Despite the technological maturity reached by NDT and EO, important research gaps on standalone technologies and their integration are still unexplored. One challenging issue is the development of monitoring systems based on the integration of sensing technologies with advanced modelling, ICT and position/navigation topics up to IOT and the new concept of citizen engineer. The goal is to provide stakeholders with handy and user-friendly information to support maintenance and controlling major risks.
This Session primarily aims at disseminating contributions from state-of-the-art NDT and EO methods, promoting stand-alone technology and their integration for the development of new investigation/monitoring methods, applications, theoretical and numerical algorithms, and prototypes for sustainable and resilient infrastructure and built environments.
The followings are areas of interest and priority for this Session:
- sensor types, systems and working modes (acoustic/electric/electromagnetic/nuclear/radiography/thermal/optical/vibration sensors; remote and ground-based, embedded sensing systems; stand-alone and integrated multi-source sensing modes);
- advanced processing methods and information analysis techniques (multi-dimensional signal processing; image processing; data processing and information analysis; inversion approaches, AI);
- multi-sensor, multi-temporal and multi-modal data fusion and integration (image fusion; spatio-temporal data fusion; AI and machine learning for data fusion and integration);
- ICT for spatial data infrastructure, distributed computing and decision support systems;
- citizens as “sensors” for defect detection and data collection;
- new NDT applications and EO missions for downstream implementations;
- NDT and EO for new standards, policies and best practices;
- case studies relevant to built environment diagnostics and monitoring.
Cultural heritage (CH) sites face escalating threats from environmental degradation, climate change, urbanization, and human activity. While traditional methods such as in-situ measurements and drone surveys using LiDAR and photogrammetry are valuable, they are often constrained by limited spatial coverage, revisit times, and operational challenges. To address these gaps, the integration of satellite remote sensing and artificial intelligence (AI) offers a transformative solution for scalable, continuous monitoring and automated change detection [1].
This study explores the combined use of multi-temporal satellite imagery—both optical and radar—and AI-driven algorithms to monitor structural changes and assess the environmental impacts on CH sites. By employing machine learning and deep learning models, the research enhances detection efficiency and accuracy, enabling non-invasive identification of structural deterioration, environmental stresses, and long-term degradation [2]. The approach emphasises using publicly available datasets and open-source tools to ensure accessibility and scalability.
In addition to technological advancements, the study adopts an ethical AI framework to address cultural and historical biases in CH monitoring. This framework seeks to minimise risks such as misrepresentation of marginalized communities and challenges posed by digitisation, including concerns about authenticity and the artificial reproduction of heritage assets. By integrating ethical considerations into the development and deployment of AI models, the research ensures that technological solutions align with sustainable and inclusive preservation practices.
The findings underscore the potential of combining advanced remote sensing technologies with AI to foster interdisciplinary collaboration, improve monitoring methodologies, and inform ethical policy frameworks. This integrated approach aims to safeguard cultural heritage sites for future generations.
Keywords: Cultural Heritage, Remote Sensing, Artificial Intelligence, Machine Learning, Monitoring
Acknowledgements
The Authors would like to express their sincere thanks and gratitude to the following trusts, charities, organisations and individuals for their generosity in supporting this project: Lord Faringdon Charitable Trust, The Schroder Foundation, Cazenove Charitable Trust, Ernest Cook Trust, Sir Henry Keswick, Ian Bond, P. F. Charitable Trust, Prospect Investment Management Limited, The Adrian Swire Charitable Trust, The John Swire 1989 Charitable Trust, The Sackler Trust, The Tanlaw Foundation, and The Wyfold Charitable Trust.
References
[1] Cuca, B., Zaina, F., & Tapete, D. (2023). Monitoring of Damages to Cultural Heritage across Europe Using Remote Sensing and Earth Observation: Assessment of Scientific and Grey Literature. Remote Sensing, 15(15), 3748.
[2] Argyrou, A., & Agapiou, A. (2022). A Review of Artificial Intelligence and Remote Sensing for Archaeological Research. Remote Sensing, 14(23), 6000.
How to cite: Tessema, T., Motavallizadeh Naeini, M., Azarmehr, N., Benedetto, F., and Tosti, F.: Integrating Satellite Remote Sensing and Ethical AI for Cultural Heritage Preservation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4477, https://doi.org/10.5194/egusphere-egu25-4477, 2025.
Corrosion of reinforced concrete structures represents one of the main causes of degradation for civil structures and infrastructure, making the development of innovative strategies for monitoring their health strongly recommended. In this context, non-invasive geophysical methodologies have been demonstrated to be effective, but the information provided is often qualitative and not fully usable for engineering purposes [1-2].
A laboratory test was conducted at the Hydrogeosite CNR-IMAA facility to explore the potential of resistivity methods and electromagnetic techniques, aiming to uncover new relationships between signal variations and degradation phenomena.
Using an integrated approach, including Ground Penetration Radar (GPR), ultrasonic tests, and electrical techniques within the framework of the Icarus Project (PRIN Project 2022), a set of reinforced concrete samples was designed and subjected to accelerated corrosion tests in a saline solution. These samples are continuously monitored to identify corrosion phenomena in the rebar and degradation of the concrete.
The final goal of the test is to experimentally link bond-slip performance through the combined use of non-destructive testing (NDT) methodologies, supported by mechanical pull-out tests. This study highlights the pressing need to develop innovative strategies for monitoring the health of reinforced concrete structures, given the significant risks posed by corrosion. The integration of geophysical and non-destructive testing (NDT) methodologies forms the core of this research, aiming to bridge the gap between qualitative data and actionable engineering insights.
The methodologies developed in this study offer practical applications for assessing corrosion levels in reinforced concrete structures. The integration of geophysical and conventional NDT data provides an efficient, non-invasive approach for routine monitoring, which is particularly valuable for monitoring engineering structures.
Research activities are realized also exploiting instrumentations and facilities provided by the Research Infrastructures of IRPAC (Infrastruttura Tecnologica e di Ricerca per lo studio del passato umano, la Conservazione e Gestione del Patrimonio Culturale) and ITINERIS , Italian Integrated Environmental Research Infrastructures System).
References
Fornasari, G.; Capozzoli, L.; Rizzo, E. Combined GPR and Self-Potential Techniques for Monitoring Steel Rebar Corrosion in Reinforced Concrete Structures: A Laboratory Study. Remote Sens. 2023, 15, 2206. https://doi.org/10.3390/rs15082206
Capozzoli, L.; Fornasari, G.; Giampaolo, V.; De Martino, G.; Rizzo, E. Multi-Sensors Geophysical Monitoring for Reinforced Concrete Engineering Structures: A Laboratory Test. Sensors 2021, 21, 5565
How to cite: Salvia, G., Di Gennaro, D., Capozzoli, L., Vasanelli, E., De Martino, G., Imperatore, S., and Nerilli, F.: NDT applied to monitor accelerated corrosion phenomena in engineering structures: a laboratory test with RC specimens, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3678, https://doi.org/10.5194/egusphere-egu25-3678, 2025.
