Search Results (281)

This study presents a stochastic approach to assess bending moment demand in reinforced concrete beams subjected to vertical loads, incorporating uncertainties in material properties, geometry, and loading conditions. A Monte Carlo simulation framework was developed in Python version 3.9.3 using the OpenSeesPy library to analyze the variability of internal forces based on probabilistic input parameters. The analysis focuses on a four-span continuous beam representative of typical structural configurations in buildings. Probability distributions were assigned to key structural design parameters such as the unit weight of concrete ($\rho$), beam dimensions (b, h), column dimension (a), and applied loads, based on standard statistical assumptions and design guidelines. A total of 10,000 simulations were performed to obtain statistical descriptors of bending moment demand across the different spans. The results reveal significant variability in moment magnitudes, underscoring the importance of accounting for uncertainty in structural design. The proposed methodology enables the estimation of demand distributions and the identification of critical spans with higher sensitivity to parameter variations. Although the study does not evaluate structural capacity or failure probability, it contributes to the integration of stochastic techniques in the preliminary stages of design. Future work may include the incorporation of reliability 16 indices and comparisons with design code values. Full article

In traditional Chinese timber architecture, “column reduction” (Jian Zhu Zao) and “column relocation” (Yi Zhu Zao) enhances spatial continuity, yet often produces bending-dominated, material-intensive frames. This study develops a generative frame system that encodes raised beam logic into a parametric line-model workflow and couples it with simulation-based optimization. Informed by case analysis, the tool implements three lateral strategies—ridge-support revision, insertion of inclined members, and inclination of originally horizontal members—and one longitudinal strategy—longitudinal truss formation—whose use is governed by a user-defined historical authenticity parameter. Structural responses were evaluated using Karamba3D, and cross-section sizing was searched using Wallacei under gravity-dominant loading. The results indicate clearer load paths, greater axial-force participation, and reduced bending, yielding lower maximum displacements at comparable self-weight; moreover, the performance ranking aligns with the calibrated authenticity loss schedule, suggesting that the authenticity controller also acts as a practical proxy for expected stiffness gains. The framework improves design and modeling efficiency while offering quantitative decision support for culturally sensitive conservation and imitation design. Limitations include line-model idealization, simplified timber and joint behavior, gravity-only loading, and a modest historical corpus. The approach is extensible to other traditional systems via parameter and rule adaptation. Full article

As traffic load continuously rises and climatic conditions increasingly vary, the performance of conventional base asphalt can no longer satisfy the needs of modern road engineering in low-temperature cracking resistance, high-temperature stability, and long-term durability. Therefore, the development of novel and efficient asphalt modifiers holds significant engineering value and practical importance. In this study, modified asphalt was prepared using varying dosages of ZM modifier (direct-injection asphalt mixture modified polymer additive). A series of experiments was executed to assess its influence on asphalt properties. First, fundamental property tests were implemented to determine the regulating effect of the ZM modifier on basic physical performances, like the softening point and penetration of the base asphalt. Penetration tests at different temperatures were performed to calculate the penetration index, thereby assessing the material’s temperature sensitivity. Subsequently, focusing on temperature as a key factor, tests on temperature sweep, and multiple stress creep recovery (MSCR) were implemented to delve into the deformation resistance and creep recovery behavior of the modified asphalt under high-temperature conditions. In addition, bending beam rheometer (BBR) experiments were introduced to attain stiffness modulus and creep rate indices, which were applied to appraise the low-temperature rheological performance. Aside from Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR) was utilized to explore the mechanism by which the ZM modifier influences the asphalt’s functional group composition and microstructure. Our findings reveal that the ZM modifier significantly increases the asphalt’s softening point and penetration index, reduces penetration and temperature sensitivity, and enhances high-temperature stability. Under high-temperature conditions, the ZM modifier adjusts the viscoelastic balance of asphalt, hence enhancing its resistance to flow deformation and its capacity for creep recovery. In low-temperature environments, the modifier increases the stiffness modulus of asphalt and improves its crack resistance. FTIR analyses reveal that the ZM modifier does not introduce new functional groups, indicating a physical modification process. However, by enhancing the cross-linked structure and increasing the hydrocarbon content within the asphalt, it strengthens the adhesion between the asphalt and aggregates. Overall, the asphalt’s performance improvement positively relates to the dosage of the ZM modifier, providing both theoretical basis and experimental support for its application in road engineering. Full article

