Loading Scenarios Research Articles

Reliability-based capacity safety factor for G20Mn5 cast steel joints

Cast steel joints have complex geometries and great load-carrying capacity, and hence their resistances are commonly determined through FE simulations. Considering the significant variability in the material mechanical properties (that in fracture strains, in particular) and the model uncertainty inevitably involved in the FE analysis, a large safety factor (γR) should be adopted for the FE simulation-based design of cast steel joints. However, the γR-values specified by the current design specifications, e.g. the Chinese standard CECS 235: 2008 in which γR = 3.0, are deemed to be overly conservative. This paper examined the structural reliability of G20Mn5 cast steel joints to provide a rational γR-value for the practical joint design. The above-mentioned uncertainty of the material and geometric properties as well as the model uncertainty related to FE simulations are evaluated, and Monte Carlo simulations were conducted on nine representative joints to obtain the statistical characteristics of cast steel joints’ ultimate resistance, allowing for both failure modes of material fracture and instability caused by large-scale yielding. FORM was adopted to calculate the reliability index β for the assumed γR-values and different loading scenarios, and the recommended values for γR under target reliability indices are proposed. For snow load-dominant and wind load-dominant load combinations, the recommended γR corresponding to β = 4.2 are 2.29 and 2.16, respectively, and the recommended γR corresponding to β = 3.7 are 2.01 and 1.90, respectively.

Low-resource dynamic loading identification of nonlinear system using pretraining

To address the challenge of load identification in nonlinear systems, an audio neural network-based method called WaveNet is proposed that leverages its capability to capture long-term dependencies in mechanical systems, enabling accurate load identification. Unlike traditional dynamic load identification methods that often encounter difficulties with matrix solutions, this approach takes advantage of WaveNet’s capabilities, enhancing both accuracy and efficiency. We integrate pre-training and transfer learning techniques to address the data scarcity challenges often encountered in real-world engineering applications. By transferring features across distributed datasets, this method reduces the dependency on single-task data, thereby improving model robustness. The performance of the WaveNet method is rigorously evaluated against traditional benchmarks such as Multilayer Perceptron (MLP) and Convolutional Neural Network (CNN) benchmarks under random load conditions applied to a complex structural framework. The proposed method achieves a root mean squared error (RMSE) of 1.521 and a determination coefficient (R²) of 0.996 in the random load case, demonstrating superior accuracy compared to other approaches. Moreover, its applicability is verified through simulations of both impact load and harmonic load scenarios, showcasing the effectiveness of transfer learning in overcoming domain discrepancies. Finally, the method is tested in random experiments to validate its engineering applicability. The results highlight the significant accuracy improvement in low-resource tasks achieved through pre-training, showcasing the potential and value of the proposed method.

Application of ANN Concepts for Prediction of Crack Growth and Remaining Life of Circumferentially Cracked Piping Components under Different Loading Scenarios

This paper presents the details of Artificial Neural Network (ANN) based models to predict crack depth and remaining life of circumferentially part-through cracked piping components used in the nuclear industry. Crack growth data from experimental studies on (i) straight pipes made up of SA 312 Type 304LN stainless steel having part-through circumferential notch under combined bending and torsion and (ii) dissimilar metal pipe weld joints having circumferential through-wall crack in the weld were used. The dissimilar metal pipe weld joint is made up of SA312 Type 304LN austenitic stainless steel and SA508 Gr. 3 Cl. 1 low alloy carbon steel and joined by Nickel-rich Inconel alloy weld. About 75% of the mixed experimental data has been used for development of model and the remaining data for validation. For the development of ANN model, (i) back propagation technique (ii) sigmoidal transfer functions and (iii) Levenberg-Marquardt algorithm are used. The maximum percentage difference observed between the predicted and the experimental results for crack depth and remaining life was 19.59% and 15.78% respectively. The efficiency of the developed model was verified using several statistical parameters. The developed models are useful for the structural integrity assessment of piping components under different loading scenarios.