Geophysical methods have long been utilized for non-destructive testing of concrete structures, focusing on key techniques such as Ground Penetrating Radar (GPR), Direct Current Resistivity (DCR), and Seismic Methods (ultrasonic seismic). These methods assess factors like the condition of reinforcement bars, internal discontinuities, structural strength, and corrosion in concrete. In particular, DCR data, often collected with the Wenner array for fixed electrode distance, directly evaluates concrete corrosion through apparent resistivity. While some laboratory-scale resistivity tomography studies exist, this study introduces a novel measurement setup designed for multi-electrode and multi-channel DCR instruments. The setup enables data collection using different electrode arrays on building columns' single, adjacent, and opposite surfaces. We analyzed and compared the 3D inversion results obtained from synthetic and experimental data using various measurement setups and electrode arrays. This presentation will highlight the comparative results and insights gained from these configurations.
Acknowledgment: This study was supported by the Scientific and Technological Research Council of Türkiye (TÜBİTAK) under project ID 121Y281. We extend our sincere gratitude to TÜBİTAK for their valuable support.
How to cite: Candansayar, M. E., Arıcan, C., and Gündoğdu, N. Y.: 3D Inversion of DCR Data for Structural Column Analysis in Buildings, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8221, https://doi.org/10.5194/egusphere-egu25-8221, 2025.
In the context of degradation prevention and maintenance of civil infrastructure, there is a strong demand for non-destructive testing aimed at monitoring the condition of reinforced concrete systems, particularly regarding the corrosion of reinforcement bars. 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 by electrochemical tests is a major issue for predicting the service life of reinforced concrete structures. The research group of the University of Ferrara is gaining experience on this topic combining structural engineering and electrochemical techniques with NDT geophysical methods. The new regulatory provisions require the monitoring of "strategic" reinforced and prestressed concrete structures from the construction phase, conducting periodic investigations into reinforcement corrosion and maintaining a record of the data. Many non-destructive electrochemical techniques are useful for this purpose, such as measuring the open circuit potential (OCP) and linear polarization resistance (LPR), surface potential (SP) measurement, and measuring the resistivity of concrete. However, these methodologies are not so effective in the case of a structure where the degradation state is being assessed for the first time, because the electrochemical techniques allow for determining whether the corrosion process has initiated and estimate the corrosion rate at that time, but they are not able to assess the extent of degradation. Recently, Ground Penetrating Radar (GPR) has been applied to monitor the evolution of the corrosion process affecting reinforcement bars, also using new methods for processing GPR data, highlighting a strong correlation between the corrosive phenomenon and the electromagnetic response of the acquired signals. This project is proposing to develop an integrated methodology that enables the creation of a predictive model capable of estimating the overall degradation state of reinforced concrete and providing a quantitative assessment of its structural stability.
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. For these aims, several reinforced concrete samples were produced using cement (Type II), suitable for structural applications, in which carbon steel rebars were embedded. The rebars were protected with an epoxy paint, leaving an exposed area of about 13 cm2. The samples were immersed in chloride-containing solutions and the rebars polarized for increasing periods of time. This aimed at inducing accelerated corrosion and achieving increased weight mass loss values of the exposed portion of the steel reinforcement. At the end of the polarization, the samples were opened for a degradation assessment and for actual mass loss evaluation. Finally the obtained weight loss values were correlated with the electromagnetic signals detected by GPR measurements.
How to cite: Rizzo, E., Zanotto, F., Balbo, A., Menghini, F., Fabbri, A., and Grassi, V.: Development of an integrated methodology for monitoring corrosion in reinforced concrete , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6565, https://doi.org/10.5194/egusphere-egu25-6565, 2025.
Ground Penetrating Radar (GPR) is a valuable tool in transportation infrastructure surveys that has evolved alongside the advancements in global technology. As a Non-Destructive Testing (NDT) technique, GPR is mainly utilized for pavement investigations and has been successfully used to assess the thickness in pavement engineering. However, despite its many years of use and improvements, there is still one major issue: how effectively can GPR detect thin asphalt layers? This challenge, commonly referred to as the "thin layer problem" according to the international literature, arises from the fact that it is difficult to detect reflections from thin layers. The main issue is the possible overlap of bottom and surface reflections, which makes accurate detection difficult.
The present research study addresses the accuracy requirements associated with using high frequency GPR antennas to identify and measure the thickness of thin asphalt layers. A key feature of this research is the proposed methodology, which provides a simple and effective approach to processing GPR data from thin asphalt layers to accurately detect their thickness. The methodology was validated using field data based on a highway section where rehabilitation works were carried out in conjunction with a newly constructed asphalt surface course. The estimated thickness of the thin layer showed an acceptable margin of error compared to the core sample measurements.
Overall, the results demonstrate the robustness and adaptability of GPR for quality assurance and quality control purposes, even in complex environments. In summary, GPR is a powerful tool that paves the way for more efficient pavement infrastructure management.
How to cite: Loizos, A., Plati, C., and Mouzakis, A.: Challenges in Detecting Thin Asphalt Layers Using GPR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10960, https://doi.org/10.5194/egusphere-egu25-10960, 2025.
The primary objective of this study is to develop efficient surveying and data processing methodologies that enable the extraction of more detailed metric data on road infrastructure than what can be obtained through traditional survey techniques.
Condition assessments conducted using traditional methodologies may be risky and, in some cases, ineffective. The Mobile Laser Scanner (MLS) technique, based on LiDAR technology, is widely adopted as a reliable alternative, as it allows for the generation of dense, accurate point clouds of both the road surface and associated artworks.
The aim of our work is to provide a comprehensive workflow for processing MLS data to generate useful indicators that describe the functional and structural characteristics of both the pavement and related structures, with the goal of optimizing decision-making processes for infrastructure managers.
Data processing for road surface analysis involves three main stages: (i) Extraction of points corresponding to the road pavement or the surfaces of associated structures; (ii) Generation of a curvilinear abscissa Digital Elevation Model (DEMc); and (iii) Analysis of surface regularity and the intrados of the artworks.
Point cloud filtering relies on the M-estimator SAmple Consensus (MSAC) algorithm, a robust variant of the RANSAC method. The DEMc is designed to follow the curvilinear alignment of the road axis. A curvilinear planimetric grid is first generated, with the curvilinear abscissa corresponding to the points marked by horizontal road signage. Elevation values are then assigned to each grid node, derived via local interpolation of points from the road surface. Surface condition assessment and cross-slope analysis are conducted by examining each cross-profile extracted from the DEMc. For each profile, regularity indices such as Rut Depth, as well as characteristic geometric parameters like transverse slopes, are calculated.
Regarding the analysis of structures, particularly tunnel intrados, our study proposes a methodology that utilizes an automatic unrolling algorithm for point clouds of the intrados, based on the RANSAC method. Intensity values of the LiDAR data are then analyzed to detect potential water infiltrations, while roughness values are calculated to assess surface integrity and identify cracks or steel bar ejections. The results, though focused on only two types of degradation, are useful for pinpointing tunnel sections in need of urgent intervention, thereby indicating areas of high priority for action or alert. The entire process is implemented in MATLAB.
The condition of the underlying layers of the road pavement were examined through Ground-Penetrating Radar (GPR) measurements to identify potential damage sources responsible for the deterioration of the surface layers. This also enables an assessment of whether the deformations affect only the superficial layers or extend to deeper strata. The outcome of the entire process is the creation of an Atlas in QGIS.
