Engineering

Abstract: Fast synthetic aperture radar (SAR) imaging simulation is required by many computer vision applications. Although the shooting and bouncing ray (SBR) method has significantly accelerated electric field calculation, the number of ray tubes is still the bottleneck for SAR image simulation speed. This letter proposes an innovative adaptive SBR method driven by Q-learning for accelerated SAR imaging simulation. The core strategy is to convert the ray tube allocation into a reinforcement learning problem. The ray-shooting plane is dynamically partitioned into localized patches, where a Q-learning agent intelligently scales the ray density in real-time. By observing the geometric features of the target surface, the agent learns to employ coarser ray tubes in flat regions to eliminate redundant computation, while deploying denser ray tubes in complex areas. A multi-objective reward function is designed to balance accuracy against computational resource consumption. Numerical experiments demonstrate that the proposed Q-learning-based SBR method drastically reduces computational cost while preserving imaging similarity.

Abstract: Metallic wing covers are defined here as wing skins plus mechanically attached or integrally machined stringers; an integrally stiffened panel is the monolithic case in which the skin and stiffeners are machined from one plate. This structured narrative review examines upper and lower metallic wing covers as manufacturing objects in civil transport, business-aircraft, and selected military fixed-wing programmes. Aircraft-level evidence is concentrated from 1950 onward, with earlier peen-forming origins included only where they explain later industrial adoption. The evidence base combines peer-reviewed papers, SAE Technical Papers, patents, trade literature, government reports and supplier disclosures, so the review uses explicit source weighting rather than statistical aggregation. Evidence is graded by source strength, and patents are treated as capability evidence rather than proof of production use unless independently corroborated. The synthesis shows that route selection is governed by structural scale, cover role, curvature class, alloy and temper, inherited stock state, panel architecture, and compensation or validation capability. The upper/lower cover divergence that emerges is directly evidenced for selected Airbus, Gulfstream and B-1B cases and is treated, for Boeing and other lineages where cover-separated routes are not publicly disclosed, as a mechanistically supported inference rather than a demonstrated production rule. The strongest public evidence supports peen and particle-impact routes for selected directional or inflected lower-cover cases, Creep Age Forming (CAF) for large smooth heat-treatable covers, and specialised laser or hybrid routes where corroborated by supplier, patent, or named-programme evidence. Modern, non-Western, business-jet, and many military programmes are frequently supported only by patent, supplier, lineage, or contextual evidence and are therefore interpreted cautiously.

Abstract: Acquiring real-time biological data is essential for effective control of microalgae cultivation processes, yet routine monitoring still depended on laborious offline analyses that relied on time-consuming wet-chemical techniques. This study introduces a flow-through, multi-wavelength visible-light (VIS) sensor for real-time monitoring of biomass and pigment concentrations in microalgae cultivation processes. Based on 209 experimental data points, six machine-learning regression models were developed to estimate dry biomass, chlorophyll α, chlorophyll β, total chlorophyll, total carotenoids, and astaxanthin concentrations. Validation under realistic continuous operation with an independent dataset demonstrated that biomass and astaxanthin predictions were within ± 10% of offline reference measurements. The proposed low-cost and versatile multi-wavelength platform, together with machine-learning-based calibration, provides a practical soft-sensor concept for real-time monitoring of microalgal bioprocesses and offers a foundation for future integration of model-based and predictive control strategies.

Abstract: This study examines the technical feasibility of using phase-change materials (PCMs) to provide overnight heat to individuals sleeping outdoors or in tents. Thermal batteries made with PCMs absorb heat at a certain time and place and change phase at a defined temperature, usually from a solid to a liquid or melt. At a later time or different place where the surrounding temperature is below the transition temperature, the phase change reverses and the material releases heat. There is ample wasted heat in our society from residential, commercial, and municipal facilities, data centers, industrial processes, and internal combustion vehicles. Solar radiation could also be used to warm PCM mats during the daytime. We conducted scaled-down tests with a paraffin wax PCM, a standard sleeping bag, and a 3-probe digital thermometer. We heated a PCM with a transition temperature of 72 °F with ~160 °F water to approximate warmed process cooling water, and with a heat lamp to mimic solar heating. The pad, placed in a standard sleeping bag outdoors at an ambient temperature of ~50 °F, maintained the bag’s internal air temperature at ~70 °F or >15 °F above ambient for well over 12 hours. Scaling calculations reveal that each full-size pad would only cost about $3 USD. This concept thus appears to have excellent technical and economic viability.

