Search Results (1,197)

(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

Structural health monitoring (SHM) plays a critical role in ensuring the safety and performance of aerospace structures throughout their lifecycle. As aircraft and spacecraft systems grow in complexity, the integration of machine learning (ML) into SHM frameworks is revolutionizing how damage is detected, localized, and predicted. This review presents a comprehensive examination of recent advances in ML-based SHM methods tailored to aerospace applications. It covers supervised, unsupervised, deep, and hybrid learning techniques, highlighting their capabilities in processing high-dimensional sensor data, managing uncertainty, and enabling real-time diagnostics. Particular focus is given to the challenges of data scarcity, operational variability, and interpretability in safety-critical environments. The review also explores emerging directions such as digital twins, transfer learning, and federated learning. By mapping current strengths and limitations, this paper provides a roadmap for future research and outlines the key enablers needed to bring ML-based SHM from laboratory development to widespread aerospace deployment. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

The preservation of bridge infrastructure has become increasingly critical as aging assets face accelerated deterioration due to climate change, environmental loading, and operational stressors. This issue is particularly pronounced in regions with limited maintenance budgets, where delayed interventions compound structural vulnerabilities. Although traditional bridge inspections generate detailed condition ratings, these are often viewed as isolated snapshots rather than part of a continuous structural health timeline, limiting their predictive value. To overcome this, recent studies have employed various Artificial Intelligence (AI) models. However, these models are often restricted by fixed input sizes and specific report formats, making them less adaptable to the variability of real-world data. Thus, this study introduces a Transformer architecture inspired by Natural Language Processing (NLP), treating condition ratings, and other features as tokens within temporally ordered inspection “sentences” spanning 1993–2024. Due to the self-attention mechanism, the model effectively captures long-range dependencies in patterns, enhancing forecasting accuracy. Empirical results demonstrate 96.88% accuracy for short-term prediction and 86.97% across seven years, surpassing the performance of comparable time-series models such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs). Ultimately, this approach enables a data-driven paradigm for structural health monitoring, enabling bridges to “speak” through inspection data and empowering engineers to “listen” with enhanced precision. 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

Thermal imbalance can cause significant stress in large-scale structures such as bridges and buildings, negatively impacting their structural health. To assist in the structural health monitoring systems that analyze these thermal effects, a flexible temperature sensor was fabricated using EHD inkjet printing. However, the reliability of such printed sensors is challenged by complex dynamic hysteresis under rapid thermal changes. To address this, an LSTM calibration model was developed and trained exclusively on quasi-static data across the 20–70 °C temperature range, where it achieved a low prediction error, a 33.563% improvement over a conventional polynomial regression. More importantly, when tested on unseen dynamic data, this statically trained model demonstrated superior generalization, reducing the RMSE from 12.451 °C for the polynomial model to 4.899 °C. These results suggest that data-driven approaches like LSTM can be a highly effective solution for ensuring the reliability of flexible sensors in real-world SHM applications. Full article

This paper presents a compact dynamic optical frequency domain reflectometry (D-OFDR) platform enabling millimeter-scale, distributed strain sensing for real-time structural health monitoring (SHM) of reinforced concrete subjected to seismic loading. The proposed D-OFDR interrogator employs a dual-interferometer architecture: a main interferometer for strain sensing and an auxiliary interferometer for nonlinear frequency sweep compensation. Both signals are detected by photodetectors and digitized via a dual-channel FPGA-based DAQ board, enabling high-speed embedded signal processing. A dual-edge triggering scheme exploits both the up-chirp and down-chirp of a 50 Hz bidirectional sweep to achieve a 100 Hz interrogation rate without increasing the sweep speed. Laboratory validation tests on stainless steel cantilever beams showed sub-hertz frequency fidelity (an error of 0.09 Hz) relative to conventional strain gauges. Shake-table tests on a 2 m RC column under incremental seismic excitations (scaled 10–130%, peak acceleration 0.864 g) revealed distinct damage regimes. Distributed strain data and frequency-domain analysis revealed a clear frequency reduction from approximately 3.82 Hz to 1.48 Hz, signifying progressive stiffness degradation and structural yielding prior to visible cracking. These findings demonstrate that the bidirectional sweep-triggered D-OFDR method offers enhanced real-time monitoring capabilities, substantially outperforming traditional point sensors in the early and precise detection of seismic-induced structural damage. Full article