Data acquisition was carried out using a Leica Pegasus TRK500 Neo MLS, in collaboration with C.U.G.RI., Leica Geosystems for the survey, and SPN Salerno Pompei Napoli S.p.A. for logistical support. The survey was conducted over a 4 km stretch of the A3 highway (Campania Region, Italy), an area significantly affected by hydrogeological hazard.
How to cite: Di Benedetto, A., Benedetto, A., Bianchini Ciampoli, L., Fiani, M., Loncarevic, D., Morabito, A. L., and Petti, L.: Advanced monitoring of road pavement and infrastructure degradation using mobile laser scanning and ground penetrating radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21012, https://doi.org/10.5194/egusphere-egu25-21012, 2025.
As urban areas face increasing challenges from climate change, rapid urbanisation, and environmental degradation, enhancing urban resilience has become crucial for ensuring the sustainability of cities. Green infrastructure, particularly urban trees and green spaces, plays a central role in this effort, providing essential ecosystem services such as stormwater management, urban cooling, and carbon sequestration. However, the health and interaction of these natural systems with the built environment and infrastructure remain underexplored. Traditional methods for assessing the health of urban trees, particularly underground root systems, are often invasive and detrimental to the environment. This research explores the potential of non-destructive testing (NDT), specifically Ground Penetrating Radar (GPR), as a tool for assessing the health of urban trees and their underground root systems.
This project aims to develop an innovative, non-invasive methodology using GPR to assess the underground root systems of urban trees and their interaction with infrastructure. By providing urban planners with accurate, actionable data, the project seeks to identify risks to both urban trees and surrounding infrastructure, thereby enhancing urban resilience. This research supports cities in managing green infrastructure more sustainably, promoting the integration of natural systems into urban planning, and helping cities become more adaptable to the challenges posed by climate change.
The methodology involves using GPR technology to conduct surveys on urban tree root systems across various sites, mapping the underground root structures to identify potential risks to infrastructure, such as road damage or interference with utilities, and areas requiring preservation efforts. The GPR surveys are complemented by a review of tree species, urban settings, and environmental factors that impact root growth and health. A community-driven approach ensures that the data generated is applied in a way that directly benefits local communities, promoting collaborative solutions that integrate green infrastructure into urban planning. This approach aligns with the Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action), by promoting climate-resilient urban environments through sustainable infrastructure practices.
Preliminary results demonstrate the feasibility of using GPR as a non-invasive tool for enhancing resilience in urban planning. The research lays the foundation for developing a resilience framework to help cities integrate green infrastructure into climate adaptation strategies. This work will provide urban planners and policymakers with critical data for making informed decisions that strengthen the resilience of both urban ecosystems and infrastructure.
Acknowledgements:
This project is supported by the UK Department of Science, Innovation and Technology's International Science Partnerships Fund (ISPF) via the Royal Academy of Engineering under the Frontiers Seed funding scheme (FS-2425-22-157).
How to cite: Lantini, L., Aktas, Y. D., Sanderson, D., Husamaldin, L., and Saadati, P.: Ground Penetrating Radar and Community Engagement for Enhancing Resilience in Green Infrastructure , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18888, https://doi.org/10.5194/egusphere-egu25-18888, 2025.
This Abstract presents the design and analysis of an advanced Edge slotted waveguide antenna array tailored for high-gain applications within the S-band frequency range (2 GHz - 4 GHz). Initially developed for scientific and military purposes, slotted waveguide antennas (SWAs) have become a key component in various radar systems due to their exceptional features, including high directivity, low side lobe levels (SLL), low losses, excellent phase stability, and better power handling capabilities. The antenna array introduced in this work integrates as many as 40 number of radiating elements to achieve high gain and a sharply focused pencil-beam radiation pattern, making it ideal for radar and communication systems.
The radiating slots are precisely machined into the narrow wall of a WR-284 waveguide, ensuring both optimal electromagnetic performance and structural robustness. Comprehensive electromagnetic simulations confirm the antenna’s efficient operation within the S band, meeting the rigorous requirements of modern technologies. The design achieves a remarkable realized gain of 42.48 dBi, with beamwidths of 1.3° and 4.6° in the vertical and horizontal planes, respectively, and a VSWR of 1.24:1. The array , demonstrating the exceptional capabilities of slotted waveguide antennas in delivering high power, interference-resistant performance. These findings emphasize the critical role of SWAs in advancing radar and communication technologies.
How to cite: Pathak, S., Priya, T., and Bhattacharya, A.: Design of an Slotted Waveguide Antenna Array for Ground Controlled Interception (GCI) Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6807, https://doi.org/10.5194/egusphere-egu25-6807, 2025.
Void defects significantly undermine the safety and operational performance of ballastless tracks in high-speed railways [1]. Ground Penetrating Radar (GPR), serving as a non-destructive testing tool, is widely used for detecting internal defects in ballastless tracks, owing to its fast detection speed and high resolution. However, the complex rebar distribution in track slabs causes severe interference in GPR data during detection, reducing the detectability of void signals, while the sensitivity of GPR data to void defects detection varies across different polarization modes, further complicating accurate identification [2].
To address these challenges, this paper proposes a void defects detection method by rebar clutter suppression and polarization fusion imaging. First, a deep learning model is developed to suppress rebar clutter in GPR data, improving void signal visibility. Then, Reverse Time Migration (RTM) is applied to fuse data from HH and VV polarization modes, further enhancing imaging resolution and accuracy [3].
The proposed method is validated through forward modeling and field experiments. Results demonstrate its effectiveness in suppressing rebar clutter and improving void detection and imaging. This paper provides an approach for structural health monitoring of ballastless tracks, offers insights into advancing GPR applications in complex rebar environments, and introduces a new perspective for using GPR in the detection of ballastless tracks.
References:
[1] Yang, Y., & Zhao, W. (2019). Curvelet transform‐based identification of void diseases in ballastless track by ground‐penetrating radar. Structural Control and Health Monitoring, 26(4), e2322.
[2] Wang, X., Liu, H., Meng, X., Cui, J., & Du, Y. (2024). Enhanced imaging of concealed defects behind concrete linings using Residual Channel attention network for rebar clutter suppression. Automation in Construction, 166, 105574.
[3] Liu, H., Yue, Y., Lian, Y., Meng, X., Du, Y., & Cui, J. (2024). Reverse-time migration of GPR data for imaging cavities behind a reinforced shield tunnel. Tunnelling and Underground Space Technology, 146, 105649.
How to cite: Peng, H. and Liu, H.: Rebar Clutter Suppression and Fusion Imaging for Enhanced Detection of Void Defects in Ballastless Tracks Using Ground Penetrating Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20939, https://doi.org/10.5194/egusphere-egu25-20939, 2025.
Management and maintenance of transportation infrastructures are among the top priorities for public administrations and stakeholders around the world. Throughout the infrastructure lifecycle, data about asset conditions for all parties involved must be gathered and managed to develop strategies to reduce significant failures. Nowadays, there can be a lot of variation in the ways that different asset owners and contractors collect and handle data during a road's lifecycle. Due to the lack of a structured information system and information fragmentation, pavement management is vulnerable to significant rework, information loss, assessment errors, and misinterpretation of the collected data. Furthermore, a lot of issues with road pavements can arise in their deep layers, making it challenging to identify and examine them using conventional techniques. In this context, NDT methods, such as LiDAR and GPR, have been used alongside visual and automated testing to determine the root causes of pavement failures.