Abstract: Indoor companion, rehabilitation, logistics, laboratory transport, and service robot scenarios require mobile platforms that can follow a human operator safely and flexibly under lighting changes, occlusion, texture-poor corridors, and dynamic pedestrian environments. Vision-, LiDAR-, and UWB-based following systems can provide high perception capability, but their deployment cost, environmental dependence, and sensing complexity remain limiting factors for low-perception-dependence applications. This paper presents a passive following system for a Mecanum-wheel mobile platform based on gimbal posture perception and orthogonal odometry fusion. A rope-tensioned two-axis gimbal is mounted above a 300 mm x 300 mm x 150 mm omnidirectional chassis, and a six-axis inertial sensor installed at the top of the gimbal detects pitch and roll changes induced by user traction. A piecewise posture-to-velocity mapping model with a dead zone, saturation, low-pass filtering, and acceleration limiting converts the user's traction intention into planar velocity commands in the vehicle coordinate frame. To reduce pose errors caused by Mecanum-wheel slip and discontinuous roller-ground contact, two orthogonal passive odometry wheels and inertial attitude estimation are fused to provide planar position feedback for closed-loop following. A prototype was implemented using an Infineon TRAVEO CYT4BB77 controller, TI DRV8701E motor drivers, six-axis IMUs, magnetic encoders, and an embedded display interface. Experiments evaluated attitude estimation accuracy, planar localization accuracy, passive following performance, gyroscope compensation, and open-loop/closed-loop following. The compensated attitude module achieved a static yaw drift of 0.45 deg/h and dynamic attitude RMSE below 0.56 deg. Orthogonal odometry fusion produced an average positioning error of 3.8 mm over a 3000 mm linear displacement, reducing error by approximately 84.6% compared with pure Mecanum-wheel drive odometry. In a 5000 mm forward traction task, closed-loop following reduced the average distance error from 38.6 mm to 11.5 mm compared with open-loop attitude mapping. The results indicate that the proposed gimbal-orthogonal-odometry architecture provides a compact, intuitive, and environment-robust solution for passive following on omnidirectional mobile platforms.

Abstract: Safe and efficient operation of large-scale steam turbines is too significant for grid stability, yet frequent transient start-ups cause severe rotor displacements/vibrations and thermal bowing, delaying grid connection. Operators face this by enforcing conservative "heat-soak" pauses, increasing substantial financial costs through wasted fuel and missed dispatch windows. One way to predict the optimal duration of the “heat-soak” pauses is by using data-driven Long Short-Term Memory (LSTM) networks, which however operate as unsafe low-pass filters and "black boxes" ignorant of mechanical realities. This study proposes a novel "Gray-Box" Physics-Informed Machine Learning (PIML) framework, combining kinematic gradient regularization, asymmetric risk penalties, and thermodynamic boundary conditions directly into the LSTM's objective function. Using a low-frequency (0.2 Hz) industrial SCADA dataset from generator journal bearings, the optimized architecture maps multivariate rotor dynamics, reducing predictive error to an exceptional median Mean Absolute Percentage Error (MAPE) of 6.09% and Normalized Mean Absolute Error (NMAE) of 4.72%. Crucially, the framework operates as an autonomous actuator, dynamically evaluating thermal memory to safely compress start-up timelines. It eliminates unnecessary runtime while mandating extensions during critical excursions, guaranteeing physical integrity. Finally, an economic layer quantifies optimized time differentials into financial returns within the European market, delivering a reliable decision-support system.

Abstract: Bicycling is an important mode of sustainable and active transportation, but bicycle safety remains a major concern in urban areas, especially at intersections where cyclists interact with turning vehicles, crossing traffic, and complex roadway conditions. This study assesses the effect of roadway, environmental, and infrastructure-related factors on bicycle safety at intersections in Baltimore City. Crash data from 2022 to 2024 were obtained from the Maryland crash data records and analyzed for bicycle-involved crashes at intersections. The study used descriptive statistics, GIS-based spatial analysis, visualizations, and exploratory regression models, including linear regression, binary logistic regression, and multinomial logistic regression. The results showed that Baltimore City had 180 bicycle-involved crashes at intersections during the study period, most of which resulted in injury. Spatial analysis indicated that crashes were concentrated mainly in downtown Baltimore. Descriptive results showed that many crashes occurred during daylight, clear weather, and dry surface conditions, which may reflect higher bicycle activity during these periods. The Sankey diagram suggested that severe crash outcomes were more common in locations without bike lanes. However, the regression models did not identify statistically significant relationships between the selected variables and crash severity. The findings highlight the need for better bicycle exposure data, more complete infrastructure variables, and improved intersection-level safety planning in Baltimore City.