Blast response and protection level of concrete composite wall panels made with recycled tyre fibres in a cement matrix

This paper investigates the blast response and protection level of a new blast-resistance wall panels made of Recycled Tyre Fibres Concrete (RTFC) as an exterior protective layer compositely connected to a concrete wall through experiments and numerical modelling. RTFC is a novel sustainable cement-based versatile composite, made with recycled non-biodegradable material (tyre rubber particles) in the form of composite fibres in a cement matrix, with applications in blast and ballistic protection that features high dynamic properties. Three composite wall panels were constructed and subjected to blast loads generated by Advanced Blast Simulator (ABS). Three different loading scenarios, each with an intensity capable of causing complete structural failure, were applied. The blast wave characteristics, such as the imposed overpressure and impulse, were measured alongside the deflection of the specimens. The wall specimens could withstand the blast loads with minimal damage. No cracks were observed in the RTFC layer, with only minor cracks appearing on the concrete side of the wall. The protection performance of the wall specimens was examined following UFC guidelines, revealing their outstanding resistance to blast loads. Moreover, in line with these guidelines, the composite wall panel is eligible for a High Level of Protection (HLoP) classification. Finally, a Single Degree of Freedom (SDOF) analytical model was developed for the wall panel to determine the Dynamic Increase Factor (DIF) of RTFC. The analysis resulted in a DIF value of 1.5, which was then incorporated into the numerical models of the walls. The accuracy of the DIF was validated by comparing the numerical results with experimental data, confirming its reliability.

Numerical investigation of the effective mechanical properties of architected structures: a comparative study of flexural stiffness, homogenization, and elastic anisotropy

Abstract The mechanical behavior of architected structures is influenced by various parameters, including the topology of their unit cells. This anisotropic nature requires the determination of the mechanical properties under different loading scenarios. This study employs numerical investigation to characterize the influence of topology on the mechanical properties of eight architected structures, focusing on effective elastic properties and anisotropic elastic behavior. The analyzed topologies encompass four based on struts (lattices) and four based on triply periodic minimal surfaces (TPMS), comprising Sheet and Network phases. Initially, beams composed of architected structures are subjected to flexure, with Euler–Bernoulli and Tymoshenko’s theories utilized in a first numerical approach to determine their effective properties. Subsequently, a numerical homogenization method along with the Voigt-Reuss-Hill scheme is employed in a second approach. A more substantial influence of topology on the effective properties is observed in low relative densities. The study revealed that for a relative density of 10%, the appropriate selection of the topology increases the stiffness of a structure by up to ∼126%. The EBT approach underestimated the stiffness by up to ∼26% due to neglecting the impact of shear on beam deflection. The tensorial anisotropy index revealed up to ∼27% higher anisotropy compared to the Zener index. These findings provide a valuable numerical tool for the comparison and selection of architected structures suitable for diverse applications.

HyperCAN: Hypernetwork-driven deep parameterized constitutive models for metamaterials

We introduce HyperCAN, a machine learning framework that utilizes hypernetworks to construct adaptable constitutive artificial neural networks for a wide range of beam-based metamaterials exhibiting diverse mechanical behavior under finite deformations. HyperCAN integrates an input convex neural network that models the nonlinear stress–strain map of a truss lattice, while ensuring adherence to fundamental mechanics principles, along with a hypernetwork that dynamically adjusts the parameters of the convex network as a function of the lattice topology and geometry. This unified framework demonstrates robust generalization in predicting the mechanical behavior of previously unseen metamaterial designs and loading scenarios well beyond the training domain. We show how HyperCAN can be integrated into multiscale simulations to accurately capture the highly nonlinear responses of large-scale truss metamaterials, closely matching fully resolved simulations while significantly reducing computational costs. This offers new efficient opportunities for the multiscale design and optimization of truss metamaterials.

A novel impact approach based on electromagnetic loading technology: A case study on CFRP/Al riveted structures

To investigate the mechanical response and damage behavior of aircraft fuselage composite structures under out-of-plane impact loads more efficiently and flexibility, this paper proposed a novel impact approach and testing platform based on inductive coils to yield electromagnetic impact force. The influence of system key parameters on the electromagnetic loading waveforms were analyzed using an electromagnetic field finite element model. Single/repeated impact tests on CFRP/aluminium alloy (Al) riveted structures were conducted at different voltages (energies) based on this approach. The results indicate that the electromagnetic impact (EMI) approach exhibits significant advantages in both variable strain rate loading and continuous impact loading scenarios. This device can efficiently achieve multi-point and multiple impact loading. The electromagnetic impact forces with various amplitudes and pulse-widths can be accurately obtained by altering voltage and capacitance values, which can demonstrate the good experimental consistency of such test approach. Besides, with this test method, the load threshold for damage formation can be clearly defined: once the impact force exceeds the damage threshold load, the delamination area of the CFRP laminates expand as the impact energy increases. Note that when the provided out-of-plane impact load is slightly higher than the damage threshold load by changing the voltage, significant delamination damage may suddenly manifest in any one impact event of the repeated impacts.