With the continuous development of science and technology, aircraft structural health monitoring (SHM) has become increasingly important in the aviation field. As a key component of SHM, wing deformation monitoring is of great significance for ensuring flight safety and reducing maintenance costs. The traditional strain gauge measurement method can no longer meet the needs of modern aeronautical engineering. Fiber Bragg grating (FBG) sensors have been widely used in the engineering field due to their unique advantages, and have shown great potential in aircraft wing deformation monitoring. In the context of SHM in the aircraft field, this article provides an overview of four aspects: classification and principles of fiber optic sensors, packaging forms of FBG sensors, bonding technology, and calibration technology. The packaging forms includes tube-packaged, embedded package and surface-attached package. It then discuss the bonding technology of FBG sensors, and the principle and influencing factors of fiber optic bonding technology are analyzed. Finally, it conducts in-depth research on the calibration technology of FBG sensors. Through comprehensive analysis of these four aspects, the suggestions for optical fiber sensing technology in aircraft wing deformation measurement are summarized and put forward. 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

Deep learning has revolutionized structural health monitoring (SHM), yet data scarcity remains a critical bottleneck limiting its deployment in real-world engineering applications. Meta-learning—an emerging paradigm enabling models to learn from limited examples—offers a compelling solution to this challenge. Herein, we systematically investigate meta-learning’s efficacy across three key SHM applications: surface damage detection, structural response prediction, and data-driven damage identification. Our experiments demonstrate that meta-learning achieves comparable performance with substantially reduced data requirements. For surface damage detection, meta-learning maintains detection accuracy while modestly decreasing sample dependency. In response prediction tasks, although the number of prediction errors increases marginally, the data efficiency gains are substantial. Similarly, damage identification shows slight accuracy trade-offs but dramatic reductions in required training samples. These findings establish meta-learning as a practical pathway for deploying deep learning in data-constrained SHM scenarios, potentially accelerating the adoption of intelligent monitoring systems in critical infrastructure. Our results suggest that the traditional data-hungry nature of deep learning need not be a barrier to advancing automated structural health assessment. Full article

Against the backdrop of increasing demands for the safety and longevity of the bridge infrastructure, this review synthesizes the recent advances in structural health monitoring (SHM) sensing systems. Carbon nanotube (CNT), piezoelectric, RFID, wireless, fiber optic, and computer-vision-based sensing are thoroughly explored and elucidated in the existing literature survey that distills their working principles, documented deployments, and anticipated research directions. CNT sensors detect minute resistance variations for strain and crack surveillance; piezoelectric devices transduce mechanical stimuli into high-resolution electrical signals; RFID tags combine location tracking with modular sensing and wireless data relay; and wireless sensing technology integrates sensor nodes with microprocessors and communication modules, which can facilitate efficient data processing and autonomous management. Fiber optic sensing technology, known for precision and interference resistance, is ideal for high-precision monitoring under strong electromagnetic interference conditions, and vision-based systems emulate human perception to extract geometric descriptors via image analytics. The comparative analysis reveals complementary strengths that guide practitioners in selecting optimal sensor suites for specific bridge conditions. The findings underscore the transformative role of these technologies in enhancing SHM reliability and suggest that synergistic integration with robotics and emerging materials will further advance future resilient monitoring frameworks. Full article

