Search Results (113)

Recent evidence shows that multi-hazard events are becoming more frequent across Europe, highlighting the need to move beyond single-hazard approaches and toward integrated risk assessment. Despite recent advances, four key gaps persist: limited quantitative research on hazard interactions; model complexity that restricts large-scale applicability; narrow hazard coverage with insufficient integration of climate change scenarios; and neglect of cumulative impacts from sequential events. This study makes two complementary contributions. First, it proposes a scalable, unified multi-hazard risk assessment framework applicable at regional and European scales. In this framework, multi-hazard considerations are embedded throughout the entire assessment process—from study domain definition and loss metrics, through hazard characterization and conceptual incorporation of dynamic vulnerability, to the probabilistic treatment of hazard interactions and compound effects via a probabilistic, conditional-dependency framework conceptually represented as a Bayesian network. Second, based on the literature review conducted in this study, no prior European study was identified that combines flood, earthquake, and earthquake-triggered landslide hazards at the asset level for educational facilities. Therefore, this work is, to the best of the authors’ knowledge, among the first such quantitative, asset-level multi-hazard risk assessments. The framework is demonstrated for over 1700 school buildings in the Region of Central Macedonia, Greece, using pan-European open-access datasets (ESHM20, ESRM20, JRC, GIRI, and ELSUS v2), making it readily transferable across Europe. By supporting risk-informed prioritization of mitigation and resilience investments, this work is consistent with the broader objectives of the Sendai Framework and the UN Sustainable Development Goals, particularly SDG 11 and SDG 13. Full article

Extracting reliable damage-sensitive features from sparse sensor networks under Environmental and Operational Variations (EOV) remains a critical challenge in Structural Health Monitoring (SHM). The purpose of this study is to overcome this limitation by proposing a novel, data-driven framework utilizing a cost-effective network of high-sensitivity triaxial roving accelerometers. The methodology integrates an AutoRegressive with eXogenous inputs (ARX) model and Wavelet Packet Decomposition (WPD) to extract robust, damage-sensitive features from complex vibration data. To handle the high-dimensionality of the extracted signals and achieve optimal multi-sensor data fusion, Block-wise Principal Component Analysis (PCA) is employed as a signal sanitation and feature reduction tool. This algorithmic pipeline is applied to a full-scale bridge pier subjected to RC jacketing. The structural enhancements and dynamic behavior shifts post-retrofitting were statistically quantified using the Mahala Nobis distance. The analysis revealed a 41.2% attenuation in median vibration intensity and successfully verified the structural improvements at a 99% confidence interval, clearly distinguishing the retrofitting effects from ambient noise. The proposed framework successfully isolates true structural changes from EOV, providing a reliable non-destructive evaluation tool for continuous monitoring in practical civil engineering applications. Full article

Damage detection is a critical component of Structural Health Monitoring (SHM) strategies. Data-driven machine learning methods are widely employed for this purpose. However, their effective application to the management of structures and infrastructure requires addressing key challenges, in particular the limited knowledge during training of how damage affects monitored features. Although one-class classification algorithms may be adopted, their performance strongly depends on the appropriate definition of their components and the tuning of their parameters. When this optimisation is performed using undamaged data only, it may result in classifiers that are insensitive to small-scale damage. Within this context, the present study investigates the sensitivity of damage detection performance of a Deterministically Generated Negative Selection Algorithm to feature scaling and two intrinsic algorithm parameters. A novel strategy for generating artificial damaged data to support parameter tuning is proposed and evaluated against alternative approaches. A wide range of parameter values is explored, considering multiple pairs of monitored features as detection spaces, and four feature scaling methods are compared. Feature scaling is a fundamental aspect of classification problems. Thus, the main findings may be generalised to other machine learning algorithms for damage detection. To ensure full control over the monitoring data and underlying phenomena, a numerical case study is adopted. A replicable framework for generating controlled yet realistic structural monitoring data is presented. The simulated monitoring captures the natural frequencies of a bridge, accounting for temperature effects, and incorporates three damage scenarios: one diffuse and two localised at critical locations. The analysis highlights the importance of conducting anomaly detection in feature spaces where damage affects each feature differently. Pairing a damage-sensitive feature with one unaffected by damage (e.g., temperature) may be advantageous when damage effects are not known a priori. Although suboptimal, the proposed strategy for generating damaged data for parameter tuning outperforms approaches based solely on undamaged data. The identified parameter trends suggest that small detector radii and relatively short censoring distances improve algorithm performance across all normalisation strategies and damage scenarios. The results show that feature scaling has limited influence for large damage extents but becomes critical for the early detection of minor damage. Z-score normalisation provides the best overall balance between false negatives and false positives, whereas methods using denominator multipliers smaller than one provide higher True Positive Rates for small damage extents. Full article