Artificial Neural Network-Based Hybrid Controller for Electric Vehicle Applications

Power management among different energy sources of electric vehicles (EV) is one of the complex issues during the transition from one to another. A specific control is modeled based on the current and speed range of the electric motor named as Measurement of Parameter-Based Controller (MPBC), which will play a key role during transition of energy sources as per the load requirement. Two bidirectional converters are utilized to control the pulse signals generated by the traditional controllers which are connected at the battery and Supercapacitors (SCap) ends, which are treated as passive sources of the system. The Controller's artificial neural network (ANN), fuzzy logic (FLC), and proportional-integral (PI) are utilized to generate the pulse signal to the switches present in the converters by load. Further, specific controller MPBC is combined with three controllers ANN/FLC/PI individually and obtained three separate hybrid controllers as per the proposed control technique. The MPBC+PI/FLC/ANN controller-based MATLAB/Simulink model was designed, and applied to the electric motor individually at different load conditions. This model considered three different power delivery states from the PV array and assessed the motor's performance under different load scenarios. Compare the three hybrid controllers' final results to find out which one is more effective than the others.

Finite Element Analysis and Design Optimisation of Welding robot base using ANSYS

The study on Finite Element Analysis and Design Optimization of a welding robot base using ANSYS explores the crucial role of welding robots in enhancing industrial productivity and quality. It addresses the challenges of structural integrity, operational efficiency, and durability under harsh conditions. The research focuses on optimizing the robot base design to withstand high production demands, using ANSYS for detailed modelling and simulation. It reviews previous studies highlighting the importance of vibrational characteristics and structural dynamics in robot design. The methodology involves creating a geometric model, defining material properties, meshing, and applying boundary conditions and loading scenarios. The results indicate that the optimized design significantly improves stress distribution, reduces deformation, and enhances the dynamic response, making the robot base more robust and reliable. The study concludes with recommendations for broader applications in robotic systems to improve safety and productivity in industrial settings.

The effect of the floor-wall interaction on the post-elastic rocking behaviour of segmented CLT shear walls

This paper investigates the impact of floor-wall interaction on the lateral rocking behaviour of two-panel segmented Cross-Laminated Timber (CLT) shear walls through experimental testing and numerical analyses. The study extends a simplified equivalent spring modelling approach to the post-elastic domain to accurately simulate the effects of floor-wall interaction. Experimental withdrawal tests on four types of floor-to-wall connections, utilizing Fully Threaded Screws (FTSs) and Partially Threaded Screws (PTSs) at varying inclinations, revealed that FTSs exhibited higher stiffness and load-carrying capacity but lower deformation capacity compared to PTSs. The maximum force of FTS connections was about twice that of PTS connections, while PTS connections achieved ultimate displacements up to seven times greater. Numerical analyses using a parametric framework showed that floor-wall interaction increases the stiffness and lateral load-carrying capacity of segmented shear walls, with a significant reduction in deformation capacity. These analyses also demonstrated that the equivalent spring can effectively replicate the influence of floor-wall interaction on shear wall lateral response. Expressions for calculating the strength and ductility of this equivalent spring were developed, considering a limited set of geometric and mechanical parameters. These expressions provide a simplified yet accurate method for estimating the effects of floor-wall interaction. The findings suggest that understanding the interaction between floor and wall elements is crucial for optimizing the performance of segmented CLT shear walls in seismic and lateral load scenarios.

Kinematic Patterns of Different Loading Profiles Before and After Total Knee Arthroplasty: A Cadaveric Study

One of the major goals of total knee arthroplasty (TKA) is to restore the physiological function of the knee. In order to select the appropriate TKA design for a specific patient, it would be helpful to understand whether there is an association between passive knee kinematics intraoperatively and during complex activities, such as ascending stairs. Therefore, the primary objective of this study was to compare the anterior–posterior (AP) range of motion during simulated passive flexion and stair ascent at different conditions in the same knees using a six-degrees-of-freedom joint motion simulator, and secondary, to identify whether differences between TKA designs with and without a post-cam mechanism can be detected during both activities, and if one design is superior in recreating the AP translation of the native knee. It was shown that neither TKA design was superior in restoring the mean native AP translation, but that both CR/CS and PS TKA designs may be suitable to restore the individual native kinematic pattern. Moreover, it was shown that passive and complex loading scenarios do not result in exactly the same kinematic pattern, but lead to the same choice of implant design to restore the general kinematic behavior of the native individual knee.

