Conventional Design Methods Research Articles

Analysis of Computer Aided Interaction Design Based on Virtual Reality Technology

The conventional method of computer-aided interaction design faces challenges due to ambiguous user experience characteristics, resulting in a reduced number of identifiable frames. To address this issue, a novel computer-aided interaction design approach utilizing virtual reality technology has been developed. This method facilitates communication through visual computing, enabling the acquisition of feedback structures from computer vision. It allows for the mapping of human body contours and the extraction of user experience characteristics via virtual reality technology. Additionally, it measures the time required for reading and generating pertinent information, thereby constructing an effective computer-aided interaction mode. Experimental findings reveal that the average frame identification rates achieved by the proposed method are approximately 81(fps), 74(fps), and 72(fps), respectively. These results, when we will compare with two other computer-aided interaction methods, demonstrate that the integration of virtual reality technology significantly enhances performance. This review revealed that the majority of studies are centered on practical case analyses within specific application scenarios, employing empirical evaluation methods to assess objective or subjective metrics. We then concentrated on elucidating the foundational principles of interface design and their evaluation methodologies, providing a reference for future research endeavors. Additionally, the limitations, challenges, and future directions in VR interaction interface design research were discussed, highlighting the need for further research in design evaluation to continuously refine the development of standards and guidelines for VR interactive interface design.

Combined rupture mechanisms of shallow foundations: implications in limit state design

The design of shallow foundations at the ultimate limit state is conventionally performed assuming a foundation structure that does not undergo any yielding, although current technical standards require the investigation of any possible rupture mechanism, including combined rupture in the soil and in the foundation structure. The aim of this work is to study the practical implications of including the analysis of combined rupture mechanisms (involving both soil and foundation structure) in shallow foundation design under drained and undrained conditions within the framework of current technical standards. Two case studies are presented and tackled with a recent novel strategy for numerical simulations. It is shown that conventional foundation design methods may not be reliable when evaluating internal actions within a spread foundation, hence foundation yielding may not be correctly detected, implying that the bearing capacity of a foundation can be severely overestimated in conventional practice, in particular under drained conditions. On the other hand, foundation yielding can be exploited to reduce the amount of needed reinforcement, while still satisfying both geotechnical and structural technical standards.

A new method of shrink fit design for large-sized prestress die of hot extrusion: An example of large fan shaft

Due to the remarkable increase in the geometric size, interference fit amount and assembly difficulty of large-sized hot extrusion prestress die, the conventional methods (such as thick wall cylinder theory and empirical value method) of shrink fit design were inapplicable to the pre-stressed die for hot extrusion of fan shaft with a maximum radius of 255 mm. In this study, a new shrink fit design method for large-sized prestress die was proposed by using the finite element method, and two sets of the formulas and nomograms that were easy to use were obtained to determine the interference fit ratio β, the radius ratio of die and stress ring a21, a31. Finally, the shrink fit of the prestressed die for the hot extrusion of the large fan shaft was designed by adopting the introduced new approach, and the hot extrusion experiment was carried out. The results show that the prestressed die for hot extrusion of the large fan shaft designed by the new method not only reduced the material cost by 58.6% compared with the integral die, which had considerable economic advantages but also had outstanding stress-bearing capacity, which could be safely applied for the manufacture of large aero-engine fan shaft, which also verified the feasibility of the new shrink fit design method for large-sized hot extrusion prestress die.

Unified Assessment of Open and Ducted Propulsors

This paper reconciles the assessment of fan and propeller performance by deriving common metrics that describe their design and operational characteristics and applies them to real-world design examples. Historically, various applications with large differences in flight Mach number and thrust requirements have led to different design methodologies and performance descriptors for ducted and unducted propulsors, making direct comparisons between these propulsion concepts challenging until today. One of the limitations of conventional propeller design methods is the difficulty in isolating the aerodynamic performance of blade sections from the overall design concept. The overall efficiency is largely impacted by top-level design parameters, while the aerodynamic quality is determined by the shaping and spanwise stacking of blade profiles. In contrast, turbomachinery design focuses primarily on the efficiency of the compression process and their respective efficiency metrics. This paper addresses these issues by systematically breaking down propeller efficiency into contributions commonly used in turbomachinery design. By applying consistent methodologies, we thereby enable a fair and quantitative comparison of the potential performance benefit of each concept. Furthermore, using common performance metrics simplifies the design process, making it more accessible to less experienced designers and facilitating the exploration of alternative design approaches for unducted propulsors.