Abstract: Neodymium (Nd) and dysprosium (Dy) are two critical rare earth elements for fabricating NdFeB permanent magnets, which have crucial applications in modern technologies. The increasing global demand for Nd and Dy emphasizes new efficient processes for their recovery and purification, which are technologically challenging due to their close physical and chemical properties. Through systematic exploration, it was found that Lewatit VP OC 1026 resin impregnated with di-(2-ethylhexyl) phosphoric acid (D2EHPA) had a strong adsorption preference for Dy³⁺ over Nd³⁺, which is highly suitable for Dy-Nd separation from their mixed solutions under optimized conditions. The loaded resin could be eluted using dilute sulfuric solutions for recycling to the adsorption process. By employing a multistage adsorption-elution process analogous to distillation, efficient Dy-Nd separation and purification were realized from their mixed solution, with a prospective purity of 99.13% and recovery of 97.45% for Dy and a prospective purity over 99.96% and recovery of above 99.90% for Nd, despite the large concentration disparity between Dy and Nd where Nd concentration is over 26 times of that of Dy. This research demonstrates that efficient recovery and purification of metals from aqueous solutions can be achieved using selective resin adsorption processes analogous to distillation, despite large concentration differences of the metals in the solutions, which presents new alternative approaches.

Abstract: The loess gully region is characterized by complex terrain with crisscrossing gullies,where coal mining can readily induce surface subsidence and slope deformation. Such deformation often leads to geological hazards and ecological issues,including collapses,landslides, soil erosion, vegetation dry up,and land degradation.Therefore,understanding the deformation behavior of mining‑induced slopes is essential for the restoration and management of mine geological environments.This study focuses on five slopes within working faces 50205 and 50206 of the Zhen’er Coal Mine in Fugu County.Using a combination of 3DEC numerical simulations and orthophoto-based fracture identification, we systematically investigated mining-induced slope deformation under the complex topographic conditions of the loess gully region.The goal is to answer three key questions: where mining-induced slope deformation primarily occurs,how it evolves over time, and what the main controlling factors are.Spatially,the primary deformation zones and their propagation paths vary significantly among the five slopes.The largest deformation occurs in the slope body directly above the main section of the working face,gradually decreasing toward the edges of the working face. Temporally, mining-induced slope deformation exhibits a time lag, meaning that surface responses lag behind underground mining activities and continue to develop even after the working face is fully extracted.In the loess gully region, slope deformation induced by mining is controlled not only by mining activities but also by topographic factors such as slope shape, aspect,gradient, and height. The spatiotemporal evolution of deformation becomes even more complex for slopes that span multiple working faces. These findings provide a scientific basis for monitoring mining-induced slope deformation and preventing geological disasters in the loess gully region,while also offering practical guidance for safe mining operations and hazard control in similar settings.

Abstract: Abstract In this study, coevolution approach rather than evolution is considered to analyse how enabling technologies influence mechatronics progress, advancements and innova-tions. Attention of this work is given to reveal the mutual interaction between mecha-tronics technology and its enabling technologies, since mechatronics methodologies, engineering tools and applications support their advancements, along their coevolu-tion with mechatronics. With their coevolution mechatronics technology reach new maturity levels to fulfil the demand of many industrial domains and other economic sectors for new advanced innovative equipment. For systematic reasons, the impact of each enabling technology on the evolution of mechatronics is investigated and the support of mechatronics to the advancement of the considered enabling technology is examined using carefully selected publications after an exhaustive and focused search. The coevolution of mechatronics is considered through the progress and synergy of its enabling technologies in a reciprocal mode. The investigated and demonstrated co-evolution of mechatronics with its enabling technologies is expected to contribute to identifying the future challenges of mechatronics that are briefly presented in the sec-tion of discussion. The paper concludes with hints for future research and develop-ment work under the proposed coevolution conceptualization and investigation.