This review summarizes the recent advances in the application of nanomaterial coatings in optical fiber sensors, with a particular focus on deposition techniques and the research progress over the past five years in humidity sensing, gas detection, and biosensing. Benefiting from the high specific surface area, abundant surface active sites, and quantum confinement effects of nanomaterials, advanced thin-film fabrication techniques—including spin coating, dip coating, self-assembly, physical/chemical vapor deposition, atomic layer deposition (ALD), electrochemical deposition (ECD), electron beam evaporation (E-beam evaporation), pulsed laser deposition (PLD) and electrospinning, and other techniques—have been widely employed in the construction of functional layers for optical fiber sensors, significantly enhancing their sensitivity, response speed, and environmental stability. Studies have demonstrated that nanocoatings can achieve high-sensitivity detection of targets such as humidity, volatile organic compounds (VOCs), and biomarkers by enhancing evanescent field coupling and enabling optical effects such as surface plasmon resonance (SPR), localized surface plasmon resonance (LSPR), and lossy mode resonance (LMR). This paper first analyzes the principles and optimization strategies of nanocoating fabrication techniques, then explores the mechanisms by which nanomaterials enhance sensor performance across various application domains, and finally presents future research directions in material performance optimization, cost control, and the development of novel nanocomposites. These insights provide a theoretical foundation for the functional design and practical implementation of nanomaterial-based optical fiber sensors. Full article

Beam diagnostics are essential tools for monitoring the performance of charged particle beams and the safe operation of particle accelerators. The performance of an accelerator is determined by evaluating the properties of beam particles, such as energy, charge, spatial, and temporal density distributions, which require very specific instruments. Faraday Cups (FCs) have emerged as important beam diagnostic devices because of their ability to accurately measure the beam charge and, in some cases, the charge distribution, which can be subsequently used to reconstruct transverse beam profiles. This paper aims to provide a detailed review of FCs, their principles, and their design challenges. FCs have applications in various scientific disciplines that include the measurement of beam current/intensity in particle accelerators, in addition to those for mass spectrometry, beam profiles/total beam currents for broad ion beams, thermonuclear fusion, and antimatter experiments. This review also covers and discusses the versatility of FCs in various scientific disciplines, along with showcasing the technological advancements that include improved collector materials, novel designs, enhanced measurement techniques, and developments in electronics and data acquisition (D.A.Q). A summary of the challenges faced while working with the FCs, such as sensitivity, calibration, and potential errors, is included in this review. Full article

With the rapid expansion of underground rail transit construction in China, the high carbon emissions associated with subway tunnels and stations have become an increasing concern. This study systematically examines the carbon emissions of prefabricated concrete–filled steel pipe columns (PCSPCs) during the construction phase of a Beijing subway station built via the pile beam arch (PBA) method, applying the life cycle assessment (LCA) methodology as a case study. An analytical framework for the synergistic optimization of carbon emissions and costs was developed. By systematically adjusting key design parameters—such as the column diameter, wall thickness, and concrete strength—it was possible to minimize both carbon emissions and project costs while meeting the ultimate load-bearing capacity requirements. The results indicate that the production phase of PCSPCs accounts for as much as 98.845% of total carbon emissions, with labor, materials, and machinery contributing 10.342%, 88.724%, and 0.934%, respectively. A sensitivity analysis revealed that steel plates have the greatest impact on carbon emissions, followed by steel reinforcement, whereas concrete and cement exhibit relatively lower sensitivities. The ultimate load-bearing capacity of PCSPCs increases with larger column diameters, thicker walls, and higher concrete strength grades, with the relationships displaying a nonlinear trend. The damage modes and performance of PCSPCs under different design parameters were further verified through finite element analysis. On the basis of the optimization algorithm used to adjust the design parameters, the carbon emissions and costs of the PCSPCs were reduced by 10.32% and 21.55%, respectively, while still meeting the load-bearing capacity requirements. Full article