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

Evolutionary optimisation of pixelated IFA inspired antennas

As wireless communication systems increasingly require compact and efficient antennas, conventional antenna design methods are proving difficult to meet the rigorous demands of modern applications. For this purpose, this study introduces a methodology which uses pixelated Inverted-F Antenna (IFA) inspired designs optimised through genetic algorithms to enhance performance in constrained spatial environments. As pixels the antenna features the exemplary use of Einstein Hat-shaped tiles, enabling the antenna to efficiently utilize space. A with the proposed method optimised antenna is compared to traditional IFA designs and shows improved properties like enhanced antenna gain and efficiency as well as smaller reflection coefficient offering a promising solution for future compact antenna systems in the Internet of Things and beyond. Finally, a prototype was manufactured and the scattering parameters and antenna gain were measured within an anechoic chamber.

A clinical comparison of a digital versus conventional design methodology for transtibial prosthetic interfaces

A transtibial prosthetic interface typically comprises a compliant liner and an outer rigid socket. The preponderance of today’s conventional liners are mass produced in standard sizes, and conventional socket design is labor-intensive and artisanal, lacking clear scientific rationale. This work tests the clinical efficacy of a novel, physics-based digital design framework to create custom prosthetic liner-socket interfaces. In this investigation, we hypothesize that the novel digital approach will improve comfort outcomes compared to a conventional method of liner-socket design. The digital design framework generates custom transtibial prosthetic interfaces starting from MRI or CT image scans of the residual limb. The interface design employs FEA to simulate limb deformation under load. Interfaces are fabricated for 9 limbs from 8 amputees (1 bilateral). Testing compares novel and conventional interfaces across four assessments: 5-min walking trial, thermal imaging, 90-s standing pressure trial, and an evaluation questionnaire. Outcome measures include antalgic gait criterion, skin surface pressures, skin temperature changes, and direct questionnaire feedback. Antalgic gait is compared via a repeated measures linear mixed model while the other assessments are compared via a non-parametric Wilcoxon sign-rank test. A statistically significant (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P=0.032$$\\end{document}) decrease in pain is demonstrated when walking on the novel interfaces compared to the conventional. Standing pressure data show a significant decrease in pressure on novel interfaces at the anterior distal tibia (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P = 0.012$$\\end{document}), with no significant difference at other measured locations. Thermal results show no statistically significant difference related to skin temperature. Questionnaire feedback shows improved comfort on novel interfaces on posterior and medial sides while standing and the medial side while walking. Study results support the hypothesis that the novel digital approach improves comfort outcomes compared to the evaluated conventional method. The digital interface design methodology also has the potential to provide benefits in design time, repeatability, and cost.

Antiviral Peptide-Generative Pre-Trained Transformer (AVP-GPT): A Deep Learning-Powered Model for Antiviral Peptide Design with High-Throughput Discovery and Exceptional Potency.

Traditional antiviral peptide (AVP) discovery is a time-consuming and expensive process. This study introduces AVP-GPT, a novel deep learning method utilizing transformer-based language models and multimodal architectures specifically designed for AVP design. AVP-GPT demonstrated exceptional efficiency, generating 10,000 unique peptides and identifying potential AVPs within two days on a GPU system. Pre-trained on a respiratory syncytial virus (RSV) dataset, AVP-GPT successfully adapted to influenza A virus (INFVA) and other respiratory viruses. Compared to state-of-the-art models like LSTM and SVM, AVP-GPT achieved significantly lower perplexity (2.09 vs. 16.13) and higher AUC (0.90 vs. 0.82), indicating superior peptide sequence prediction and AVP classification. AVP-GPT generated a diverse set of peptides with excellent novelty and identified candidates with remarkably higher antiviral success rates than conventional design methods. Notably, AVP-GPT generated novel peptides against RSV and INFVA with exceptional potency, including four peptides exhibiting EC50 values around 0.02 uM-the strongest anti-RSV activity reported to date. These findings highlight AVP-GPT's potential to revolutionize AVP discovery and development, accelerating the creation of novel antiviral drugs. Future studies could explore the application of AVP-GPT to other viral targets and investigate alternative AVP design strategies.

