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To address the increasing vulnerability of unmanned aerial vehicle (UAV) lightweight airframe structures to high-velocity fragment impacts in complex operational environments, this study combines high-velocity impact penetration tests, quasi-static strength tests, fracture-surface microanalysis, and finite-element simulation to systematically reveal the formation mechanism of typical penetration damage and its influence on residual strength. Results show that such penetration induces damage such as adiabatic-shear local melting zones, spall cracks, and grain-boundary separation, significantly weakening static strength and shifting the fracture mode from ductility- to brittleness-dominated. A modified fracture-mechanics criterion with higher prediction accuracy than the traditional net-section criterion is proposed, and a high-precision simulation model based on explicit–quasi-static coupling is established, which well reproduces damage morphology and tensile-failure processes. Compared with conventional manned aircraft structures, UAV airframes characterized by thinner skins and higher lightweighting ratios exhibit more pronounced sensitivity to penetration-induced micro-defects, making rapid residual-strength assessment essential for operational recovery and field-level repair decision-making. The research reveals the damage mechanism and provides an engineering-applicable residual-strength assessment method, offering a reliable theoretical basis and simulation tool for rapid UAV damage evaluation and fast-turnaround repair planning for civil and industrial UAV platforms.

Extracting damage information of buildings after an earthquake is crucial for emergency rescue and loss assessment. Low-altitude remote sensing by unmanned aerial vehicles (UAVs) for emergency rescue has unique advantages. In this study, we establish a remote sensing information-extraction method that combines ultramicro oblique UAV and infrared thermal imaging technology to automatically detect the structural damage of buildings and cracks in external walls. The method consists of four parts: (1) 3D live-action modeling and building structure analysis based on ultramicro oblique images; (2) extraction of damage information of buildings; (3) detection of cracks in walls based on infrared thermal imaging; and (4) integration of detection systems for information of earthquake-damaged buildings. First, a 3D live-action building model is constructed. A multi-view structure image for segmentation can be obtained based on this method. Second, a method of extracting information on damage to building structures using a 3D live-action building model as the geographic reference is proposed. Damage information of the internal structure of the building can be obtained based on this method. Third, based on analyzing the temperature field distribution on the exterior walls of earthquake-damaged buildings, an automatic method of detecting cracks in the walls by using infrared thermal imaging is proposed. Finally, the damage information detection and assessment system is researched and developed, and the system is integrated. Taking earthquake search-and-rescue simulation as an example, the effectiveness of this method is verified. The damage distribution in the internal structure and external walls of buildings in this area is obtained with an accuracy of 78%.

The paper describes the basic concept of the integration between UAV surveying results and BIM. As a case study, it was considered the large spatial shell that served as a hangar for the Antonov An-225 Mriya, the largest in the world strategic cargo aircraft with a maximum take-off weight of 640 tons. Due to an explosion inside, the hangar and aircraft were significantly damaged. The key point of the study is the damage estimation by analysis and modeling of this unique engineering structure. The study has included several steps: hangar structure documentation (before damage), UAV surveying of the hangar (for ongoing condition estimation), terrestrial measurements for the control, and integration of 3D models inside BIM for structural analysis. Deploying the UAV allowed us to generate detailed 3D models of the hangar by means of photogrammetry and computer vision methods. The inclusion of the field geodetic measurements into the processing made it possible to increase significantly positioning accuracy of the results to the sub-centimeter level and served as a ground truth for the models obtained based on UAV sensors data. The results proved the feasibility of BIM and UAV photogrammetry for the hangar stability model development and practical verification based on geospatial and structural engineering data.

