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Reliability Evaluation and Optimization of System with Fractional-Order Damping and Negative Stiffness Device
No full textAbstract
Research on reliability control for enhancing power systems under random loads holds significant and undeniable importance in maintaining system stability, performance, and safety. The primary challenge lies in determining the reliability index while optimizing system parameters. To effectively address this challenge, we developed a novel intelligent algorithm and conducted an optimal reliability assessment for a Negative Stiffness Device (NSD) seismic isolation structure incorporating fractional-order damping. This algorithm combines the Gaussian Radial Basis Function Neural Network (GRBFNN) with the Particle Swarm Optimization (PSO) algorithm. It takes the reliability function with unknown parameters as the objective function, while using the Backward Kolmogorov (BK) equation, which governs the reliability function and is accompanied by boundary and initial conditions, as the constraint condition. During the operation of this algorithm, the neural network is employed to solve the BK equation, thereby deriving the fitness function in each iteration of the PSO algorithm. Then the PSO algorithm is utilized to obtain the optimal parameters. The unique advantage of this algorithm is its ability to simultaneously achieve the optimization of implicit objectives and the solution of time-dependent BK equations.To evaluate the performance of the proposed algorithm, this study compared it with the algorithm combines the GRBFNN with Genetic Algorithm (GA-GRBFNN)across multiple dimensions, including performance and operational efficiency. The effectiveness of the proposed algorithm has been validated through numerical comparisons and Monte Carlo simulations. The control strategy presented in this paper provides a solid theoretical foundation for improving the reliability performance of mechanical engineering systems and demonstrates significant potential for practical applications
Predictive reanalysis in structural dynamics
No full textPredictive reanalysis has emerged as a vital computational strategy in structural dynamics,
enabling efficient updates of structural response predictions following minor modifications in
geometry, material properties, or boundary conditions, without resorting to full re-computation.
Traditionally rooted in finite element methods, reanalysis techniques have evolved through the
integration of Artificial Intelligence (AI) models, offering unprecedented speed and adaptability
in dynamic system assessments. This paper provides a comprehensive overview of predictive
reanalysis approaches, with an emphasis on recent AI-assisted methodologies. The synergy
between data-driven models such as neural networks, decision trees and ensemble learning and
physics-based simulations enables more accurate prediction of structural behavior under varying
operational scenarios. The application of machine learning has demonstrated significant potential
in reducing computational costs, increasing adaptability and enhancing real-time monitoring
capabilities in engineering systems. A numerical case study is presented, involving a cantilever
beam discretized into five finite elements. The analysis explores how changes in cross-sectional
properties at various segments affect the first natural frequency. Predictive AI models are
employed to estimate frequency shifts and their performance is compared against classical
empirical formulas. The results validate the ability of trained AI models to generalize the
influence of structural variations and support decision-making in early design or maintenance
phases. The study also highlights current challenges in predictive reanalysis, including data
scarcity, model interpretability and integration with real-time monitoring systems. Future
directions are outlined, focusing on hybrid modeling techniques, improved data acquisition
strategies and the development of standardized benchmarks for AI-assisted structural reanalysis.