Automated pavement distress detection (PDD) is critical for the structural health monitoring (SHM) of transportation infrastructure, yet existing methods struggle with real-time multi-target detection under resource constraints. In this paper, YOLOv8-PDD was constructed based on YOLOv8 by introducing the large separable kernel attention (LSKA) mechanism module into the Spatial Pyramid Pooling—Fast (SPPF) module, replacing Complete-IoU (CIoU) loss with Distance-IoU (DIOU) loss as the loss function, and adopting Soft-Non-Maximum Suppression (NMS) to replace the original NMS algorithm. The proposed YOLOv8-PDD achieved 78.3% mean average precision with intersection over union above 0.5 (mAP@0.5 +8.1%) with a minimal complexity increase of +0.2 GFLOPs compared to the baseline YOLOv8n model. While incurring a negligible increase in latency (+0.09 ms), YOLOv8-PDD significantly outperforms YOLOv8n in detection accuracy (mAP@0.5 +8.1%), offering a superior accuracy–efficiency trade-off for real-time applications. YOLOv8-PDD performed well in detecting all categories, with AP values above 75% except for transverse crack and strip patch. Significant improvements in pothole detection AP@0.5 (+22.1%) and strip patch detection AP@0.5 (+17.7%) indicate superior small target and complex background adaptability. Our model achieved a detection efficiency of 68 frames per second (FPS) on consumer-grade CPUs (OpenVINO-optimized), outperforming 10 models (e.g., YOLOv5n and RTDETR-l) in accuracy–speed balance. Full article

Self-healing materials (SHMs) are a class of bio-inspired materials capable of autonomously repairing damage, similar to how living organisms heal wounds. The core motivation behind SHMs is to extend the service life of components while enhancing safety and reducing maintenance or replacement needs. SHMs can be broadly categorized into intrinsic systems, which rely on reversible internal bonds (dynamic covalent or supramolecular interactions) to heal repeatedly, and extrinsic systems, which embed external healing agents (e.g., microcapsules or vascular networks) that are released upon damage to effect repairs. Researchers have demonstrated self-healing behavior in diverse material families, including polymers, metals, ceramics/cementitious materials, and protective coatings, thereby improving crack resistance, fatigue life, and reliability across aerospace, automotive, civil infrastructure, energy storage, and microelectronics applications. Advances in material design and additive manufacturing have started integrating SHMs into practical structures. However, challenges such as scaling up production, maintaining mechanical performance, and ensuring long-term durability remain. Reported healing efficiencies in self-healing materials typically range from ~50% to near-complete recovery (~100%), depending on material systems and testing conditions, highlighting key trade-offs between healing performance, mechanical integrity, and scalability. Overall, SHMs represent a promising strategy for creating safer and more sustainable engineering systems, with ongoing developments aimed at overcoming current limitations and expanding their capabilities. This review highlights key trade-offs between healing efficiency, mechanical performance, and scalability, providing insights into the design and application of next-generation self-healing materials. Full article