Centrifuge tests on the consolidation behaviour of foundations under alternating loads

This paper presents the results from centrifuge tests on raft foundations and piled rafts in overconsolidated kaolin clay carried out in a beam centrifuge at the University of Pretoria. The foundations were subjected to unloading and reloading phases as well as to multiple consecutive groundwater drawdowns, simulating typical loading scenarios of structures in an urban environment. In order to investigate the time-dependent load-deformation behaviour of foundations, the settlement of the foundations, the pore water pressures at different depths and the axial strains in the piles, from which pile resistances were then derived, were measured continuously. In the tests, repeated groundwater drawdowns resulted in a settlement accumulation, with the increase in settlements decreasing with each repetition. The pile position within the pile group was found to have a major impact on the mobilised pile resistance. The position, however, became less significant as the load level increased. Furthermore, insights were gained concerning the load distribution within piled rafts during consolidation.

Analysing Flexural Response in RC Beams: A Closed-Form Solution Designer Perspective from Detailed to Simplified Modelling

This paper presents a detailed analytical approach for the bending analysis of reinforced concrete beams, integrating both structural mechanics principles and Eurocode 2 provisions. The general analytical expressions derived for the curvature were applied for the transverse displacement analysis of a simply supported reinforced concrete beam under four-point loading, focusing on key limit states: the initiation of cracking, the yielding of tensile reinforcement and the compressive failure of concrete. The displacement’s results were validated through experimental testing, showing a high degree of accuracy in the elastic and crack propagation phases. Deviations in the yielding phase were attributed to the conservative material assumptions within the Eurocode 2 framework, though the analytical model remained reliable overall. To streamline the computational process for more complex structures, a simplified model utilising a non-linear rotational spring was further developed. This model effectively captures the influence of cracking with significantly reduced computational effort, making it suitable for serviceability limit state analyses in complex loading scenarios, such as seismic impacts. The results demonstrate that combining detailed analytical methods with this simplified model provides an efficient and practical solution for the analysis of reinforced concrete beams, balancing precision with computational efficiency.

Design, Evaluation & Implementation of a Novel MRI-compatible Physiologic Loading Simulator for Ex-Vivo Joints.

Quantitative magnetic resonance imaging (qMRI), in combination with mechanical testing, offers potential to investigate how loading is related to joint and tissue function. However, current testing devices compatible with MRI are often limited to uniaxial compression, often applying low loads, or loading individual tissues (instead of multiple), while more complex simulators do not facilitate MRI. Hence, in this work, we designed, built and tested (N=1) an MRI-compatible multiaxial load-control system which enables scanning cadaveric joints (healthy or pathologic) loaded to physiologically-relevant levels. Testing involved estimating and validating physiologic loading conditions before implementing them experimentally on cadaver knees to simulate and image gait loading (stance and swing). The resulting design consisted of a portable loading device featuring pneumatic actuators to reach a combined loading scenario, including axial compression (=2.5 kN), shear (=1 kN), bending (=30 N·m) and muscle tension. Initial laboratory testing was carried out; specifically, the device was instrumented with force and pressure sensors to evaluate loading and contact response repeatability in one cadaver knee specimen. This loading system was able to simulate healthy or pathologic gait with reasonable repeatability (e.g., 1.23 to 2.91 % coefficient of variation for axial compression), comparable to current state-of-the-art simulators, leading to generally consistent contact responses. Contact measurements demonstrated a tibiofemoral to patellofemoral load transfer with knee flexion and large contact pressures concentrated over small sites between the femoral cartilage and menisci, agreeing with experimental studies and numerical simulations in the literature.

Power Quality Improvement Using a Novel Simplified Adaptive Control of Solar PV Integrated Shunt Active Power Filter

ABSTRACTPower quality improvement in grid‐tied solar photovoltaic (PV) system is one of the emergent current research trends. To address the power quality concern, shunt active power filters (SAPF) are widely deployed and researched in grid‐tied PV systems. This paper presents a simplified adaptive transversal (SAT)‐filter control scheme for the two‐stage PV‐SAPF system. The proposed control has the ability to improve different power quality indices. Also, it features faster operation under steady‐state and under dynamic conditions, better dc‐offset rejection ability due to estimation of the accurate fundamental component of the load current. To justify the effectiveness of the proposed control scheme, a thorough comparison is carried out with different conventional and adaptive control schemes. Simulations and extensive experimental analysis are carried out under severe transient conditions such as balanced‐unbalanced supply, sudden load, and irradiation change scenario.