Aging bridges require regular inspection due to performance deterioration. For this purpose, numerous researchers have considered the use of unmanned aerial vehicle (UAV) systems for structural health monitoring and inspection. However, present UAV-based inspection methods only represent the type and extent of external damage, but does not assess the seismic performance. In this study, a seismic fragility analysis of deteriorated bridges employing a UAV inspection-based updated digital twin is proposed. The proposed method consists of two phases: (1) bridge condition assessment using UAV inspection for updating the digital twin and (2) seismic fragility analysis based on the updated digital twin. To update the digital twin, the bridge damage grade is assigned based on the UAV inspection, and subsequently, the corresponding damage index is calculated. The damage index is utilized as a percentage reduction in the stiffness of finite element (FE) model, based on a previously proposed research. Using the updated digital twin, the seismic fragility analysis is conducted with different earthquake motions and magnitudes. To demonstrate the proposed method, an inservice pre-stressed concrete box bridge is examined. In particular, the seismic fragility curves of deteriorated bridges are compared with those of intact bridges. The numerical results show that the maximum failure probability of the deteriorated bridges is 3.6% higher than that of intact bridges. Therefore, the proposed method has the potential to updated the digital twin effectively using UAV inspection, allowing for seismic fragility analysis of deteriorated bridges to be conducted.

Structural damage classification in composite UAV wings is a key challenge in Structural Health Monitoring (SHM), particularly under barely visible impact damage conditions. Fiber Bragg Grating (FBG) sensor networks provide high-resolution strain data; however, systematic experimental benchmarking of lightweight neural architectures trained on real FBG datasets remains limited, especially under sensor degradation scenarios. This work presents a four-phase benchmarking study of Multilayer Perceptron (MLP) configurations using strain measurements from a composite UAV wing instrumented with 32 FBG sensors across five damage states and 210 loading experiments. The framework evaluates optimization strategies, hyperparameter sensitivity, architectural depth, and robustness under controlled sensor dropout, Gaussian noise, and wavelength drift perturbations. Results indicate that compact architectures with progressive dimensional reduction (256–128–64) trained using adaptive optimizers (AdamW and Nadam) achieve the best balance between macro-F1 performance (up to 0.85 during validation), stability, and computational efficiency. Robustness analysis shows gradual performance degradation under sensor loss, suggesting distributed strain-field learning. These findings provide practical guidelines for selecting computationally efficient and robust neural models for deployable FBG-based SHM systems in aerospace applications.

The increasing frequency of hydrometeorological extremes, such as torrential rainfall, strong winds, and hailstorms, often causes widespread mechanical damage to crops. This study evaluates the potential of cost-effective unmanned aerial vehicle (UAV) photogrammetry with a standard RGB camera for quantifying crop damage. A maize field with mechanical damage caused by wild boar activity was used as an analogue for storm-induced damage. Two approaches were applied: (i) a 3D structural method based on Canopy Surface Models (CSMs) derived from Structure-from-Motion (SfM) photogrammetry, and (ii) automated image classification using a Support Vector Machine (SVM) combined with Object-Based Image Analysis (OBIA). The accuracy of the damage assessment was compared using two terrain inputs: a UAV-derived DEM (UAV DEM) and the official Czech national LiDAR-based DEM (DEM 5G). The results showed high consistency between both methods and datasets. The relative crop damage rate was 29.25% with the UAV DEM and 26.76% with the DEM 5G, with a spatial agreement exceeding 95%. Jaccard similarity coefficients confirmed strong concordance (0.8953 and 0.9207). The findings highlight the applicability of UAV-based 3D structural analysis for late-stage crop monitoring, when spectral indices lose reliability. They also emphasise that the official DEM 5G can serve as a suitable substitute for a UAV-derived DEM in damage assessment. The methodology thus represents a rapid, cost-effective, and operationally feasible solution for agricultural monitoring, insurance claims, and environmental management.

Detecting cracks early benefits building maintenance by assessing structural safety, which in turn helps prevent potential severe damage and collapse, given that cracks in concrete surfaces often reflect underlying structural damage. However, the conventional method by human hands is time-consuming, inconvenient, and high risk for inspectors. In this present study, an improved framework for inspecting building surface cracks, which integrates digital innovations of Unmanned Aerial Vehicle (UAV) and deep learning technologies with wide-area coverage, high efficiency, and less intervention, is established. The feasibility of the proposed approach is demonstrated by conducting an experimental test on an in-service office building. The results show that not only can we achieve a prediction accuracy of over 97% on the validation dataset, but also that increasing the number and variety of images in the training dataset positively impacts the ability to detect concrete cracks. However, this improvement might not be as notable once the model has already learned sufficient features of concrete cracks. Additionally, a 3D model was created to virtually showcase the detection results. This opens up new possibilities for conducting building damage inspections by integrating these results into a virtual 3D space, which enhances overall structural health management and offers new insights for improving detection performance. Challenges and future directions to improve the effectiveness and address potential improvement approaches of the proposed framework in practice are also suggested.