The high-speed and high-Reynolds-number conditions encountered in actual flight, coupled with the performance requirements for both low-speed climb and high-speed cruise, pose challenges for boundary-layer transition prediction and optimization in laminar design. Consequently, there are still relatively few mature and applicable high-speed laminar airfoils available. To address the insufficient validation of Reynolds-averaged Navier-Stokes (RANS) models under actual high-speed and high-Reynolds-number (Re > 10⁷) flight conditions, the practical fidelity of the most commonly used $\gamma -{\tilde{R}e}_{\theta t}$ transition model as well as NASA CFL3D solver is systematically assessed based on NASA HSNLF(1)-0213 and Honda SHM-1 high-speed business jet laminar airfoils. To the best of the authors’ knowledge, since there is no available geometry data for the SHM-1 airfoil, this is the first systematic analysis of this airfoil from a perspective other than the design team. Results demonstrate that the $\gamma -{\tilde{R}e}_{\theta t}$ transition model could accurately capture natural transition and separation-induced transition at Reynolds numbers up to 16.2 × 10⁶, while also exhibiting strong robustness against variations in Mach and Reynolds number. Using the HSNLF(1)-0213 as the baseline airfoil and the design conditions of SHM-1, a multi-objective drag-reduction optimization considering climb and cruise performance was then conducted based on the Isight platform. The optimal airfoil achieves 9.53% climb drag reduction and 9.21% cruise drag reduction, revealing that aft-loading and strong favorable pressure gradients are essential to balance lift characteristics and sustain extensive laminar flow at high Reynolds numbers. Full article

Thermal contextual algorithms for 375 m VIIRS active fire detection can produce substantial commission errors over persistent non-wildfire heat sources (e.g., refineries, gas flares, and volcanoes), and globally fixed thresholds may be suboptimal under heterogeneous thermal backgrounds. We present a lightweight spatiotemporal prior layer that augments by applying prior-guided, pixel-level parameter switching during the discrimination stage. The layer combines: (i) a persistent non-wildfire thermal anomaly mask (PFM) derived from multi-year VNP14IMG recurrence and seasonality statistics on a 0.004° grid, and (ii) a short-term heat-source mask (SHM) based on nighttime VIIRS I4/I5 brightness temperature stability to capture newly emerged or rapidly intensifying static sources. Pixels flagged by either prior are processed with a stricter parameter set, while other pixels follow the baseline setting. We evaluate the method using a stratified validation dataset (N = 3435) spanning industrial/urban clusters, volcanic regions, forest/grassland wildfires, and fragmented crop residue burning, with validation supported by independent high-resolution imagery (Sentinel-2/Landsat) and external POI datasets. The framework markedly reduces false positives in high-interference zones (industrial/urban false positive rate from 88.6% to 22.7%; volcanic from 100.0% to 57.3%) while preserving high performance for forest/grassland wildfires (F1 ≈ 0.999). For fragmented residue burning, omission error decreases from 11.2% to 1.3%, improving detection completeness without an apparent increase in commission errors. Overall, the results suggest that integrating long- and short-term spatiotemporal priors via threshold switching can improve the robustness and interpretability of contextual VIIRS fire detection under complex thermal backgrounds in the evaluated scenarios. Full article

Tracking structural behavior is critically important to reduce maintenance and repair costs. Structural Health Monitoring (SHM) aims to evaluate the structural integrity, detect damage or abnormalities, and estimate overall safety. The integration of Machine Learning techniques has significantly advanced SHM by enabling the identification of deterioration patterns through sensor data analysis. This study focuses on classifying different vibration patterns recorded under various excitation scenarios (ambient, transient, and forced) using sensors installed directly on a 3-DoF structure. The proposed approach used a two-dimensional convolutional neural network (2D-CNN) trained on vibration image patterns generated from vibration signal scalogram images. To address dataset imbalance, stratified 5 × 3 Nested cross-validation and multiple performance metrics were computed to ensure robust evaluation. The proposed method was compared with single-sensor scalogram approaches and baseline models, including Support Vector Machines (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), One-Dimensional Convolutional Neural Network (1D-CNN), and Long Short-Term Memory (LSTM) models, incorporating class-weighting strategies. Additionally, the contribution of the Total Energy Delivered by Sensor (TES) feature was evaluated for SVM, RF, and XGBoost models. The 2D-CNN model achieved superior performance in identifying excitation types associated with structural dynamic behavior, highlighting its effectiveness for structural vibration pattern recognition in SHM applications. Full article