Building Information Modeling (BIM) can be a useful tool in this sense, providing an environment in which to store, manage and update data related to various infrastructure assets. In this context, the main goal of BIM integration in management procedures is to incorporate lifecycle data into digital three-dimensional models of the assets of civil infrastructures. Nonetheless, the road industry still lacks standardized processes for creating, integrating, representing, and maintaining data in BIM. This poses a problem for the industry, as there are currently no effective ways for the various disciplines and players involved in a road project to share data throughout its lifecycle.
The proposed methodology combines data provided by multiple NDT sources to generate a BIM model of a road pavement, that accurately depicts its configuration even regarding its deep layers. The digital representation of such an asset can be useful in carrying out analysis of its condition in a digital and three-dimensional environment. Moreover, pavement distresses found underneath the surface can be detected and integrated into the model, providing a more thorough and detailed representation of the pavement conditions. Using BIM procedures, such as clash detections methods, an automatic analysis of which pavement layers are affected by multiple kinds of distresses can be performed. Therefore, a database of pavement distresses, the corresponding layers and their location along the infrastructure can be obtained.
The methodology was tested on real data obtained during on-site surveys carried out on an Italian highway. The results show promising insight regarding the possible advancements in management and maintenance procedures of transportation infrastructures, as implementing BIM as a tool to store and manage information regarding pavement conditions can prove to be a great support to administrations and stakeholders in Italy and worldwide. Moreover, the use of the proposed process along the integrated analyses performed by IoT sensors, such in the case of bridges, can provide a more thorough insight regarding the entire infrastructure conditions, by comparing the ones related to its different assets.
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")
How to cite: Bertolini, L., D'Amico, F., and Bianchini Ciampoli, L.: Road pavement conditions evaluation through NDT and BIM integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18307, https://doi.org/10.5194/egusphere-egu25-18307, 2025.
Point cloud data acquired through LiDAR technology enables the rapid reconstruction of complex indoor building structures. However, due to the discrete nature of point clouds, they fail to accurately represent the geometric dimensions of building components and cannot be directly applied to digital model construction. Conventionally, this limitation necessitates manual modeling in specialized BIM software to integrate both geometric and semantic information of building structures, which is labor-intensive and time-consuming. To address this, we propose an automated method for geometric feature extraction and BIM reconstruction, enabling more efficient and accurate modeling processes. By segmenting building components and extracting their geometric features, the method automates the construction of building structural entities based on the IFC (Industry Foundation Class) standard, which is an open and vendor-neutral modeling standard widely used in the BIM domain.
Specifically, the approach starts by filtering ceilings and floors using histograms of height values, as they geometrically represent planar structures and can be represented by footprints, which commonly are constructed from closed contours formed by projecting wall segments onto horizontal planes. To ensure accurate footprint representation, non-wall objects, such as furniture, are first excluded from the scene. For this purpose, a series of viewpoints are used to simulate camera positions and generate image sequences. Coupled with these image sequences, a pretrained large-scale language-image model, YOLO-World, is applied to identify the bounding boxes of the furniture, while the SAM2 model is used to segment individual entities. The segmented pixels are then back-projected and aggregated in 3D space to isolate and exclude non-building objects. Once the walls are identified, the point clouds are processed using a region-growing and merging algorithm to extract multiple facades, which are projected onto horizontal planes to generate line segments. Based on these line segments and the scene’s bounding box, the horizontal plane is divided into cell partitions, and an energy optimization-based graph-cut algorithm is applied to identify the optimal cell set, with the resulting closed contours representing the footprints. These footprints are then extruded along the height direction into 3D geometries to generate Ifcwall, Ifcceiling, and Ifcfloor objects through the Ifcopenshell library, producing a complete and standardized IFC model.
This method was validated in complex indoor environments with furniture such as tables, chairs, and sofas, demonstrating high precision in reconstructing fundamental building components like walls, floors, and ceilings. By providing an automated and efficient solution for simple indoor structure reconstruction, the approach lays the groundwork for modeling more intricate scenarios and facilitates the development of intelligent, sustainable digital twin models to support comprehensive lifecycle building management.
How to cite: Zhai, R., Gan, X., He, Y., and Li, J.: Automated BIM Reconstruction from Point Clouds Using IFC Standards in Indoor Environment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2727, https://doi.org/10.5194/egusphere-egu25-2727, 2025.
The structural integrity of transportation infrastructure is critical in ensuring public safety, economic stability and societal advancement. The demand for versatile, scalable and real-time monitoring solutions becomes exponential as these assets age, get used more and face environmental pressures. Conventional inspection methods, such as visual inspections and static evaluations, while valuable in localized applications, have significant limitations, including dependence on the expertise of specialized operators, time consumption, and an inability to provide dynamic insights across extensive networks [1]. In this regard, Digital Twin (DT) technology has emerged to provide a virtual replica of physical assets in real-time with data from multiple sources [2]. Supplementing DTs, remote sensing techniques including Multi-Temporal InSAR (MT-InSAR) and high-resolution satellite images can easily identify structural displacements in the millimeter scale region over extensive region.
Satellite constellations, provide periodical updates with high spatial and temporal resolution, allowing a near real time monitoring of infrastructure without the need for ground-based instrumentation. These advancements are further enhanced by Building Information Modeling (BIM), which supports the creation of dynamic digital models encompassing all data relevant to the management, maintenance, and optimization of transportation infrastructure. This research presents a comprehensive approach to integrating Digital Twin technology with satellite remote sensing, BIM, and non-destructive testing methodologies. The study highlights the potential of combining near-real-time satellite data, field inspections, and advanced visualization techniques to develop a scalable, network-level monitoring system for critical assets such as bridges and viaducts. These results reiterate the value of high-resolution satellite missions along with next-generation technologies for enabled predictive maintenance and structural integrity management, supporting the sustainable and resilient transportation infrastructure development.
Acknowledgements
This research is supported by the Project “PIASTRE” accepted and funded by the Lazio Region, Italy
References
[1] Napolitano A., et al., Integration of Satellite Monitoring data in a Digital Twin of Transport Infrastructure. Proceedings Volume 13197, Earth Resources and Environmental Remote Sensing/GIS Applications XV; 131970Y (2024) https://doi.org/10.1117/12.3034395
[2] 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.
How to cite: Napolitano, A., Gagliardi, V., Calvi, A., Manalo, J. R. D., and Benedetto, A.: Digital Twin for Advanced and Continuous Monitoring of Infrastructure Assets Using Remote Sensing and Non-Destructive Testing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20016, https://doi.org/10.5194/egusphere-egu25-20016, 2025.