For the inspection of structures, particularly bridges, it is becoming common to replace humans with autonomous systems that use unmanned aerial vehicles (UAV). In this paper, a framework for autonomous bridge inspection using a UAV is proposed with a four-step workflow: (a) data acquisition with an efficient UAV flight path, (b) computer vision comprising training, testing and validation of convolutional neural networks (ConvNets), (c) point cloud generation using intelligent hierarchical dense structure from motion (DSfM), and (d) damage quantification. This workflow starts with planning the most efficient flight path that allows for capturing of the minimum number of images required to achieve the maximum accuracy for the desired defect size, then followed by bridge and damage recognition. Three types of autonomous detection are used: masking the background of the images, detecting areas of potential damage, and pixel-wise damage segmentation. Detection of bridge components by masking extraneous parts of the image, such as vegetation, sky, roads or rivers, can improve the 3D reconstruction in the feature detection and matching stages. In addition, detecting damaged areas involves the UAV capturing close-range images of these critical regions, and damage segmentation facilitates damage quantification using 2D images. By application of DSfM, a denser and more accurate point cloud can be generated for these detected areas, and aligned to the overall point cloud to create a digital model of the bridge. Then, this generated point cloud is evaluated in terms of outlier noise, and surface deviation. Finally, damage that has been detected is quantified and verified, based on the point cloud generated using the Terrestrial Laser Scanning (TLS) method. The results indicate this workflow for autonomous bridge inspection has potential.

Bridge inspections are relied heavily on visual inspection, and usually conducted within limited time windows, typically at night, to minimize their impact on traffic. This makes it difficult to inspect every meter of the structure, especially for large-scale bridges with hard-to-access areas, which creates a risk of missing serious defects or even safety hazards. This paper presents a new technique for the semi-automated damage detection in tunnel linings and bridges using a hybrid approach based on photogrammetry and deep learning. The first approach involves using photogrammetry to reconstruct a 3D model. It is shown that a model with sub-centimeter accuracy can be obtained after noise removal. However, noise removal also reduces the point cloud density, making the 3D point cloud unsuitable for quantification of small-scale damages such as fine cracks. Therefore, the captured images are also analyzed using deep convolutional neural network (CNN) models to enable crack detection and segmentation. For this aim, in the second approach, the 3D model is generated by the output of CNN models to enable crack localization and quantification on 3D digital model. These two approaches were evaluated in separate case studies, showing that the proposed technique could be a valuable tool to assist human inspectors in detecting, localizing, and quantifying defects on concrete structures.

Monitoring cracks opening on concrete bridges is a key aspect for structural health assessment. Digital image processing, combined with Unmanned Aerial Vehicles (UAVs) and photogrammetry, allows for non-contact 3D reconstruction of cracks, reducing costs and potential unsafe factors involved in manual inspections. This paper presents a flexible procedure based on UAV photogrammetry for accurate evaluation of cracks geometry, that can be implemented for periodic structural monitoring. Stereo-pair of images, acquired with UAVs close to the cracked surface, are used to build a scaled photogrammetric model through Structure-from-Motion. Cracks are detected on images by image binarization and digital image processing techniques. Thereafter, one single image is used to reconstruct crack 3D geometry, by back-projecting crack image coordinates on a 3D model of the object. This can be built from the current stereo-pair of images, or based on an existing photogrammetric model, in the case of a periodic monitoring set-up. Crack width is accurately estimated in 3D world. The procedure is tested and evaluated in a case study, obtaining millimetric accurate results, which is in line with the average ground sample distance of the images employed. Results highlight the potentials of UAVs and photogrammetry not only for bridge inspections and damages localization, but also for accurately evaluating cracks geometry and helping structural engineers to assess structure health conditions.