Single-crystal 4H silicon carbide (4H-SiC) is a key substrate material for third-generation semiconductor devices, where surface and subsurface integrity critically affect performance and reliability. This study systematically examined the evolution of surface morphology and subsurface damage (SSD) during nanoscratching of 4H-SiC under varying normal loads (0–100 mN) using a nanoindenter equipped with a diamond Berkovich tip. Scratch characteristics were assessed using scanning electron microscopy (SEM), while cross-sectional SSD was characterised via focused ion beam (FIB) slicing and transmission electron microscopy (TEM). The results revealed three distinct material removal regimes: ductile removal below 14.5 mN, a brittle-to-ductile transition between 14.5–59.3 mN, and brittle removal above 59.3 mN. Notably, substantial subsurface damage—including median cracks exceeding 4 μm and dislocation clusters—was observed even within the transition zone where the surface appeared smooth. A thin amorphous layer at the indenter-substrate interface suppressed immediate surface defects but promoted subsurface damage nucleation. Crack propagation followed slip lines or their intersections, demonstrating sensitivity to local stress states. These findings offer important insights into nanoscale damage mechanisms, which are essential for optimizing precision machining processes to minimise SSD in SiC substrates. Full article

This study presents a self-powered smart wrist brace integrated with a piezoelectric sensor for real-time biomechanical monitoring during weightlifting activities. The system was designed to quantify wrist flexion across multiple loading conditions (0 kg, 0.5 kg, and 1.0 kg), leveraging mechanical strain-induced voltage generation to capture angular displacement. A flexible PVDF film was embedded within a custom-fitted wrist brace and tested on male and female participants performing controlled wrist flexion. The resulting voltage signals were analyzed to extract root-mean-square (RMS) outputs, calibration curves, and sensitivity metrics. To interpret the experimental results analytically, a lumped-parameter cantilever beam model was developed, linking wrist flexion angles to piezoelectric voltage output based on mechanical deformation theory. The model assumed a linear relationship between wrist angle and induced strain, enabling theoretical voltage prediction through simplified material and geometric parameters. Model-predicted voltage responses were compared with experimental measurements, demonstrating a good agreement and validating the mechanical-electrical coupling approach. Experimental results revealed consistent voltage increases with both wrist angle and applied load, and regression analysis demonstrated strong linear or mildly nonlinear fits with high $R^2$ values (up to 0.994) across all conditions. Furthermore, surface plots and strain sensitivity analyses highlighted the system’s responsiveness to simultaneous angular and loading changes. These findings validate the smart wrist brace as a reliable, low-power biomechanical monitoring tool, with promising applications in injury prevention, rehabilitation, and real-time athletic performance feedback. Full article

Carbon nanotubes (CNTs) are cylindrical nanostructures formed by rolling a graphene sheet—a hexagonal lattice of carbon atoms—into a tube. Based on the rolling direction, CNTs are categorized as armchair, zigzag, or chiral. The chiral vector, derived from the graphene lattice, defines the CNT’s structure, with chiral CNTs denoted by indices (n, m), where m > 0 and m ≠ n. The mechanical properties and structural stability of CNTs are highly sensitive to defects and impurities within their atomic framework. Among these, point defects such as single-atom vacancies are the most prevalent and can significantly degrade mechanical performance. These defects alter stress distribution, reduce stiffness, and impair strength, thereby limiting the functional reliability of CNTs in advanced applications such as nanocomposites, sensors, and electronic devices. This study examines the influence of vacancy defects on CNT mechanical behavior through a multiscale modeling framework. Molecular dynamics (MD) simulations are conducted using LAMMPS, with structural visualization via Visual Molecular Dynamics (VMD). Concurrently, a finite element (FE) model is developed in ANSYS, where the CNT is idealized as a space frame of elastic beam elements representing carbon–carbon bonds. The integration of atomistic and continuum approaches offers a comprehensive understanding of defect-induced mechanical degradation. The MD and FEM results are in strong agreement with findings in existing literature, validating the adopted methodology. These findings contribute valuable insights into the design and optimization of CNT-based materials for high-performance engineering applications. Full article

