth mapping technology.

The system can provide a relatively low-cost structural monitoring and assessment solution, which can allow researchers and surveyors to accurately visualize survey areas of interest and inform the decision-making process regarding maintenance of crucial civil structures.

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

[3] Sotoudeh, S. et al. "A study into the integration of AR-based data collection and multi-dimensional signal processing methods for GB-SAR target detection." Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023). Vol. 12797. SPIE, 2023.

How to cite: Uzor, S., Sotoudeh, S., and Tosti, F.: Low-cost support visualization of bridge structures using smartphone LiDAR and head mounted Augmented Reality (AR), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4063, https://doi.org/10.5194/egusphere-egu24-4063, 2024.

Achieving a comprehensive understanding of the health condition of the pavement asset is a crucial step for road network managers to establish an efficient maintenance and rehabilitation program. This is particularly true when the managed infrastructure traverses hydrogeologically complex areas that are reported to be highly vulnerable to both surface and deep hydraulic phenomena. Often, these areas are simultaneously affected by geotechnical events such as landslides and settlements. Monitoring the evolution of the effects related to these occurrences is indeed crucial to understand the actual risks to traffic, predict the remaining service life, and assess the overall resilience of the transport network to major natural events.

The integration of non-destructive testing (NDT) data, typically collected through high-productivity surveys, is now widely recognized as a method for gaining a deep understanding of pavement decay phenomena, with significant implications for the reliability of their evolution predictions.

This study presents the results of the integration of ground-penetrating radar (GPR) and mobile laser scanner (MLS) developed within the context of monitoring the A3 motorway, near the city of Salerno, Southern Italy. In particular, the focus is on a specific road stretch enclosed between two viaducts and affected by a remarkably complex hydrogeological scenario.

The integrated analysis revealed the possibility of identifying severe distress occurring at the subgrade level, successfully linked to underground water movements induced by the relationship between slope morphography and road embankment.

Acknowledgements

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 study was funded by Regione Lazio through the Project “PIASTRE” (PR FESR Lazio 2021-2027). We would like to thank C.U.G.RI. (Inter-University Research Center for the Prediction and Prevention of Major Hazards), Leica Geosystems for the collaboration in field survey operations and the Consorzio Stabile SIS S.c.p.a. for the logistical support and assistance.

How to cite: Bianchini Ciampoli, L., Di Benedetto, A., Fiani, M., and Benedetto, A.: Integration of NDT data for monitoring road pavement distresses in hydrogeologically complex areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18965, https://doi.org/10.5194/egusphere-egu24-18965, 2024.

Microplastic pollution is becoming a growing environmental concern globally. Over the past decades, the ubiquity and persistence of microplastics have raised crucial questions about their presence, distribution, and impacts in soils. Environmental factors such as soil chemical composition, moisture, temperature variations, and biological activities dynamically contribute to the fate and transport of these particles. Research on the presence of microplastics in soil is still in a developmental stage, and standardized methods for identification are lacking. In this study, we introduce a method for identifying microplastics, specifically in an area of the Raffaele Viviani public park in Naples (Italy), that has a history of various waste disposals in the last decades. The study provides detailed a description of sampling procedures, sample preparation, and analysis techniques. A mineralogical analysis was conducted to characterize the soils and understand if there were deposits of foreign material, through X-ray diffractometry. To isolate microplastics, a method utilizing a 1.5% sodium dodecyl sulphate surfactant for dispersion, and density separation with a saturated NaCl solution for the extraction of microplastics. Microplastics identification was achieved using a portable Raman spectrometer, and spectrum interpretation was conducted using the open-source program and database OpenSpecy. The use of a database was fundamental for identification, accomplished by comparing spectra from the database with the spectra measured in the samples. Results highlighted the presence of various plastic types, some showing signs of degradation under the microscope, indicating potential interactions with the surrounding environment. Mineralogical analyses confirmed the presence of mineral phases typical of the local geological formation, the ca. 15 ka volcaniclastic Neapolitan Yellow Tuff, although the finest fraction was compromised during sample preparation. The study implies that an initiative to establish a Food Forest in the Raffaele Viviani public park raises concerns about potential plant exposure to contaminants represented by microplastics and their potentially harmful heavy metals. In conclusion, this study is meant as a starting point, emphasizing the need for further research to fully comprehend the extent and implications of microplastic degradation processes. It proposes awareness and collective commitment as crucial keys for addressing environmental pollution and ensuring a sustainable future.

How to cite: Graziano, S. F., Di Vaia, M., Di Renzo, V., Grumetto, L., Neri, I., and D'Antonio, M.: Microplastic pollution in soil: a case-study from the Raffaele Viviani public park in Naples, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16224, https://doi.org/10.5194/egusphere-egu24-16224, 2024.

In recent years, innovative technologies for monitoring and managing civil infrastructure have been widely used by stakeholders and managing bodies, to ensure the correct and efficient maintenance of these critical elements. In this context, great attention is given to the environment surrounding these assets. Indeed, the environment plays a crucial role in altering the conditions of the civil infrastructures, either as a result of catastrophic natural events or due to the gradual morphological changes over time. Understanding the surrounding conditions of critical infrastructures is therefore essential to ensure accurate predictions regarding the changing conditions of the infrastructures over time, to prevent damages, and to intervene promptly and efficiently when necessary. In addition, comprehending the environmental conditions surrounding critical infrastructures is crucial for ensuring accurate long-term monitoring procedures, mitigating potential damages proactively, and facilitating efficient and timely interventions when necessary. Moreover, several international studies and research projects focused on these topics have been increasingly supported by industry and infrastructure managers in handling data from innovative monitoring technologies. Among these technologies, remote sensing techniques, including satellite analysis using MT-InSAR methods for assessing structural subsidence [1], and change detection techniques to evaluate temporal variations are gaining momentum. Furthermore, the use of Unmanned Aerial Vehicles (UAVs), to integrate information derived from other remote surveys, stands as a crucial topic to be more investigated.

This research aims to identify a methodology for managing multi-sensor and multi-scale survey information integrating satellite remote sensing and ground-based Non-Destructive Testing for Digital Twin-based infrastructure monitoring. However, the interpretation of data derived from satellite remote sensing [1] and ground-based Non-Destructive Testing (NDT) [2] techniques remain an area awaiting comprehensive exploration within the realm of transport infrastructure monitoring. This approach is aimed at the definition of a Digital Twin of the analyzed infrastructure and the environment in which it is located. An experimental application was developed selecting a bridge, located in Italy, identified as a case study. Several data obtained from inspections performed by UAVs and satellite remote sensing were implemented, as well as a digital modeling process, specifically developed for integrating such database to create a Digital Twin of the bridge and the environment. This application stands as a starting point for defining a broader integrated monitoring methodology for the management of critical transport infrastructures.

Acknowledgements

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

References

[1] Gagliardi V., et Al. Digital twin implementation by multisensors data for smart evaluation of transport infrastructure. SPIE Optical Metrology. Multimodal Sensing and Artificial Intelligence: Technologies and Applications III, Munich, 2023.

[2] Tosti F., et Al "Integration of Remote Sensing and Ground-Based Non-Destructive Methods in Transport Infrastructure Monitoring: Advances, Challenges and Perspectives," 2021 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS), Jakarta Pusat, Indonesia, 2021, pp. 1-7,

How to cite: Napolitano, A., Gagliardi, V., D'Amico, F., Calvi, A., and Benedetto, A.: Remote Sensing and Non-Destructive Testing for Digital Twin-based Infrastructure Monitoring, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19679, https://doi.org/10.5194/egusphere-egu24-19679, 2024.