Deformation monitoring in airport runways is a key task in airport management and operations requiring prompt intervention and actions to maintain surface regularity. The technological advancement in this area has been dramatic. Terrestrial and aerial remote sensing can provide accurate and dense deformation data [1]. In the past decade, the use of satellite observations for airport monitoring has grown due to the advantage of accurate results with high temporal and spatial resolution [2]. However, the satellite remote sensing data are still deployed conventionally to users through bi-dimensional maps and charts, therefore limiting the level of interaction of end-users and impacting the decision-making process. Immersive technologies can fill this gap [3] by enhancing the visualization and communication of information from satellites in spatial environments [4], enabling a better understanding of surface deformations in runways.
This research explores the design of a Virtual Reality (VR) tool for visualizing multi-temporal deformations at Runway 3 of the ‘Leonardo Da Vinci Airport’ in Fiumicino, Rome, Italy. The protocol encompasses the use of high-resolution data acquired during November 2016 to December 2019 and processed using the Permanent Scatterer Interferometry (PSI) technique. The tool is developed in Unity 3D with the following key design goals:
- Cross-validation with two or more VR headsets allowing for multi-user collaborative analysis.
- 3-dimensional interactive visualizations that allow for scalability in visualizing data at the millimeter level as well as isolating sections of interest to stakeholders.
- Analysis of historical structural data using machine learning to predict future deformations and highlight potential risks.
Using satellite remote sensing, we combined sub-millimeter information on the displacements of the pavement runway with the total station to provide a holistic digital model of the physical site. The system can provide an efficient infrastructure modelling and assessment solution, which will allow researchers and engineering professionals to a) create digital 3D snapshots of a physical site for later assessment, b) track positional data on existing displacements, and c) inform the decision-making process regarding locations for early and potential future interventions.
References
[1] Bianchini Ciampoli et al. Displacement Monitoring in Airport Runways by Persistent Scatterers SAR Interferometry. Remote Sensing. 2020; 12(21):3564.
[2] Gagliardi, et al. Testing Sentinel-1 SAR Interferometry Data for Airport Runway Monitoring: A Geostatistical Analysis. Sensors. 2021; 21(17):5769.
[3] Wang, Peng, et al. “A Critical Review of the Use of Virtual Reality in Construction Engineering Education and Training.” International Journal of Environmental Research and Public Health, vol. 15, no. 6, June 2018, p. 1204.
[4] Luleci, Furkan, et al. “Structural Health Monitoring of a Foot Bridge in Virtual Reality Environment.” Procedia Structural Integrity, vol. 37, 2022, pp. 65–72.
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.
How to cite: Uzor, S., Atsakpo, E., Tessema, T., Bianchini Ciampoli, L., Gagliardi, V., Benedetto, A., and Tosti, F.: Enhanced Deformation Monitoring in Virtual Reality using Remote Sensing Data in Airport Runway Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20180, https://doi.org/10.5194/egusphere-egu25-20180, 2025.
Due to the recent awareness of climate change phenomena and ever-growing aircraft’s loads, superstructural health monitoring plays a pivotal role in effectively assessing an infrastructure’s degree of resilience. In this context, the contribution of non-destructive testing techniques - able to monitor structural conditions and survey extensive paved areas - needs to be reconsidered in favour of a potential synergy with real-time and pre-embedded monitoring sensors. By allowing seemingly continuous acquisition and immediate monitoring of strain and thermic pavement properties, fiber optic sensors’ applications could be deployed to measure rigid pavement behaviour and predict correspondent residual life. The present research focuses on the validation of such a technology through an ad hoc experimental setup, in order to assess the feasibility of an asset-wise, real-time data-driven concrete pavement management system. In conclusion, the encouraging outcomes of the experimental activities allow to consider the network of embedded structural health monitoring systems as an effective opportunity for managing extensive infrastructures, planning rehabilitation and maintenance strategies, ultimately preventing abrupt damage and unserviceability issues.
How to cite: Pinto, R., Bianchini Ciampoli, L., and Benedetto, A.: Advances, challenges and perspectives in fiber optic sensing applications to airport concrete pavement structural health monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19401, https://doi.org/10.5194/egusphere-egu25-19401, 2025.
Sustainable Development Goal 3 of the 2030 Agenda for Sustainable Development is to “ensure healthy lives and promoting well-being for all at all ages”. Research consistently demonstrates that access to nature has profound benefits for physical health and mental well-being [1]. Green Infrastructure (GI), ranging from street trees, green roofs, parks, wildlife areas, woodlands, and wetlands, plays a vital role in supporting nature’s recovery, fostering biodiversity, and the ecosystem. Additionally, GI offers natural solutions to challenges like flood risk, poor air quality, and the urban heat effects exacerbated by climate change whilst creating inclusive spaces for people to experience physical and mental health benefits and delivering quality of life and environmental benefits for communities.
Recent studies show that urban green spaces alone support 2.1 million people to meet their weekly physical activities, estimated to be worth £5.6 billion, and reduce mental health service costs of approximately £141 million [2]. Acknowledging the critical role of GI for sustainable and resilient cities [3] and the challenges associated with an informed investment we propose the use of ‘reward and benefit’ analysis as an economic tool and utilization of medium-high-resolution remote sensing data and field information for mapping and monitoring of green infrastructures. Further, we propose to explore the correlation/link between the health impact of greener spaces on communities and the financial feasibility and viability of green spaces and projects.
Combining remote sensing and reward-benefit analysis creates a powerful framework for green infrastructure management and resilient urban development. Integration of spatial information and economic evaluations could support policy and decision-makers in evidence-based policy-plan development on prioritizing investments, resource allocation, enhanced stakeholder management, and healthier-happier neighbourhoods to achieve long-term sustainability goals.
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] Gunwoo Kim, Patrick A. Miller, The impact of green infrastructure on human health and well-being: The example of the Huckleberry Trail and the Heritage Community Park and Natural Area in Blacksburg, Virginia, Sustainable Cities and Society, Volume 48, 2019,101562, ISSN 2210-6707,(https://doi.org/10.1016/j.scs.2019.101562)
[2] Saraev, V., O’Brien, L., Valatin, G. and Bursnell, M. (2021), Valuing the mental health benefits of woodlands. Research Report. Forest Research, Edinburgh i-iv + 1-32pp
[3] Environmental Improvement Plan 2023 (https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/)
How to cite: Kumari, S., Tessema, T., Husamaldin, L., Saadati, P., Mortimer, D., and Tosti, F.: From Data to Decisions: Integrating Remote Sensing and a Reward and Benefit Analysis for Green Infrastructure Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14087, https://doi.org/10.5194/egusphere-egu25-14087, 2025.
After 20 years, with 12,854 ± 0.056 ka BP (Wolbach,2018) YDIH, Younger Dryas Impact Hypothesis (Firestone,2007,2009; Moore,2024) confirmed original date of 12.850 cal yrs BP for Continental-Ice, CI-KISS (Bujatti-Narbeshuber,1997b).