Combined Freak Wave, Wind, and Current Effects on the Dynamic Responses of Offshore Triceratops

Offshore structures are exposed to various environmental loads, including extreme and abnormal waves, over their operational lifespan. The existence of wind and current can exacerbate the dynamic response of these structures, posing threats to safety and integrity. This study focuses on the dynamic responses of offshore triceratops under different environmental conditions characterized by the superimposition of freak waves, uniform wind, and current. The free surface profile of the freak wave was generated using the dual superposition model. The numerical model of the offshore platform designed for ultra-deep-water applications was developed using the ANSYS AQWA 2023 R2 modeler. Numerical investigations, including the free decay tests and time-domain analysis under random sea states, including freak waves, were initially carried out. Then, the combined effects of freak waves, wind, and current were studied in detail under different loading scenarios. The results revealed the increase in structural response under the freak wave action at the focus time. Wind action resulted in a mean shift in responses, while the inclusion of current led to a pronounced increase in the total response of the platform, encompassing deck and buoyant legs, alongside the tether tension variation. Notably, considerable variations in the response were observed after freak wave exposure under the combined influence of wind, freak wave, and current. The results underscore the profound effects induced by wind and current in the presence of freak waves, providing valuable insights for analyzing similar offshore structures under ultimate design conditions.

Effect of Thermal Load Caused by Tread Braking on Crack Propagation in Railway Wheels on Long Downhill Ramps

To investigate the propagation behavior of thermal cracks on the wheel tread under the conditions of long downhill ramps, a three-dimensional finite element model of a 1/16 wheel, including an initial thermal crack, was developed using the finite element software ANSYS 17.0. The loading scenarios considered include mechanical wheel–rail loads, both with and without the superposition of thermal wheel–brake shoe friction loads. The virtual crack closure method (VCCM) is employed to analyze the variations in stress intensity factors (SIFs) for Modes I, II, and III (KI, KII, and KIII) at the 0°, mid, and 90° positions along the crack tip. The simulation results show that temperature is a critical factor for the propagation of thermal cracks. Among the SIFs, KII (Mode II) is larger than KI (Mode I) and KIII (Mode III). Specifically, the thermal load on the wheel tread during braking contributes up to 23.83% to KII when the wheel tread reaches the martensitic phase transition temperature due to brake failure. These results are consistent with the observed radial propagation of thermal cracks in wheel treads under operational conditions.

Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis

Abstract In the field of electric motor maintenance, this study introduces a transformative approach by inte-grating entropy-based algorithms with machine learning for enhanced multi-class fault detection. Employing Shannon, Renyi, and Tsallis entropy algorithms on standard fault detection measure-ments, the research significantly advances predictive maintenance strategies through a robust, ear-ly-indication, system-agnostic analysis. Detailed examination is conducted, comparing results de-rived from datasets that include statistical features (excluding entropy) against the proposed entro-py-based datasets, when applied to a Multi-Layer Perceptron Classifier. Optimization of the MLPC and all compared algorithms’ hyperparameters is done using the state-of-the-art Optuna tool to dynamically explore each search space, ensuring that each methodology performs adequately in a timely fashion while allowing for adaptation. The results showcase significant enhancement in clas-sification accuracy of diverse electric motor operational states, facilitating the differentiation be-tween healthy and various levels of fault conditions under assorted load scenarios. Computational analyses reveal favorable results related to execution time and memory overhead, thereby support-ing the practicality in operations constrained by memory resources. Validation of the approach is achieved through laboratory experiments on a purpose-built test bench. Versatility of entropy-based measures through their proposed utilization in diverse fault indications is achieved by a demonstration in the field of mechanical fault detection with a focus on bearing faults through well-respected datasets.

A modified synchronverter for a weak grid with virtual power circles‐based PQ decoupling scheme

AbstractThe high penetration of renewable energy sources (RES) results in a low‐inertia, weak power grid. To mitigate this and restore system inertia, it has been widely proposed to operate the inverters of RES units to mimic synchronous generators; this technology is known as a virtual synchronous generator (VSG). In weak grids there is, however, strong coupling between active power () and reactive power (), and any VSG technique therefore requires decoupling in order to operate effectively. This article proposes a new decoupling technique based on the transformation of the power circle of a VSG connected to a weak grid: first, the power circle is translated from its designed position to that of a conventional synchronous generator (SG) connected to a strong grid, achieving partial decoupling. Then, to achieve full decoupling, the authors propose further to modulate the radius of the translated power circle; this is achieved using a series resistance‐capacitance–inductance () circuit which is virtually implemented in the VSG controller. The efficacy of the proposed scheme is validated using a modified synchronverter connected to the weak grid in representative loading scenarios. It is demonstrated that the technique achieves a decoupled control for the synchronverter connected in a weak grid. Moreover, the modified synchronverter is capable of supporting frequency and voltage regulation in the grid without inducing large transient grid currents during mild frequency and voltage variations.

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.