Background: Estrogen deficiency following human menopause or rodent ovariectomy (OVX) induces adverse alterations in body composition and metabolic function. This study investigated the combined effects of acetic acid supplementation and voluntary exercise on metabolic health and skeletal muscle mitochondrial function using an OVX mouse model. Methods: Forty female C57BL/6J mice (8 weeks old) were randomly assigned to 5 groups: sham (SHM), ovariectomized control (OVX), OVX with exercise (OVX-E), OVX with acetic acid (OVX-A), and OVX with both interventions (OVX-AE). Following a 1-week recovery from OVX, a 13-week intervention was initiated: 5% sodium acetate-supplemented chow and/or voluntary wheel running. Body composition, glucose tolerance, total energy expenditure, skeletal muscle mitochondrial function, and the contents of AMPKα, PGC-1α, and carbonyl protein were assessed. Results: OVX impaired whole-body metabolism and skeletal muscle mitochondrial function, specifically in the gastrocnemius muscle. While the exercise alone failed to mitigate the OVX-induced mitochondrial dysfunction, the combined treatment of exercise and acetic acid supplementation significantly rescued from the OVX-induced mitochondrial dysfunction. Conclusions: OVX resulted in detrimental changes in whole-body metabolism, but voluntary exercise and/or acetic acid supplementation had no rescuing effects on those parameters. In gastrocnemius muscle, acetic acid supplementation during exercise enhanced mitochondrial function in OVX mice. Full article

Radar interferometry can provide important information for Structural Health Monitoring (SHM) of bridges and other transportation structures. In this article, joint communication and sensing (JCAS) telecommunication infrastructure is tested as a ground-based radar, offering advantages in terms of long-term costs, deployment and maintenance. This work specifically addresses the estimation of the radar support movement (i.e., pylon or mast), which represents a major challenge in this kind of measurements. Movements of the radar system combine with the true target motion and, if not correctly compensated, can compromise the accuracy of the results. A technique for estimating radar movements based on the displacement tracking of multiple permanent scatterers (PSs) in the scenario is presented. True target displacements can then be retrieved by applying linear regression methods to fixed PSs located at different viewing angles, accounting for both radar movements and atmospheric displacement components. The technique was validated using real data acquired during an experimental campaign on a bridge test site. First, results obtained for a target subject to known displacements are shown. A second measurement session was aimed at testing the method for bridge dynamic monitoring. Finally, the same technique was applied antenna mast monitoring in terms of modal analysis and vibration characterization. Full article

(1) Background: Structural Health Monitoring Systems (SHMSs) can reduce maintenance costs and aircraft downtime. However, their economic impact remains underexplored, particularly in cost–benefit terms. (2) Methods: This study conducted a targeted literature review on all the existing studies consisting of seventeen economic analyses of SHMS applications. Key features—such as SHMS type, structural material, vehicle type, integration stage, and cost elements—were classified to identify prevailing trends and gaps. (3) Results: The analysis revealed a predominance of piezoelectric-based SHMS applied to metallic fixed-wing aircraft, with limited attention to composite structures and e-VTOLs. Most studies focused on maintenance phase impacts, overlooking integration costs during manufacturing. Potential benefits like operational life extension, prognostic capabilities, and safety margin reduction were rarely explored, while critical drawbacks such as detection performance, reliability, and power consumption were underrepresented. Maintenance and fuel costs were the most frequently considered economic drivers; downtime costs were often neglected. (4) Conclusions: Although the majority of reviewed studies suggest a positive economic impact from SHMS implementation, significant gaps remain. Future research should address SHMS reliability, integration during early design stages, and applications to emerging aircraft like e-VTOLs to fully realize SHMS economic advantages. Full article

The complexity of smart home technologies and the need for personalized energy efficiency solutions highlight the importance of user-oriented decision-support tools. This study presents a Smart Home Management System (SHMS) selection solution that combines a web-based dashboard, a mobile application, and a relational database. A 54-question structured questionnaire was designed to capture user requirements, and four alternatives—KNX, JUNG Home, LB Management, and eNet Smart Home—were compared using the Simple Additive Weighting (SAW) method. Evaluation criteria included installation complexity, communication technology, integration and control capabilities, and user experience. The system was implemented with Next.js, React Native, and Post-greSQL, ensuring flexibility, scalability, and secure data management. Preliminary evaluation with specialists (system integrators, architects, designers) and students confirmed the coherence of the questionnaire, the adequacy of criteria, and the clarity of recommendations. Results showed that the tool improves user engagement, reduces decision-making uncertainty, and supports the adoption of energy-efficient residential solutions. The study’s main limitation is the small test sample, which will be expanded in future large-scale validation. Planned improvements include interactive product comparisons, cost estimation, adaptive questionnaire logic, and 3D visualizations. Overall, the system bridges the gap between technical SHMS solutions and user-oriented decision-making, offering practical and academic value. Full article