270 yrs earlier, Mayan Codex Troano date of Atlantic-Ocean, AO-KISS (13.124 BP), corroborated by Laacher See Eruption (LSE) as impact-volcanism proxy date (Bujatti-Narbeshuber,1997), now 13.160 (Friedrich,2004;Kromer,2004), 13.034 yrs BP (Van Raden,2019), is expanded by Greenland bipolar sulfate (Lin,2022) as Impact-Volcanism-Quadruplet (IVQ). LSE marks start of 5 step-geomagnetism (Dichiara,2023) and (Gravity-Greenhouse Threshold Transition) GTT2-warming “staircase” where LSE-IVQ cooling is overwhelmed by H20-gravity-warming (Nikolov,2024,Jucker,2024). GTT2 characterizes Inter-Aleroed-Cold-Period 2 (IACP2) reaching its maximum with Holocene-End-Aleroed-Temperature (HEAT) Melt-Water-Pulse (MWP) 0B with < 6m step in sea level (Bard,2010), the real Younger-Dryas- Onset (YDO) trigger, years later reinforced by CI-KISS (12,854 ka BP), supporting biphasic YD-cooling (Max,2022), then warming MWP1B.
IACP evolves through Atlantic-Ocean (AO-) KISS with Mid-Atlantic Ridge & Plateau Lowering Events (MARPLEs), meso-stratospheric water and gas plume of 25,03 x 1015 tons (Muck,1976), first into IAPC1-Albedo cooling phase (Repschläger,2023) with bleached magmatic, silico-clastic (Davias and Gilbride,2012) “White Mats” (Bujatti-Narbeshuber,2023) and geomagnetic (Mörner,1977; Chen,2020) Gothenburg-Excursion-Onset (GEO), later LSE, marks, despite 5 LSE-IVQ volcanic cooling episodes, the restart into IAPC2 with the rewarming Gravity-Greenhouse-Threshold GTT-staircase.
Finally the full, both Continental-Ice and Atlantic-Ocean (CIAO-) KISS scenario (Bujatti-Narbeshuber and Hoogewerff,1995), is necessary to explain and predict Pleistocene/Holocene-Catastrophic-Climate-Change (PHCCC).
Research on PHCCC starts from the hypothesis that a Koefels-comet Taurid-fragment impacted into the Ötztal glacier ice near Hohe Geige mountain (3393 m, Tirol, Austria) with Pleistocene/Holocene KISS serving as long sought, necessary trigger factor for the pre-failure weakening of the Koefels crystalline rock, “since there is no other evidence for a pre-existing zone of weakness promoting slope failure” (Zangerl,2021).
Holocene retreat of stabilizing ice, much later, around 9527-9498 cal BP (Nicolussi,2015), lead to the extremely rapid rock slide of Koefels with 3,28 km3 (Brückl,2001).
Koefels-crater is the largest crater in the metamorphic, crystalline area of the Alps. Even seismic fatigue cannot solely explain (Oswald,2021) without the KISS-impact “the particular situation of the Koefels rockslide because it is still unclear why this giant event occurred at this location and within a very strong rock mass” (Zangerl,2021).
Hohe Geige and Koefels crater are furthermore positioned above a unique circular Geomagnetic Anomaly (Ahl, Slapansky,2003) evident in the aeromagnetic map of Austria (Seiberl,1991), suggested as impact magnetism based on growing evidence for a global CIAO-KISS event (Bujatti-Narbeshuber,1997a).
Hohe Geige and Koefels extend northward into a sector, widening from 12 to 17 km, 50 km long, that contains the largest concentration of rock slides (12) in the Alps (Tollmann,1992; Abele,1974). This is explained by both seismicity or an ejecta curtain from an oblique KISS-impact and already led to solving the Carolina Bays enigma as late Pleistocene, 12.850 cal yrs BP, “paleoseismic, impact-seismic, liquefaction features” (Bujatti-Narbeshuber, 1997b).
Koefels-comet Taurid-fragments with an oblique Ice-impact into the Ötztal glacier low-impedance ice-layer “guiding the pressure wave horizontally thus absorbing up to 70% of the impact energy would significantly reduce both the peak pressures at depth along with their expressions in the rock record” (Stickle,Schultz,2012,2013; French, Koeberl,2010).
How to cite: Bujatti-Narbeshuber, M.: Pleistocene/Holocene (P/H) boundary oceanic Koefels-comet Impact Series Scenario (KISS) of 12.850 yrs BP Global-warming Threshold Triad (GTT): Part V, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20574, https://doi.org/10.5194/egusphere-egu25-20574, 2025.
Mining plays a critical role in economic development, yet it presents significant safety risks and environmental challenges. Balancing worker safety and environmental preservation while maintaining sustainable practices is a pressing concern for the modern mining industry. This study explores the use of integrated geophysical techniques to enhance mine safety assessments and support sustainable mining operations. By combining methods such as Electrical Resistivity Tomography (ERT), seismic surveys, Ground-Penetrating Radar (GPR), magnetic surveys, and gravity surveys, the approach provides a comprehensive and non-invasive understanding of subsurface conditions in active mining areas. The integrated geophysical framework enables real-time monitoring of underground stability, facilitating the detection of hazards such as ground subsidence, roof falls, sinkholes, potholes, and unstable fault zones. These techniques deliver high-resolution data, revealing surface and deep structural instabilities, which allow for proactive risk management and inform strategic decision-making processes. The research demonstrates how continuous geophysical monitoring reduces accident risks, enhances operational efficiency, and ensures long-term sustainability in mining practices. This study further highlights the broader benefits of adopting integrated geophysical methods, emphasizing their role in minimizing environmental impact while improving workplace safety. By integrating advanced geophysical techniques into mining operations, this research establishes a model for ensuring both worker safety and environmental preservation, aligning with global sustainable development goals. The proposed approach bridges the gap between mining safety and sustainability, showcasing the potential of technological innovation to mitigate the inherent risks of mining activities.
How to cite: Kumar, R., Bera, A., Pal, S. K., and Bhattacharjee, R. M.: Advancing Mine Safety through Integrated Geophysical Techniques: A Pathway to Sustainable and Risk-Free Mining Practices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-828, https://doi.org/10.5194/egusphere-egu25-828, 2025.
As part of structural and infrastructure health monitoring, we propose a rapid and non-invasive experimental geophysical survey to determine the key structural parameters of an infrastructure. This approach involves the simultaneous acquisition of 20 minutes of seismic ambient noise using seismic arrays strategically placed at various points of the structure. These acquisitions do not require diverting or blocking traffic flow, nor does it disrupt the operation of road infrastructure.
The acquired signals are analyzed using various techniques, including standard spectral analysis (Fourier Amplitude Spectra, FAS), Frequency Domain Decomposition (FDD), Ambient Noise Deconvolution Seismic Interferometry (ANDI), and NonPaDAn analysis. This approach, which involves the joint application of these methods, was developed and validated on two types of viaducts: the Gravina Bridge, located on the SS.655-Bradanica, and the Monticello Viaduct on the SS.407-Basentana (Albano di Lucania), both in the Basilicata region of Italy.
The Gravina Bridge is a newly constructed arch bridge, and the structural parameters experimentally estimated are a reference point for future investigations into the evolution of these parameters over time. In contrast, the Monticello Bridge, a multi-span viaduct built in the 1970s, allowed for a comparison between the estimated structural parameters and the varying degrees of degradation observed in its girders.
By employing multiple analysis methods, the interpretation of results has been enhanced, demonstrating the potential of this approach to estimate the main vibrational modes, the relative modal shapes, equivalent damping, and seismic noise wave propagation velocities and the evolution of these parameters over time.