Abstract: Photonic crystal fiber (PCF) sensors based on surface plasmon resonance (SPR) have matured into a versatile platform, yet the literature remains fragmented: biomedical and power system studies are reviewed in isolation, and most surveys catalog individual designs without explaining why particular structural choices deliver particular performance. This review departs from that catalog style by introducing a unifying, dual-domain analytical framework that treats power-system monitoring and biomedical diagnostics as two ends of a shared refractive-index sensing continuum governed by the same small set of design levers. We organize recent (2023–2026) PCF-SPR research around five such levers geometry exposure, plasmonic-material engineering, two dimensional (2D) functional overlayers, multi core/multi parameter architectures, and data-driven inverse design and we critically benchmark reported sensitivities, resolutions, and trade-offs rather than merely listing them. Particular emphasis is placed on three currently underexplored frontiers: hybrid plasmonic stacks combining gold with graphene, MXene (Ti₃C₂Tₓ), and black phosphorus; machine learning and explainable AI workflows that replace exhaustive finite element sweeps; and multi peak ten hole architectures that enable simultaneous voltage, temperature, magnetic-field, humidity, and refractive-index sensing for smart grid diagnostics. By mapping performance gains against fabrication cost and identifying concrete research gaps, this review offers a decision-oriented guide for designing the next generation of field-deployable, lab on fiber PCF SPR sensors.

Abstract: Organic matter is widely recognized as a key factor limiting the effectiveness of conventional cement stabilization of soft soils, thereby affecting the performance of reinforced soil layers used in construction. However, the influence of different organic matter components on reactive MgO carbonation reinforcement remains insufficiently understood. In this study, silty clay containing varying contents (0~8%) of fulvic acid (FA) and HA (HA) was treated using Portland cement (PC) stabilization, MgO stabilization, and MgO carbonation. The engineering performance and microstructural characteristics of the reinforced soils were evaluated through measurements of dry density, unconfined compressive strength (UCS), pH, X-ray diffraction (XRD), scanning electron microscopy (SEM), and pore structure analysis. The results indicate that MgO carbonation exhibited the highest densification efficiency among the three curing methods, resulting in significantly higher dry density and UCS values than those obtained under PC curing and MgO curing. Increasing organic matter content generally reduced the alkalinity of the stabilized soils and adversely affected strength development. The inhibitory effect of FA on stabilization performance was more pronounced than that of HA, as evidenced by a lower minimum UCS and greater sensitivity to organic matter dosage. Carbonation treatment effectively mitigated the negative influence of both FA and HA, producing substantially higher strengths than the other curing methods. XRD analysis revealed that nesquehonite, dypingite, and hydromagnesite were the major carbonation products, while increasing organic matter content reduced the formation of these strength-contributing phases and promoted the retention of uncarbonated brucite. SEM observations further confirmed that organic matter altered the morphology and distribution of carbonate products, resulting in a looser microstructure despite a reduction in pore volume. Overall, the findings demonstrate that reactive MgO carbonation stabilization possesses stronger resistance to organic matter interference than conventional PC stabilization and can effectively improve the engineering performance of organic-rich soft soils while facilitating CO₂ sequestration. This study provides experimental evidence and design-relevant insights for the optimal application of low-carbon MgO-CO₂ stabilization technology in reinforced soil construction.

Abstract: Urban accessibility is increasingly shaped by the interaction between physical mobility, digital service accessibility, and social relationships. However, most existing urban simulation models primarily focus on physical transportation networks and rarely incorporate digital accessibility or social interaction mechanisms. This limitation restricts the ability of conventional models to capture emerging behavioral patterns associated with digital service adoption and changing urban lifestyles. To address this gap, this study develops a multi-layer Social Dynamics Simulation (SDS) model that integrates three interdependent network layers: a real network representing physical accessibility, a virtual network representing digital accessibility, and a social network representing interpersonal relationships. The model introduces an integrated accessibility index that combines physical and digital accessibility based on a probabilistic service choice framework estimated using survey data (n = 6,210). The proposed model is applied to a virtual city experiment to examine how digital service usage and social interaction preferences influence long-term urban dynamics. Simulation results indicate that digital accessibility partially relaxes spatial constraints imposed by transportation networks, enabling households to maintain acceptable service access even in locations with lower physical accessibility. However, transportation accessibility remains a dominant factor shaping residential concentration around transit nodes. The results further demonstrate that digital service substitution can reduce travel demand while reinforcing accessibility differences across population groups. The proposed framework contributes to computational urban systems modeling by incorporating digital service substitution and social interaction effects into a multi-layer simulation environment. The results highlight the importance of representing non-physical accessibility processes when evaluating urban dynamics in increasingly digitalized cities.