Ultimately, this work contributes to the growing body of research bridging computational
mechanics and machine intelligence, fostering more resilient, adaptive and efficient structural
systems.Project no. 451-03-137/2025-03/ 200105 from 04.02.202
Influence of Infill Pattern on Ballistic Resistance Capabilities of 3D-Printed Polymeric Structures
No full textRecent technological advances have expanded the use of 3D-printed polymer components across industries, including a growing interest in military applications. The effective defensive use of such materials depends on a thorough understanding of polymer properties, printing techniques, structural design, and influencing parameters. This paper analyzes the ballistic resistance of 3D-printed polymer structures against 9 × 19 mm projectiles. Cuboid targets with different infill patterns—cubic, grid, honeycomb, and gyroid—were fabricated and tested experimentally using live ammunition. Post-impact, CT scans were used to non-destructively measure projectile penetration depths. The honeycomb infill demonstrated superior bullet-stopping performance. Additionally, mechanical properties were experimentally determined and applied in FEM simulations, confirming the ability of commercial software to predict projectile–target interaction in complex geometries. A simplified analytical model also produced satisfactory agreement with experimental observations. The results contribute to a better understanding of impact behavior in 3D-printed polymer structures, supporting their potential application in defense systems
NiMn2O4 nano-cotton particles and nanofibers: Exploring structural, magnetic and electrochemical energy storage properties
No full textHerein, nickel manganite (NiMn2O4) was successfully synthesized via glycine-nitrate sol-gel combustion process to obtain nano-cotton particles (SG-800) and electrospinning method to obtain nanofibers (ES-400), both followed by calcination. Synthesized materials were characterized in order to evaluate structural, magnetic and energy storage properties. The X-ray diffraction (XRD) pattern revealed the formation of a cubic spinel structure in both cases. The crystallite size of SG-800 (53 nm) was higher than that of ES-400 (16 nm). X-ray photoelectron spectroscopy showed the presence of Ni2+, Mn2+, Mn3+ and Mn4+ valence states in both samples. Synthesized materials were paramagnetic at room temperature, turning to ferromagnetic ordering at the critical temperature of 104 and 95 K, while the appearance of the spin-glass-like state was observed at 65 and 80 K for SG-800 and ES-400, respectively. SG-800 and ES-400 were tested in different electrolytes on a glassy carbon electrode as a substrate and demonstrated potential for energy storage through diffusion-controlled, Faraday redox electrochemical reactions. Carbon aerogel (CA) produced by thermal carbonization of lyophilized sodium-alginate hydrogel exhibited EDLC capacitor-like behavior, as shown by electrochemical characterization. Hybrid supercapacitors were assembled from SG-800 or ES-400 and CA and their performance was evaluated. The SG-800 as an electrode material showed superior capacitance and stability, probably due to higher crystallinity and formation of active sites for electrochemical redox reactions.This is the peer-reviewed version of the article: Milena P. Dojcinović, Vladan Kusigerski, Ivana B. Stojković Simatović, Vera P. Pavlović, Janez Kovač, Matjaž Spreitzer, and Maria Vesna Nikolić, "NiMn2O4 Nano-Cotton Particles and Nanofibers: Exploring Structural, Magnetic and Electrochemical Energy Storage Properties" in Journal of Energy Storage, Volume 131, Part A (2025) [https://doi.org/10.1016/j.est.2025.117442
Quality Tools Application in Examining Discomfort Issues at Mining Machinery Operators’ Workplaces
No full textThe significance of evaluating human factors issues encountered at mining machinery
operation greatly exceeds the amount of available research, given that accidents
in mining operations continue to be a recurring concern. This study included
97 Serbian mining machinery operators, who answered the questionnaire which
examines injuries and discomfort issues at mining machinery operators’ workplaces.
Descriptive statistics was conducted followed by quality tools: Pareto (ABC), Ishikawa,
and control charts were performed. ABC analysis found that 41.7% of operators
complained to back pain, mostly due to poor working conditions. Back pain is
caused by repetitive movements, poor anthropometric adjustments, lack of training,
environmental factors, and vibrations, according to the Ishikawa diagram. The
attribute control chart shows that no points exceed the lower and upper limits. Thus,
examined processes are controlled. A future research avenue is further data collection
and expansion of the sample size, as well as the application of other quality tools.no. 451-03-65/2024-03/20010
Exploring the Role of Influential Scholars in Maritime and Port Logistics Systems
No full textAbstract: There is no annual ranking for top scientists in the area of maritime and port logistics systems (MPLSs), such as in the area of physics or top 10 most influential mathematicians. Therefore, the main aims of the present study are to offer the academic community more visibility of the influential research and highlight the scholars whose relevant bibliometric indexes are higher than average. A systematic, scientific, and fair approach based on very well-known bibliometric indexes is conducted to identify the most influential scholars. This provides possibilities for a rigorous comparative analysis, as well as assessment of scholars' scientific outputs. The internal database includes a total of 8,774 documents that were comprehensively analysed. All the obtained results are reproducible and verifiable. If this approach omitted some credible scholars, it should not be considered as a judgment of the merit of their scientific output