Attitude tracking of rigid-liquid-flexible coupling spacecraft by active disturbance rejection control

Considering the complex attitude dynamics of Rigid-Liquid-Flexible Coupling Spacecraft, especially when the large amplitude sloshing of liquid exists in rapid attitude maneuver, the conventional control design method based on the linearized model is not suitable for the significant coupling and strong nonlinearity. By contrast, Active Disturbance Rejection Control is model-independent and easy to design and implement. In this paper, the three main components of Active Disturbance Rejection Control, Tracking Differentiator, Extended State Observer and Nonlinear State Error Feedback are comprehensively applied to the attitude tracking control of Rigid-Liquid-Flexible Coupling Spacecraft. Firstly, two cascaded Tracking Differentiators are used to arrange the transition process of the attitude command, which not only reduces overshoot and oscillation of the tracking process, but also obtains the angular acceleration information of attitude trajectory for feed-forward compensation. Secondly, the total disturbance caused by liquid sloshing and flexible vibration is observed by Extended State Observer and compensated synchronously. Finally, the Nonlinear State Error Feedback is used to further improve the transient behavior and steady-state quality of the control system. The simulation results show that the tracking accuracy of the attitude and angular velocity using the Active Disturbance Rejection Control are about 3–27 times and 6 to 115 times that of the PID control, respectively. The convergence time, overshooting are also significantly less than the PID control.

Optimal design of deep stormwater drainage tunnels to address urban flooding in Korea

Deep stormwater drainage tunnels play a crucial role in mitigating severe urban flooding in Korea, particularly as climate change leads to more extreme weather events. These tunnels typically consist of an entrance reservoir, a circular reinforced concrete culvert with a flat slope, a downstream reservoir, and an outlet to a nearby river. In this system, the flow within the circular culvert is driven solely by the momentum of free-falling stormwater runoff. However, concerns have emerged within the Korean water resources engineering community about the drainage capacity of these tunnels, even with a substantial fall height of approximately 30 m. Intensified localized rainfall, the abrupt transition from flood waves to pressurized flow within the circular culvert, and trapped air pockets can also pose significant challenges to the tunnel’s drainage efficiency. Conventional design methods often fail to optimize the culvert’s diameter and spatial configuration, leading to suboptimal performance. In response, this study utilizes 3D numerical simulations with the interFoam solver from the OpenFOAM toolbox to address these issues. The results indicate that smaller culvert diameters—provided they do not exceed the maximum permissible flow velocity—enhance drainage capacity by reducing the impact of shock waves and stabilizing flow. These findings challenge the prevailing design practice in Korea, which holds that larger culverts inherently offer superior drainage capacity. Moreover, the simulations suggest that as culvert diameters increase, the size of trapped air pockets grows, further reducing efficiency. Although air chambers can help mitigate the retarding effects of trapped air pockets, their effectiveness diminishes if positioned near the origin of shock waves, where they risk being filled with stormwater. Based on these insights, several key recommendations are proposed: First, the design of deep stormwater tunnels should prioritize minimizing the extent of trapped air pockets, even when air chambers are used. Second, the current focus on detention capacity in design practices should be reevaluated, as excessive detention capacity may exacerbate air pocket formation. Finally, modifying the inlet channel to induce spiral flow within the entrance reservoir could reduce impulsive forces, lower maintenance costs related to armoring rocks at the reservoir bottom, and stabilize flow more quickly, thereby enhancing overall drainage capacity.