This study explores a global–local non-intrusive computational strategy to address problems in computational mechanics, specifically applied to a double cantilever beam (DCB) with cohesive interfaces. The method aims to reduce computational requirements while maintaining accuracy. The DCB, representing two plates connected by a cohesive zone simulating delamination, was modeled with a 3D representation using the cohesive zone method for crack propagation. Different mesh configurations were tested to evaluate the strategy’s effectiveness. The results showed that the global–local strategy successfully provided solutions that were comparable to monolithic models. Mesh size had a significant impact on the results, but even with a simplified local model that did not fully represent the plate thickness, the structural deformation and crack displacement were accurately captured. The interface near the study area influenced the stress distribution. Although effective, the strategy requires careful mesh selection due to its sensitivity to mesh size. Future research could optimize mesh configurations, expand the strategy to other structures, and explore the use of orthotropic materials. This research introduces a computational approach that reduces costs while simulating delamination and crack propagation, highlighting the importance of mesh configuration for real-world applications. Full article

This work presents the theoretical design and finite element modeling of high-sensitivity microcantilevers for biosensing applications, integrating piezoelectric actuation with novel ultrananocrystalline diamond (UNCD) structures. Microcantilevers were designed based on projections to grow a multilayer metal/AlN/metal/UNCD stack on silicon substrates, optimized to detect adsorption of biomolecules on the surface of exposed UNCD microcantilevers at the picogram scale. A central design criterion was to match the microcantilever’s eigenfrequency with the resonant frequency of the AlN-based piezoelectric actuator, enabling efficient dynamic excitation. The beam length was tuned to ensure a ≥2 kHz resonant frequency shift upon adsorption of 1 pg of mass distributed on the exposed surface of a UNCD-based microcantilever. Subsequently, a Gaussian distribution mass function with a variance of 5 µm was implemented to evaluate the resonant frequency shift upon mass addition at a certain point on the microcantilever where a variation from 600 Hz to 100 Hz was observed when the mass distribution center was located at the tip of the microcantilever and the piezoelectric borderline, respectively. Both frequency and time domain analyses were performed to predict the resonance behavior, oscillation amplitude, and quality factor. To ensure the reliability of the simulations, the model was first validated using experimental results reported in the literature for an AlN/nanocrystalline diamond (NCD) microcantilever. The results confirmed that the AlN/UNCD architecture exhibits higher resonant frequencies and enhanced sensitivity compared to equivalent AlN/Si structures. The findings demonstrate that using a UNCD-based microcantilever not only improves biocompatibility but also significantly enhances the mechanical performance of the biosensor, offering a robust foundation for the development of next-generation MEMS-based biochemical detection platforms. The research reported here introduces a novel design methodology that integrates piezoelectric actuation with UNCD microcantilevers through eigenfrequency matching, enabling efficient picogram-scale mass detection. Unlike previous approaches, it combines actuator and cantilever optimization within a unified finite element framework, validated against experimental data published in the literature for similar piezo-actuated sensors using materials with inferior biocompatibility compared with the novel UNCD. The dual-domain simulation strategy offers accurate prediction of key performance metrics, establishing a robust and scalable path for next-generation MEMS biosensors. Full article

In the context of autonomous environmental monitoring, this study investigates a surface acoustic wave (SAW) sensor designed for selective carbon dioxide (CO₂) detection. The sensor is based on a LiTaO₃ piezoelectric substrate with copper interdigital transducers and a polyetherimide (PEI) layer, chosen for its high electromechanical coupling and strong CO₂ affinity. Finite element simulations were conducted to analyze the resonance frequency response under varying gas concentrations, film thicknesses, pressures, and temperatures. Results demonstrate a linear and sensitive frequency shift, with detection capability starting from 10 ppm. The sensor’s autonomy is ensured by a piezoelectric energy harvester composed of a cantilever beam structure with an attached seismic mass, where mechanical vibrations induce stress in a piezoelectric layer (PZT-5H or PVDF), generating electrical energy via the direct piezoelectric effect. Analytical and numerical analyses were performed to evaluate the influence of excitation frequency, material properties, and optimal load on power output. This integrated configuration offers a compact and energy-independent solution for real-time CO₂ monitoring in low-power or inaccessible environments. Full article