Fiber-reinforced composites (FRCs) have emerged as transformative alternatives to traditional marine construction materials, owing to their superior corrosion resistance, design flexibility, and strength-to-weight ratio. This review comprehensively examines the current state of FRC technologies in marine deck and underwater applications, with a focus on manufacturing methods, durability challenges, and future innovations. Thermoset polymer composites, particularly those with epoxy and vinyl ester matrices, continue to dominate marine applications due to their mechanical robustness and processing maturity. In contrast, thermoplastic composites such as Polyether Ether Ketone (PEEK) and Polyether Ketone Ketone (PEKK) offer advantages in recyclability and hydrothermal performance but are hindered by higher processing costs. The review evaluates the performance of various fiber types, including glass, carbon, basalt, and aramid, highlighting the trade-offs between cost, mechanical properties, and environmental resistance. Manufacturing processes such as vacuum-assisted resin transfer molding (VARTM) and automated fiber placement (AFP) enable efficient production but face limitations in scalability and in-field repair. Key durability concerns include seawater-induced degradation, moisture absorption, interfacial debonding, galvanic corrosion in FRP–metal hybrids, and biofouling. The paper also explores emerging strategies such as self-healing polymers, nano-enhanced coatings, and hybrid fiber architectures that aim to improve long-term reliability. Finally, it outlines future research directions, including the development of smart composites with embedded structural health monitoring (SHM), bio-based resin systems, and standardized certification protocols to support broader industry adoption. This review aims to guide ongoing research and development efforts toward more sustainable, high-performance marine composite systems. Full article

Achieving the full potential of wind energy in the global renewable transition depends critically on enhancing the performance and reliability of polymer composite components. This review synthesizes recent advances from 2022 to 2025, including the development of next-generation hybrid composites and the application of high-fidelity computational methods—finite element analysis (FEA), computational fluid dynamics (CFD), and fluid–structure interaction (FSI)—to optimize structural integrity and aerodynamic performance. It also explores the transformative role of artificial intelligence (AI) in structural health monitoring (SHM) and the integration of Internet of Things (IoT) systems, which are becoming essential for predictive maintenance and lifecycle management. Special focus is given to harsh offshore environments, where polymer composites must withstand extreme wind and wave conditions. This review further addresses the growing importance of circular economy strategies for managing end-of-life composite blades. While innovations such as the geometric redesign of floating platforms and the aerodynamic refinement of blade components have yielded substantial gains—achieving up to a 30% mass reduction in PLA prototypes—more conservative optimizations of internal geometry configurations in GFRP blades provide only around 7% mass reduction. Nevertheless, persistent challenges related to polymer composite degradation and fatigue under severe weather conditions are driving the adoption of real-time hybrid predictive models. A bibliometric analysis of over 1000 publications confirms more than 25 percent annual growth in research across these interconnected areas. This review serves as a comprehensive reference for engineers and researchers, identifying three strategic frontiers that will shape the future of wind turbine blade technology: advanced composite materials, integrated computational modeling, and scalable recycling solutions. Full article

This paper presents a detailed analysis of the severe structural anomalies that led to the urgent rehabilitation of the Eduardo Caldeira Bridge in Machico, Madeira. Situated in a challenging coastal environment with complex volcanic geology, the bridge exhibited a critical failure of its bearing devices, which were assigned the highest defect severity rating (Grade 5). A multidisciplinary diagnostic methodology, combining visual inspection data, non-destructive testing, and geotechnical analysis, was employed to identify the root causes of this degradation. The investigation concluded that the bearing failure was not due to widespread material deterioration but was directly linked to significant lateral structural displacements, exacerbated by localized geotechnical instabilities. This paper details the data-driven rehabilitation strategy that was subsequently implemented, including the complete replacement of the bearings and substructure stabilization measures. The study provides a valuable case study of a complex, mechanics-driven failure mode and demonstrates that for such critical infrastructure, a proactive management model integrating advanced technologies like Structural Health Monitoring (SHM) and Building Information Modelling (BIM) is essential for ensuring long-term safety and resilience. Full article