Abstract: Data-constrained island power systems with increasing shares of variable renewable energy (VRE) face growing challenges in maintaining reliability while preserving market efficiency. Existing reserve sizing practices typically rely on either fixed deterministic rules or data-intensive probabilistic methods, both presenting practical limitations in Small Island Developing States (SIDS). This paper develops a market-based framework for the co-optimization of energy and operating reserves in low-inertia island power systems, in which reserve requirements are determined from historically observed extreme generation or load deviations that represent operationally validated high-risk system conditions, while reserve allocation and pricing emerge from the co-optimization process. By relying on observed operational variability, the proposed approach avoids explicit probabilistic uncertainty modeling while retaining sensitivity to system stress conditions. The approach is evaluated using a stylized island power system representative of Caribbean SIDS. Results show that reserve requirements are highly sensitive to operating conditions, reaching up to 26.7% of demand under high variability and significantly exceeding conventional fixed reserve criteria. The framework reduces non-served energy, improves reserve allocation efficiency, and generates scarcity-consistent reserve prices under stressed conditions. These findings demonstrate that the proposed methodology provides a practical intermediate solution between deterministic and probabilistic reserve sizing approaches while remaining suitable for data-constrained island power systems.

Abstract: The leaching of chalcopyrite in sulfate or chloride media has been proposed to occur through a combined non-oxidative/oxidative mechanism, where H₂S forms an intermediary species, concurrently with direct oxidative leaching. Some studies have noted that elevated concentrations of Cu(II) improve chalcopyrite leaching in sulfate media. In this study, the role of Cu(II) was investigated in the non-oxidative/oxidative process through electrochemical tests on a chalcopyrite electrode, supported by bulk leach tests. The results of the electrochemical tests are consistent with the formation of H₂S through non-oxidative leaching of chalcopyrite. The H₂S subsequently reacts with Cu(II) to form intermediate cuprous sulfide species, which can be readily oxidized by dissolved oxygen. The bulk leach tests point to a synergy between Cu(II) and O₂, further confirming the catalytic role of Cu(II). The findings support the feasibility of running heap leaching of chalcopyrite-rich ores at elevated copper concentrations in either chloride or sulfate systems.

Abstract: Fiber-reinforced polysiloxane composites (FRPCs) with glass, silica, carbon, graphite, car-bon/polybenzimidazole, alumina, and quartz fibers in different architectures, infiltrated with a high char yield polysiloxane resin, were developed for hypersonic applications. Techneglas manufactures the polysiloxane resin. FRPCs were manufactured by the Koo Research Group. Material characterization of thermal stability, flammability, ablation, thermophysical, and mechanical properties of these high-performance FRPCs was per-formed. Thermal stability properties were characterized using thermogravimetric analysis, and flammability properties using microscale combustion calorimetry for the FRPCs. Ablation properties using oxy-acetylene test bed with advanced diagnostics were performed at sev-eral heat flux test conditions, exposure times for recession rate, mass loss rate, front surface temperature, and back-face heat-soaked temperature to compare material performance. The microstructures of these FRPCs before and after ablation and mechanical testing were in-vestigated using scanning electron microscopy and micro-computed tomography. Thermophysical properties of the virgin and char FRPC were measured at elevated tem-peratures. Using these material properties and surface thermochemistry analysis, material response modeling was performed and validated with aerothermal test data. Mechanical properties, such as tensile, compression, and flexural were conducted via ASTM stand-ards. The above material properties of these FRPCs compared favorably with several commercial ablatives under similar extreme aerothermal environments.