How to cite: Serlenga, V., Gallipoli, M. R., Tragni, N., and Petrovic, B.: Structural Characterization of Infrastructures through multi-Methodological Geophysical Approach , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17848, https://doi.org/10.5194/egusphere-egu25-17848, 2025.
Accurate assessment of asphalt layer compaction is crucial during the construction process. Ground-penetrating radar (GPR) technology allows continuous measurement of the dielectric constant of the surface layer, enabling comprehensive mapping of compaction levels. However, its application can be challenged by factors such as surface moisture resulting from roller nozzle sprays and the necessity of calibration coefficients in the dielectric constant-density relationship model. In this study, GPR technology was employed to develop an evaluation method for assessing the compaction quality of asphalt surface layers. Compaction metrics were analyzed by continuously collecting dielectric constant data from the asphalt surface layer, with the compaction interval and coefficient of variation used as indicators of compaction level and uniformity, respectively. The proposed method accounts for the impact of water vapor between the antenna and the ground on GPR accuracy and incorporates a data stability correction technique to remove outliers. The dielectric constant-density prediction model was calibrated through laboratory and field core testing. Field trial results demonstrate that GPR technology is effective in evaluating the compaction quality of asphalt layers. Unmanned compaction machinery was found to achieve better compaction uniformity, with a lower coefficient of variation compared to traditional methods. Variations in compaction were observed at the edges of construction sections and between lanes, showing the importance of improved construction monitoring to enhance overall compaction quality.
How to cite: Wang, S., Zhang, Y., Ma, T., Huang, X., and Jin, G.: Comprehensive Assessment of Asphalt Pavement Layer Compaction Degrees Using GPR-Based Density Profiling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14021, https://doi.org/10.5194/egusphere-egu25-14021, 2025.
Posters on site: Wed, 30 Apr, 14:00–15:45 | Hall X4
In geological fieldwork and concrete inspection, the sound of hammer blows is used to determine the quality of the material. In the case of rock, the sound of hammer blows is greatly influenced by the density of rock fractures and the heterogeneity of the rock composition. Therefore, even experienced geotechnical engineers may judge the goodness or badness of rock differently from person to person. Furthermore, the social issue of the lack of human resources of geotechnical engineers requires the application of new technologies, such as sensing technology and deep learning, to solve the problem.
In the field of civil engineering, deep learning technology is used to determine the state of deterioration from the sound of concrete being struck. However, rocks are more heterogeneous than concrete, and their applicability needs to be verified. In addition, there are no examples of matching technical decisions made by geotechnical engineers with deep learning models or verifying their accuracy. Therefore, in this study, a deep learning model was constructed based on spectral analysis of impact sound frequencies and learning audio information in order to quantitatively determine the material quality of rocks by impact sound. The validation target was rocks at a dam construction site in Japan.
The deep learning model employed a CNN, which has been reported to be used in a number of general audio classification problems, such as environmental sounds. Specifically, we constructed (1) YAMNET, which was transfer-trained on the impact sound of rock materials, and (2) 2D-CNN, which was trained by converting the impact sound of rock materials into log-mel spectrogram images, and conducted comparative verification. In order to construct the model, stratified 5-folds cross-validation was performed using data excluding test data, and optimal hyperparameters were searched.Also, the percentage of test data is 20% for all data.
As a result, we were able to construct a model with an F-score of approximately 90% with respect to the geotechnical engineer's judgement results. In the comparison between YAMNET and 2D-CNN, the F-score of 2D-CNN was superior by a few per cent. This difference can be attributed to the length of time of the input audio signal.
Finally, the model can estimate whether rock materials are good or bad with almost the same accuracy as a geotechnical engineer in the field. In addition, the deep learning model can make a decision in a few seconds. In the future we plan to make effective use of smartphones equipped with the model to improve the efficiency of field work and save manpower. In addition, validation will be carried out to estimate not only whether the rock material is good or bad, but also the detailed material classification. For the deep learning learning algorithm, we intend to compare and study state-of-the-art technologies, such as transformers, and to carry out verification to improve the accuracy of the system.
How to cite: Sugeta, D., Furuki, H., and Miyamura, S.: Verification of the accuracy of rock material determination by hammer strike sound using deep learning , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7714, https://doi.org/10.5194/egusphere-egu25-7714, 2025.
Historic roads are integral to the cultural and visual heritage of landscapes, reflecting the historical narratives of regions and the populations they served. Mapping the planimetric and altimetric pathways of these ancient routes provides critical insights into how past societies interacted with their environment. Beyond their historical and archaeological value, rediscovered and restored roadways offer contemporary opportunities for "slow mobility," promoting sustainable tourism and local engagement with heritage sites [1].
This study presents a novel data-processing framework based on ground-penetrating radar (GPR) to improve the detection and geometric characterization of buried historical roads. The research focuses on two significant case studies in Rome, Italy-Villa dei Sette Bassi and Villa di Massenzio-where historical and archaeological evidence suggest the presence of uncharted ancient road connections.
The proposed methodology employs signal attribute-based analysis [2] to address key limitations in current detection techniques, including challenges posed by highly heterogeneous environments typical of archaeologically rich soils. By improving the accuracy and reliability of identifying structural components of ancient roadways, this approach advances our understanding of historical landscapes and supports sustainable heritage utilization strategies.
Acknowledgements
The authors would like to express their gratitude to the Parco Archeologico dell’Appia Antica and the Sovrintendenza Capitolina ai Beni Culturali for their support to the research and for providing access to the survey sites. Special thanks are also extended to Prof. Alessandra Ten, Prof. Carla Amici, and Dr. Ersilia Maria Loreti for their assistance with the archaeological interpretation.
How to cite: Bianchini Ciampoli, L., Parnow, S., Tosti, F., and Benedetto, A.: Retrieving signs of buried historical road tracks by GPR data processing, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16105, https://doi.org/10.5194/egusphere-egu25-16105, 2025.
Toll pavements are an important part of highways, whose condition is not necessarily assessed according to strict monitoring procedures. So, they are almost overlooked when planning maintenance and rehabilitation measures. In addition, the particular nature of the concrete material typically used for toll plaza, makes it much more difficult to maintain healthy and functional structures. Examples of this include the propagation of cracks to full depth and more complex rehabilitation measures that require a complete replacement of the pavement slab, both of which are due to the brittle nature of concrete.
However, to ensure a resilient and sustainable road infrastructure, an accurate assessment of the condition of concrete pavements on site is crucial. Non-destructive testing enables non-invasive field inspections, and the Falling Weight Deflectometer (FWD) is the most convincing example of rapid defect detection and is a method that outperforms conventional core drilling. With this in mind, deflectometric testing was primarily used in this study to evaluate five toll plazas with in-service concrete pavements on a PPP highway for which no long-term monitoring data was available. A testing campaign was set up to evaluate the condition of each pavement slab or lane, assess the durability of the slabs, and determine the effectiveness of load transfer across joints and cracks.
The observed deflection variability of the slabs prompted a distribution fitting analysis to estimate characteristic values and thresholds for common deflectometric indicators, which were then verified against input data from pavement design. It is proposed to use the developed conceptual approach to establish evaluation criteria for individual slabs or damaged joints of concrete pavements, which could assist responsible decision makers in managing pavements and maximizing their resilience during their service life.
