Klaudia Pasternak

The accurate inventory of hydro-technical infrastructure, such as bridge structures, requires the seamless integration of data capturing both above-water and submerged geometries. This study presents a comprehensive workflow for the fusion of multi-source point clouds acquired using an integrated Norbit iLidar and Winghead i77h multibeam echosounder (MBES) system. A primary focus is placed on the challenges of data acquisition in demanding environmental conditions, specifically under-bridge areas characterized by degraded GNSS reception and multipath interference. To ensure survey-grade accuracy and geometric consistency, a rigorous field calibration procedure, including a specialized LiDAR and MBES Patch Test, was implemented. The results demonstrate that precise determination of boresight angles — specifically a Roll correction of −0.3° for the LiDAR sensor—is critical to eliminating systematic offsets and "ghosting" effects in the fused model. Furthermore, the study accounts for environmental factors such as sound velocity variability (1486.5–1487.6 m/s) and beam refraction at the air-water interface. The resulting integrated point cloud, with resolutions of 1 cm (LiDAR) and 2 cm (MBES), served as the foundation for structural 3D modeling using Building Information Modeling (BIM) and Constructive Solid Geometry (CSG) approaches. The findings confirm that the proposed diagnostic and correction workflow significantly enhances the reliability of digital twins for bridge health monitoring and hydro-morphological analysis in complex engineering environments.

The automation of the detection infrastructure in GPR imagery is a key issue, particularly in the context of the non-invasive acquisition of radargrams with a multi-antenna ground-penetrating radar. Due to the fact that the dataset acquired with a multi-antenna GPR is very large, in the context of automating the process of detecting hyperbolas, the authors have proposed an adaptive approach to the selection of GPR images. The aim of this project was to develop a method for the selection of GPR images by means of applying the appropriate quality indicators. The authors propose a new, adaptive approach to the selection of radargrams that were recorded during the route of a GPR in a single profile, where several radargrams were recorded. Depending on the obtained initial values of the standard indicators for the assessment of the quality and quality maps of the radargrams, those images from selected channels that will ensure the highest possible quality and efficiency of hyperbola detection were selected. The stage of image quality assessment is essential in the context of improving the effectiveness of the automated detection of underground infrastructure. The quality assessment was performed based on the entropy indicator, PIQE, and Laplacian variance. The selected quality indicators allowed the authors to assess the degree of blurring, noise, and the number of details representing the underground structures that are present in GPR images. An additional product of the quality assessment were the generated maps that present the distribution of entropy in the analyzed images. The image selection was verified based on the results of the parameters that assess the effectiveness of the detection of hyperbolas that represent underground networks. The proposed innovative adaptive approach to the selection of images acquired by GPR enabled a significant improvement in the efficiency of the detection of hyperbolas representing underground utility networks, by 15–40%, shortening data processing and infrastructure detection times.

Monitoring reclaimed landfills is essential for ensuring their stability and monitoring the regularity of facility settlement. Insufficient recognition of the magnitude and directions of these changes can lead to serious damage to the body of the landfill (landslides, sinkholes) and, consequently, threaten the environment and the life and health of people near landfills. This study focuses on using UAV photogrammetry to monitor geometric changes in reclaimed landfills. This approach highlights the advantages of UAVs in expanding the monitoring and providing precise information critical for decision-making in the reclamation process. This study presents the result of annual photogrammetry measurements at the Słabomierz–Krzyżówka reclaimed landfill, located in the central part of Poland. The Multiscale Model to Model Cloud Comparison (M3C2) algorithm was used to determine deformation at the landfill. The results were simultaneously compared with the landfill’s reference (angular–linear) measurements. The mean vertical displacement error determined by the photogrammetric method was ±2.3 cm. The results showed that, with an appropriate measurement methodology, it is possible to decide on changes in geometry reliably. The collected 3D data also gives the possibility to improve the decision-making process related to repairing damage or determining the reclamation direction of the landfill, as well as preparing further development plans.

Precise determination of the location of underground utility networks is crucial in the field of civil engineering for: the planning and management of space with densely urbanized areas, infrastructure modernization, during construction and building renovations. In this way, damage to underground utilities can be avoided, damage risks to neighbouring buildings can be minimized, and human and material losses can be prevented. It is important to determine not only the location but also the type of underground utility network. Information about location and network types improves the process of land use design and supports the sustainable development of urban areas, especially in the context of construction works in build-up areas and areas planned for development. The authors were inspired to conduct research on this subject by the development of a methodology for classifying network types based on images obtained in a non-invasive way using a Leica DS2000 ground penetrating radar. The authors have proposed a new classification algorithm based on the geometrical properties of hyperboles that represent underground utility networks. Another aim of the research was to automate the classification process, which may support the user in selecting the type of network in images that are sometimes highly noise-laden. The developed algorithm shortens the time required for image interpretation and the selection of underground objects, which is particularly important for inexperienced operators. The classification results revealed that the average effectiveness of the classification of network types ranged from 42% to 70%, depending on the type of infrastructure.