Data of operational conditions of structural components, acquired, e.g., in structural health monitoring (SHM), is of great interest to optimise products from one generation to the next, for example, by adapting them to occurring operational loads. To acquire data for this purpose in the desired quality, an optimal sensor placement for so-called shape and load sensing is required. In the case of large-scale structural components, wireless sensor networks (WSN) could be used to process and transmit the acquired data for real-time monitoring, which furthermore requires an optimisation of sensor node positions. Since most publications focus only on the optimal sensor placement or the optimisation of sensor node positions, a methodology for both is implemented in a Python tool, and an optimised WSN is realised on a demonstration part, loaded at a test bench. For this purpose, the modal method is applied for shape sensing as well as a physics-informed neural network for solving inverse problems in shape sensing (iPINN). The WSN is realised with strain gauges, HX711 analogue-digital (A/D) converters, and Arduino Nano 33 IoT microprocessors for data submission to a server, which allows real-time visualisation and data processing on a Python Flask server. The results demonstrate the applicability of the presented methodology and its implementation in the Python tool for achieving high-accuracy shape sensing with WSNs. Full article

Structural Health Monitoring (SHM) in aerospace engineering is more and more based on the use of Artificial Intelligence. In this manuscript machine learning algorithms were trained to identify and to characterize the structural effects of impacts on a typical aerospace aluminum panel. A significant experimental campaign was conducted to create suitable impact datasets (the vibrational behavior of the reinforced plate, acquired by piezo sensors). Shallow neural networks, properly trained, were applied to determine critical events affecting the operational conditions. The focus of the manuscript was double: on the severity of the event (a regression problem regarding impact energy) and on the detection of preexisting damage to monitored areas (a classification problem regarding the identification of damaged zones). The scope of this work was to demonstrate the validity of the machine learning approach as an SHM tool for impact effect characterization in a realistic aerospace structure (i.e., energy prediction with a percentage error never more than 10% and identification of previous damaged zones with an accuracy of more than 95%) and to demonstrate its computational efficiency despite the test complexity, provided that the selection of features is guided by a meaningful physical and mechanical interpretation of the underlying phenomena. Full article

This study explores the use of embedded piezo sensor (EPS) employing the Electro-Mechanical Impedance (EMI) technique for real-time corrosion monitoring in reinforced E-waste concrete exposed to chloride-laden environments. With the growing environmental concerns over electronic waste (E-waste) and the demand for sustainable construction practices, printed circuit board (PCB) materials were incorporated as partial replacements for coarse aggregates in concrete. The experiment utilized M30-grade concrete mixes, substituting 15% of natural coarse aggregates with E-waste, aiming to assess both sustainability and structural performance without compromising durability. EPS configured with Lead Zirconate Titanate (PZT) patches were embedded into both conventional and E-waste concrete specimens. The EPS monitored the changes in the form of conductance and susceptance signatures across a 100–400 kHz frequency range during accelerated corrosion exposure over a 60-day period in a 3.5% NaCl solution. The corrosion progression was evaluated qualitatively through electrical impedance signatures, visually via rust formation and cracking, and quantitatively using the Root Mean Square Deviation (RMSD) of EMI signatures. The results showed that the EMI technique effectively captured the initiation and propagation stages of corrosion. E-waste concrete exhibited earlier and more severe signs of corrosion compared to conventional concrete, indicated by faster increases and subsequent declines in conductance and susceptance and higher RMSD values during the initiation phase. The EMI-based system demonstrated its capability to detect microstructural changes at early stages, making it a promising method for Structural Health Monitoring (SHM) of sustainable concretes. The study concludes that while the use of E-waste in concrete contributes positively to sustainability, it may compromise long-term durability in aggressive environments. However, the integration of EPS and EMI offers a reliable, non-destructive, and sensitive technique for real-time corrosion monitoring, supporting preventive maintenance and improved infrastructure longevity. Full article