Beam-type structures used in aerospace applications may experience simultaneous broadband dynamic excitation and thermal loads. Design sensitivity, as a powerful tool for structural optimization and reliability analysis, is investigated in this work. The broadband dynamic response and its sensitivity to input parameters for a Euler–Bernoulli beam in a thermal environment are examined using an efficient wave-based method (WBM). First, the accuracy of the simulation for predicting the broadband dynamic response is validated. Then, the influence of thermal effects on the dynamic response is investigated. Further, the normalized sensitivities of the dynamic response with respect to thermal loads, material properties, and geometric parameters are studied. The simulation results highlight the critical role of thermally generated compressive forces in governing structural dynamics. The normalized sensitivities with respect to different input parameters can vary across the broadband frequency band. In the low-frequency ranges, the sensitivities with respect to thermal load, thermal expansion coefficient, the cross-section area, and moment of inertia are dominant. In the high-frequency ranges, the cross-section area, moment of inertia, elastic modulus, and density have major influence on the dynamic response. All the parameters investigated could significantly affect the mid-frequency dynamic response. Full article

This study presents the development and application of the stochastic finite element method (SFEM) to analyze the static response of beams with a two-dimensional (2D) spatially varying elastic modulus. A 2D stationary stochastic field is employed to model the elastic modulus, capturing the material heterogeneity along both the longitudinal and vertical directions of the beam. The weighted integral method is applied to represent the random field as random variables and to compute the element stiffness matrices, while a first-order perturbation technique is utilized to estimate the statistical moments of the nodal displacement vector, including the mean and covariance matrix. This method enhances both computational efficiency and accuracy in capturing material heterogeneity compared to traditional approaches. The precision and effectiveness of the developed SFEM are evaluated through comparisons with Monte Carlo simulations (MCs), demonstrating strong agreement in the analysis of the coefficient of variation (COV) of displacement. A sensitivity analysis is conducted to examine the influence of the correlation length and dispersion of the stochastic field on the COV. The results indicate that the COV generally increases as these parameters grow, with the most significant variations occurring at small correlation lengths. As the correlation length becomes very large, the COV of displacement converges toward the standard deviation of the input stochastic field. Furthermore, the study reveals that the correlation length along the beam’s longitudinal axis has a more pronounced effect on the COV of displacement compared to the vertical correlation length. Full article

In practical engineering, uncertainties inevitably exist in the models and measurement data used for structures. Therefore, a statistical strategy related to damage detection methods become crucial. In this paper, a probabilistic statistical damage detection method for FG Euler–Bernoulli beam structures is proposed, extending the approach originally developed for isotropic materials. Our approach determines the probability of damage occurrence for each element, which aids in evaluating whether beam structures have been damaged. This evaluation is based on integrating the sensitivity of modal strain energy for each element with the perturbation method. To demonstrate the effectiveness and accuracy of the proposed method, several numerical examples are investigated. These examples include a simply supported FG Euler–Bernoulli beam subjected to both single and multiple element damages. The influence of gradient index, damage severity, boundary condition, and noise level on the accuracy of detection are also considered. The studies demonstrate that the probability of damage for each element remains relatively stable despite variations in the gradient indices. For the damaged elements, these probabilities approach 1, indicating that the proposed method effectively identifies damage in FG beams even when the gradient index varies. Additionally, as the level of damage increases, the accuracy of damage detection tends to improve. However, varying boundary conditions can substantially affect the outcomes of damage identification, potentially leading to inconsistencies in results. Furthermore, our proposed method demonstrates excellent resistance against noise levels of up to 5%. We also found that different boundary conditions have a great impact on the damage detection. Full article