Lifetime Corrosion Loss of Bulk Carriers
No full textThis paper analyzes the total steel replacement due to corrosion degradation in four Handymax-class bulk carriers, based on corrosion measurements recorded throughout their operational lifespan. Each ship was divided into 11 lightship mass subgroups, enabling detailed examination of cumulative lifetime corrosion losses for both entire ships and individual subgroups. Utilizing similar ship data obtained from the shipyard, the study also provides estimations of the total steel weights of each of lightship subgroups. The findings offer valuable insights into the overall aging effects on ship structures, crucial for maintenance planning, structural integrity assessments, and recycling, especially from the perspective of sustainable shipping. Additionally, the estimated weights of lightship subgroups can serve as reference data for preliminary ship design, aiding in the estimation of lightship weights and potential steel loss due to corrosion
Development of Corrosion Wastage Assessment Methodology for Water Ballast Tanks: An Aging Bulk Carrier
No full textVarious environmental factors, operating conditions, transport routes, the type of transported cargo, maintenance
practices, and other external and internal factors significantly influence the development of corrosion. Cargo holds and water
ballast tanks are particularly susceptible to corrosion damage. This study investigates the extent to which steel thickness
reduction due to corrosion contributes to the degradation of steel structures and whether this reduction aligns with the
adopted Common Structural Rules (CSR). The analysis is based on an aging bulk carrier and three types of ballast tanks
within the cargo hold area: top-side tanks, hopper-side tanks, and double-bottom tanks. Thickness measurements were
conducted on nine specific transverse structural locations, and a corrosion wastage assessment methodology was
developed based on a nonlinear stochastic model. The corrosion growth rate was modeled using a probabilistic approach
where the corrosion rate parameter d0 follows a Weibull distribution. The model also incorporates 95% confidence intervals
to reflect uncertainty and assess early risk exceedance relative to CSR corrosion margins. The results revealed significant
differences in corrosion behavior among ballast tank areas and identified critical zones where corrosion thresholds are
reached earlier than expected. The proposed methodology demonstrates its applicability in assessing structural degradation
patterns and validating CSR-based corrosion allowances
A Note on the Fathers of Escalators: Ames, Souder, Reno, Wheeler, Seeberger
No full textEscalators (moving stairways) are machines which belong to the group of continuous conveying machines according to their principle of operation, but at the same time they are, together with elevators, classified as the machines for vertical transportation. In their historical evolution they have some points of contact, mostly because the manufactures of both equipment are the same; however, escalators differ substantially from elevators, which basic principles were formulated several centuries ago. The basic design employed has not varied from those patented more than a century ago. All relevant patents as a base for modern escalators have been developed in the second half of the 19th century. Individuals and engineers, who invented those first patents, are considered as fathers of escalators. In accordance with the sequence of inventions the key persons in the history of escalators are Nathan Ames, Leamon Souder, Jesse Reno, George Wheeler and Charles Seeberger. Brief notes from their life and work and their inventions are presented in chronological order.MSTDI of Serbia funded project “Integrated research in the fields of macro, micro and nano mechanical engineering”, contract number: 451-03-65/2024-03/200105, Faculty of Mechanical Engineering, University of Belgrade
EDGE-AI-ENABLED 2D DIGITAL IMAGE CORRELATION FOR AUTONOMOUS STRUCTURAL HEALTH MONITORING OF STEEL STRUCTURES AND PRESSURE EQUIPMENT
No full textContinuous assessment of in-service steel structures and pressure equipment has been constrained by the high-bandwidth, workstation-level computation traditionally required for digital image correlation (DIC). The large data volumes produced by conventional optical systems have therefore restricted full-field strain monitoring to controlled laboratory environments. To overcome this limitation, an embedded 2D DIC strategy is recommended in this paper. Displacement and strain fields are evaluated directly on a low-power graphics module positioned at the camera head, eliminating the need to stream raw imagery. A streamlined on-device AI model matches the image pixels in real time, while a second AI model instantly converts the resulting strain maps into early damage warnings and clear estimates of the structure’s remaining strength. Only condensed structural health indicators and alarm flags are transmitted, reducing data traffic by more than an order of magnitude. Validation under representative static and cyclic loading scenarios typical of welded joints, pressure vessels and pipework are advised, with particular attention to the rapid detection of critical strain localisations and the early prediction of crack-growth trends. Adoption of such an edge-AI DIC module is expected to deliver a deployable, low-latency pathway toward condition-based maintenance for steel infrastructure and pressure equipment operating under variable service conditions