Optimization of Casing Design for Complex-Structure Wells

This study aims to address the technical challenges encountered during drilling and completion of complex-structure wells, improving the efficiency and safety of casing design. Complex-structure wells, such as horizontal wells, multilateral wells, and extended-reach wells, impose higher demands on casing design due to their intricate geological conditions. Traditional design methods, often reliant on empirical formulas and simplified assumptions, struggle to meet the evolving needs of modern drilling technology. Therefore, this research is dedicated to developing a novel optimization method for casing design in complex-structure wells to enhance drilling efficiency and economic benefits. The study employs a combination of numerical simulation and optimization algorithms. Initially, a comprehensive mathematical model is established, factoring in formation properties, drilling fluid performance, and borehole stability, to forecast drilling performance under various casing designs. Genetic algorithms are then utilized to optimize casing parameters, seeking the optimal casing design that meets safety and economic constraints. The effectiveness of the optimized solution is validated through field trial data and compared against conventional design methods. It is found that the optimized casing design significantly reduces drilling risks, accelerates drilling speed, and cuts drilling costs. Specifically, under the premise of maintaining borehole stability, the optimized scheme decreases drilling time by an average of 15% and reduces drilling costs by approximately 10%. Moreover, the proposed method exhibits strong adaptability, capable of being flexibly adjusted according to different well types and geological conditions, providing a robust technical foundation for efficient drilling of complex-structure wells.

Comprehensive design method of controller parameters for three‐phase LCL‐type grid‐connected inverter based on D‐partition

AbstractThe LCL‐type inverter is a core component in grid‐connected renewable energy systems, with its performance heavily influenced by the controller. Conventional design methods of controller parameters generally rely on approximation or trial and error, making it difficult to optimize parameters for multiple performance indices. This paper proposes a comprehensive design method of controller parameters for a three‐phase LCL‐type grid‐connected inverter based on the D‐partition method, obtaining a multi‐objective parameter stability domain of controller parameters that simultaneously satisfies multiple performance indices, such as gain margin, phase margin, and current loop bandwidth. The stability domain can be visually presented, which can avoid repetitive adjustments. Additionally, the variation trend of the stability domain when there are deviations in the LCL filter parameters is analyzed. Finally, the accuracy and effectiveness of the proposed design method are validated through simulations and experiments, achieving precise parameter design for the controller of LCL grid‐connected inverters even in the presence of deviations in filter parameters.

Efficient Inverse Design of Large-Scale, Ultrahigh-Numerical-Aperture Metalens

Efficient design methods for large-scale metalenses are crucial for various applications. The conventional phase-mapping method shows a weak performance under large phase gradients, thus limiting the efficiency and quality of large-scale, high-numerical-aperture metalenses. While inverse design methods can partially address this issue, existing solutions either accommodate only small-scale metalenses due to high computational demands or compromise on focusing performance. We propose an efficient large-scale design method based on an optimization approach combined with the adjoint-based method and the level-set method, which first forms a one-dimensional metalens and then extends it to two dimensions. Taking fabrication constraints into account, our optimization method for large-area metalenses with a near-unity numerical aperture (NA = 0.99) has improved the focusing efficiency from 42% to 60% in simulations compared to the conventional design method. Additionally, it has reduced the deformation of the focusing spot caused by the ultrahigh numerical aperture. This approach retains the benefits of the adjoint-based method while significantly reducing the computational burden, thereby advancing the development of large-scale metalenses design. It can also be extended to other large-scale metasurface designs.

Fabrication of High-Performance Asphalt Mixture Using Waterborne Epoxy-Acrylate Resin Modified Emulsified Asphalt (WEREA)