Abstract: Metallic powder systems containing niobium play a key role in the development of advanced materials for structural, biomedical, energy, and surface-engineering applications. The incorporation of niobium into metallic powders influences particle behavior during processing, phase stability, microstructural evolution, and the resulting mechanical and corrosion properties of consolidated materials. This review examines the scientific and technological advances related to niobium-containing metallic powders, covering powder production routes, particle characterization methods, processing techniques, and performance evaluation. Publications on powder metallurgy, additive manufacturing, thermal processing, surface modification, and corrosion-resistant materials were analyzed to identify relationships among powder characteristics, processing conditions, and material performance. The available evidence indicates that niobium contributes to grain refinement, precipitation control, microstructural stabilization, improved resistance to wear, and localized corrosion. The element also expands the applicability of metallic powders in functional coatings, biomaterials, engineered surfaces, and components manufactured from particulate feedstocks. Current challenges involve powder homogeneity, process reproducibility, economic considerations, and the prediction of long-term service behavior. The analysis highlights niobium's contribution to the design of high-performance metallic powder systems and identifies research directions for developing materials with enhanced reliability and industrial applicability.

Abstract: Standard H_∞ controller synthesis produces robust controllers with well-shaped sensitivity and complementary sensitivity transfer functions, S and T. However, at times H_∞ does not enforce strict requirements on sensitivity, in particular the desired requirement that T has unity gain at DC frequency. This results in typically negligible steady-state tracking error, as the H_∞ optimization produces T(0)≈1. In drive cycle applications where reference velocity profiles contain extended ramp segments, this negligible deviation is integrated over time into a growing, non-negligible bias. The conventional remedy is to augment the plant with an integrator prior to synthesis, but this increases the order of the plant model and can be inconvenient when the control designer’s modeling has already been completed. This paper presents a post-synthesis gain adjustment method using Youla parameterization that corrects the DC tracking deficiency without modifying the plant or repeating H_∞ synthesis. The poles and zeros corresponding to the H_∞ controller’s Youla transfer function Y are preserved, with a free parameter K replacing the gain of Y. Re-calculating the controller after solving for the value of K that enforces T(0)=1 results in a hybrid controller that retains the robustness of the original but with improved performance in ramp-input scenarios with minimal effort for the control designer. Simulation results on a vehicle speed tracking problem confirm elimination of accumulating bias while preserving robustness margins from the original H_∞ design.

Abstract: Maintaining the geometric quality of railway tracks is essential for ensuring the safety and efficiency of railway operations. This study presents a comparative analysis of Multiple Linear Regression (MLR) and Random Forest (RF) models for predicting the Track Quality Index (TQI), based on historical inspection data collected from the mountainous Kolašin–Podgorica railway section between 2017 and 2022, with data from 2024 reserved for independent validation. The dataset includes high-resolution measurements divided into 20-meter homogeneous units, incorporating infrastructure, geometric, operational, and maintenance-related variables. Both models were trained on scaled input features, and their performance was evaluated using the coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE). The results demonstrate that the machine learning approach significantly outperformed the statistical baseline; the RF model achieved a higher goodness-of-fit (R2 = 0.69 vs. 0.57) and reduced the average prediction error (MAE) by approximately 15% compared to the MLR model. Furthermore, RF exhibited superior stability in capturing severe localized degradation trends. These findings highlight the potential of ensemble machine learning methods to mitigate large prediction errors and enhance data-driven, proactive track maintenance planning in geometrically complex railway networks.

Abstract: Automated damage inspection of railway viaducts requires pixel-level identification of structural components and surface damage such as cracking and rebar exposure. A common assumption in bridge inspection is that damage segmentation improves when component information is provided alongside the image. This study tests that assumption on the Tokaido synthetic viaduct dataset using controlled comparisons between segmentation models with and without component information. Both damage and structural component segmentation are evaluated across multiple architectures, and the trained component model is assessed on real viaduct photographs against a baseline model requiring no task-specific training. Adding component information does not improve damage segmentation: all tested strategies remain within 0.008~mean Intersection-over-Union (mIoU) of a baseline without component input. This null result persists even when component predictions are reliable, indicating that structural element identity does not provide useful information for damage localisation in this setting. The best unconditioned model reaches 0.569~mIoU for damage segmentation; for real-photo component segmentation, the trained model reaches 0.424~mIoU compared with 0.250~mIoU for the training-free baseline. These results show that multi-task benefits reported in bridge inspection do not automatically translate into gains from explicit use of component information on synthetic viaduct data, where damage placement is largely independent of structural element type. The multi-architecture benchmark and the measured real-photo structural transfer gap provide reference baselines for subsequent work on component-aware and transfer-robust inspection.