How to cite: Gkyrtis, K., Loizos, A., and Plati, C.: Conceptualization of toll road pavements assessment with non-destructive deflectometric testing , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17586, https://doi.org/10.5194/egusphere-egu25-17586, 2025.
Bridge monitoring and maintenance is renowned as a critical priority for governments and stakeholders
worldwide. Such critical infrastructures play a fundamental role in transportation networks, therefore
requiring constant monitoring to ensure a correct functioning of these networks, as well as the safety of
road users. However, carrying out on-site surveys is usually time consuming, and funds are generally
lacking, especially in the public sector. Therefore, methods to continuously obtain data regarding bridge
conditions, while correctly storing and managing this information are required ever more by the industry.
Integration of Internet of Things (IoT) technologies into Building Information Modeling (BIM)
environments brings a new dimension to infrastructure monitoring and management by enabling realtime data acquisition, processing, and visualization procedures. Various IoT devices such as
accelerometers, temperature sensors, and environmental sensors can be used to supply the necessary
stream of data continuously, thus creating a dynamic, holistic view of both structural and operational
conditions of the analysed assets. All of these can also be integrated into BIM-based Digital Twin
platforms for monitoring, and to predict needs in maintenance and lifecycle management.
This research addresses the methods for integrating IoT networks into BIM environments, creating an
adaptive platform that could provide real-time updates and seamless data fusion. IoT sensors provide
localized and network-wide views of infrastructure conditions, including deformation patterns, thermal
anomalies, and stress distributions. Synchronizing these data streams with BIM models gives
stakeholders an intuitive and holistic platform for monitoring infrastructure health and planning
interventions. Furthermore, the research delves into the challenges related to the integration of IoT and
BIM, such as interoperability among diverse data sources, the continuous updating of BIM models, and
the scalability of such systems for large-scale adoption.
Applications using real world data show the potential of this approach in impacting the management of
critical transportation assets such as bridges and viaducts. IoT-enhanced BIM systems are pathways to
smarter, more resilient infrastructure networks by allowing proactive maintenance and efficient
resource allocation. This research highlights the need for industry-wide collaboration in the
standardization and adoption of such technologies so they can be effectively implemented at a global
scale.
A
How to cite: D'Amico, F., Bertolini, L., and Napolitano, A.: IoT and BIM integrated platform for more efficient infrastructure monitoring and management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18508, https://doi.org/10.5194/egusphere-egu25-18508, 2025.
Water is the main agent of deterioration in traditional buildings, particularly those with deficient or absent roofing and rainwater management systems. These sites wholly or partially surviving as ruins punctuate and define the landscape and represent invaluable heritage assets at risk of deterioration [1, 2].
In this research, the authors focus on historic masonry ruined structures located in England. An extensive literature review is used to investigate deterioration mechanisms and the role of climate change [3, 4], identify recurrent causes and sources of water ingress, evaluate the role of vegetations as well as the role of different maintenance regimes and past interventions.
The project will also investigate the most appropriate non-destructive testing (NDT) methods able to monitor and develop easy and repeatable methods of water ingress assessment in historic masonry ruined structures [5, 6]. The study focuses on the use of popular (e.g., ultrasound, ground-penetrating radar (GPR)) and less conventional NDTs in this domain, understanding main capabilities and limitations. The methods can set competencies, recurrent locations along the structure, accessibility and principles to underpin investigation and treatment of water ingress in traditional buildings. The paper concludes with recommendations for future research in this area. It includes the implementation of the most advanced non-destructive testing techniques and the acquisition of additional data concerning the behaviour of masonry structures under varying environmental conditions.
Keywords: Historic Masonry Ruins, Roofless Structures, Non-destructive Testing (NDT), Water Ingress, Climate Change
Funding: This is part of a Historic England funded project with title: “Collapse of Masonry Walls in Historic Ruined Structures: Understanding the Underlying Causes and Warning Signs; Identifying Investigation Strategy and Preventive Conservation Measures”.
References
[1] Ramirez, Ghiassi, Pineda and Lourenço, “Moisture and Temperature Effects on Masonry Structures: The Civic Tower of Pavia as a Case Study,” Lect. Notes Civ. Eng.
[2] Sass, O. & Viles, H. Heritage hydrology: a conceptual framework for understanding water fluxes and storage in built and rock-hewn heritage, Heritage Sci.
[3] Tolley, “Wigmore Castle, Herefordshire, the repair of a major monument: an alternative approach.”
[4] Laycock and Wood, “Understanding and controlling the ingress of driven rain through exposed, solid wall masonry structures,” Geol. Soc. Spec. Publ.,
[5] Franzoni, Berk, Bassi and Marrone, “An integrated approach to the monitoring of rising damp in historic brick masonry,” Constr. Build. Mater.
[6] M.I. Martinez-Garrido, R. Fort, M. Gomez-Heras, J. Valles-Iriso, M.J. Varas-Muriel, “A comprehensive study for moisture control in cultural heritage using non-destructive techniques, J. Appl. Geophysics.
How to cite: Bianchini, N., Balodimou, E., Husamaldin, L., Saadati, P., Tessema, T., D'Alessandro, D., and Tosti, F.: Non-Destructive Testing Approaches to Assess Water Ingress in Historic Masonry Ruins in the Context of Climate Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19747, https://doi.org/10.5194/egusphere-egu25-19747, 2025.
Designed to withstand repetitive cycles of aircrafts’ static loads and thermic gradients, apron rigid pavement requires long-term serviceability and structural reliability throughout its service period. Due to the vast dimensions of concrete superstructural asset monitored, international and local aviation regulative agencies advise the implementation of an effective Airport Pavement Management System (APMS), leading to structural analysis and residual service life prediction models of the existing asset to support airport handlers in construction and maintenance strategies.
Relying on measuring paved areas’ evolutive conditions, in an APMS it is recommended to carry out space and time consistent multi-source Non-Destructive Testing techniques (NDT). At the present state-of-the-art geometrical and mechanical properties collected through NDT surveys are individually analysed and compared against correspondent alarm thresholds. The disaggregated elastic and structural information from each NDT technique conveys into a pavement mechanics analysis, usually performed through finite element methods at the scale of sample unit of measurement. The resultant stress field for the specific superstructural configuration feeds a cumulative concrete fatigue damage and residual life model based on sample unit’s preset service period and traffic mix.
To accurately estimate priority of intervention assessing functional entity, degrading condition severity and asset importance, a structural model for rigid pavement elastic analysis based on NDT survey data needs to be further refined at the level of detail of each elementary unit surveyed. By using the same level of detail of NDT data collection stage, an analytical approximate solution for rigid mechanic and thermic rigid pavement rheological behaviour is being developed as an interoperable and computationally efficient alternative to finite element methods. Conveying NDT sampled properties for each elementary unit into a related structural and damage analysis, an effective pavement management system could be achieved for data-driven and airport-wise scalable asset management.
How to cite: Benedetto, A., Pinto, R., Bianchini Ciampoli, L., and Vezzari, V.: An integrated structural model for predicting rigid pavement damage based on NDT survey data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21016, https://doi.org/10.5194/egusphere-egu25-21016, 2025.