Surface monitoring of landfills is crucial not only during their operation but also for later land restoration and development. Measurements concern environmental factors, such as leachate, migration of pollutants to water, biogas, and atmospheric emissions, and geotechnical factors, such as stability and subsidence. Landfill subsidence can be measured using modern surveying techniques. Modern measurement methods for landfill body displacement monitoring and their control after restoration and adaptation as recreational areas include terrestrial laser scanning (TLS), and scanning and low-altitude photogrammetric measurements from an unmanned aerial vehicle (UAV). The acquired measurement data in the form of 3D point clouds should be referenced to the local control network to enable a comprehensive analysis of data acquired using various techniques, including geotechnical sensors such as benchmarks, piezometers, and inclinometers. This study discusses the need for surface monitoring of municipal solid waste (MSW) landfills. A properly 3-D mapped landfill mass is the basis for ensuring the geotechnical safety of the restored landfill. Based on archival data and current measurements of the Radiowo landfill (Poland), this study compares the advantages and limitations of the following measurement techniques: linear and angular measurements, satellite measurements, TLS, and UAV scanning and photogrammetry, considering specific conditions of the location and vegetation of the landfill. Solutions for long-term monitoring were proposed, considering the cost and time resolution necessary for creating a differential model of landfill geometry changes.

The hydrothermal method of obtaining nano zinc oxide doped with different contents of aluminum ions (III) was presented and discussed in this paper. Aqueous solution of Zn(NO₃)₂*6H₂O and Al(NO₃)₃*9H₂O salts mixture were used as the synthesis precursor. In order to reduce the process time all reactions were performed in a microwave reactor. The influence of process parameters and the content of impurity ions on the properties of synthesized nano zinc oxide were analyzed. In addition to zinc oxide doped with Al(III) ions, an additional spinel phase (ZnAl₂O₄) was obtained. The luminescent properties of nano zinc oxide as a function of the dopant ions were also discussed. Based on the luminescence measurements results, it was found that the luminescence intensity decreases with the increasing dopant content. The obtained materials are aimed to be implemented as luminescent materials in optoelectronic and sensors.

Reliable detection of underground infrastructure is essential for infrastructure modernization works, the implementation of BIM technology, and 3D cadasters. This requires shortening the time of data interpretation and the automation of the stage of selecting the objects. The main factor that influences the quality of radargrams is noise. The paper presents the method of data filtration with use of wavelet analyses and Gabor filtration. The authors were inspired to conduct the research by the fact that the interpretation and analysis of radargrams is time-consuming and by the wish to improve the accuracy of selection of the true objects by inexperienced operators. The authors proposed automated methods for the detection and classification of hyperboles in GPR images, which include the data filtration, detection, and classification of objects. The proposed object classification methodology based on the analytic hierarchy process method introduces a classification coefficient that takes into account the weights of the proposed conditions and weights of the coefficients. The effectiveness and quality of detection and classification of objects in radargrams were assessed. The proposed methods make it possible to shorten the time of the detection of objects. The developed hyperbola classification coefficients show promising results of the detection and classification of objects.

Applying georadar (GPR) technology for detecting underground utilities is an important element of the comprehensive assessment of the location and ground infrastructure status. These works are usually connected with the conducted investment processes or serialised inventory of underground fittings. The detection of infrastructure is also crucial in implementing the BIM technology, 3D cadastre, and planned network modernization works. GPR detection accuracy depends on the type of equipment used, the selected detection method, and external factors. The multitude of techniques used for localizing underground utilities and constantly growing accuracy demands resulting from the fact that it is often necessary to detect infrastructure under challenging conditions of dense urban development leads to the need to improve the existing technologies. The factor that motivated us to start research on assessing the precision and accuracy of ground penetrating radar detection was the need to ensure the appropriate accuracy, precision, and reliability of detecting underground utilities versus different methods and analyses. The results of the multi-variant GPR were subjected to statistical testing. Various analyses were also conducted, depending on the detection method and on the current soil parameters using a unique sensor probe. When planning detection routes, we took into account regular, established grids and tracked the trajectory of movement of the equipment using GNSS receivers (internal and external ones). Moreover, a specialist probe was used to evaluate the potential influence of the changing soil conditions on the obtained detection results. Our tests were conducted in a developed area for ten months. The results confirmed a strong correlation between the obtained accuracy and the measurement method used, while the correlation with the other factors discussed here was significantly weaker.