Existing research shows that using waterborne epoxy resin (WER) instead of emulsified asphalt as the binder for cold mix asphalt (CMA) can enhance the rutting resistance, high-temperature performance, fracture performance, and early performance of CMA. In order to eliminate the potential drawbacks such as insufficient strength and low-temperature performance of CMA during application, a novel method was proposed in this study for the preparation of waterborne epoxy-acrylate resin (WER), specifically tailored to modify emulsified asphalt, resulting in waterborne epoxy-acrylate resin emulsified asphalt (WEREA). The modification effect of WER on emulsified asphalt was evaluated through rheological tests and direct tensile tests. A modified design method based on the conventional Marshall design method was proposed to determine the optimal mix proportions, including the key parameters of specimen compaction and curing. The results revealed that the incorporation of WER led to a substantial improvement in the complex shear modulus and a concurrent decrease in the phase angle. When the temperature exceeded 60 °C, the phase angle exhibited a diminishing trend, indicative of a reduced viscosity as temperatures escalated. As the WER content increased, a decrease in the direct tensile strain rate was observed, accompanied by a substantial elevation in direct tensile strength. At various stress levels, the shear strain of WEREA decreases with increased content of WER, indicating that the incorporation of WER can enhance the hardness of emulsified asphalt and improve its deformation resistance. The results from MSCR tests indicate that WER could significantly improve the elasticity and hardness of emulsified asphalt, transitioning it from a viscoelastic material to an elastic material, thereby improving its deformation resistance, resistance to rutting, and high-temperature performance. The results of fatigue life are consistent with those of the amplitude sweep, both reflecting the improvement of resistance to deformation of emulsified asphalt by WER. This indicates that WER has a significant improving effect on the fatigue resistance of emulsified asphalt. Furthermore, the Marshall design tests further confirmed the advantages of WEREA in asphalt mixtures. The optimal preparation for the WEREA mixture was proposed as follows: double-sided compaction for 50 times each, aging at 60 °C for 48 h, optimal moisture content of 5.14%, cement content of 2.5%, and emulsion content of 8.4%. The optimal mix proportions identified through these tests yielded asphalt mixtures with significantly improved stability, reduced flow value, and enhanced rutting resistance compared to the hot-mix asphalt mixture (HMA) of AC-16. These findings suggest that WEREA has the potential to significantly enhance the durability and longevity of asphalt pavements. For future applications, it can be explored for use in producing cold recycled asphalt mixtures. In addition to designing the WEREA mixture according to AC-16 gradation, consideration can also be given to using a gradation with a smaller nominal maximum aggregate size for the application in the surface layer or ultra-thin wearing course.

Seismic response prediction for buildings with sliding live loads using neural networks

The seismic response of primary structures (PS) can be significantly influenced by live loads, particularly when these loads consist of stacked bodies that are capable of sliding during seismic events. Conventional design methods often fail to account for the energy dissipation resulting from such sliding, leading to overly conservative structural estimates. This study examines the effects of sliding live loads on the seismic response of a PS, where two rigid bodies, referred to as secondary bodies (SBs), are stacked as live loads on a PS. The interaction between the lower SB and the PS, as well as between the SBs, is modelled using Coulomb's friction model. The governing equations are solved using the fourth-order Runge-Kutta method. Seismic Zones III and V from IS 1893:2016 are considered as hazard levels. A parametric investigation explores the impact of varying dynamic parameters of both the PS and SBs on the seismic response. The results demonstrate significant energy dissipation within the stack due to sliding, which, if not considered, results in conservative displacement estimates. The study also evaluates how friction coefficients, mass ratios, and excitation levels affect the stack's energy dissipation capacity. A novel method to compute the modified primary structural period (Tnew) for design purposes is proposed, and an artificial neural network (ANN) model is developed, achieving a high prediction accuracy (R2 = 99 %). Sensitivity analysis is performed to assess the influence of input parameters on the output. This research highlights the need to include sliding loads in seismic design.

Research on autocannon firing dispersion based on bond space method

This paper is devoted to constructing a dynamic numerical model for the firing dispersion caused by the autocannon dynamic characteristics. The buffer dynamics and projectile-barrel coupling are considered. First, the simulation of autocannon firing dispersion using commercial software usually leads to expensive computational costs. Second, autocannon dynamic design includes multiple subsystem models with nonlinearities. The conventional design method makes it difficult to describe the dynamic response of such complex systems with a unified model. To end these, a dynamic junction bond space method is proposed for analyzing muzzle vibration and firing dispersion under continuous firing loads, where the gene expression programming (GEP) method is adopted to construct the surrogate model for the buffer flow field. For the coupling analysis of the projectile and barrel, the projectile load is applied at a moving junction, which coincides with the flexible node of the barrel. By this, the dynamic numerical model for autocannon firing dispersion is established, and then the system state equation is obtained for each time step. Moreover, an autocannon standing target shooting example is presented to demonstrate the validity of the proposed method; the results show that the firing dispersion from the bond space model is consistent with the test.

Optimal design and performance evaluation of grinding wheels with triply periodic minimal surface lattice structure

High-performance grinding wheels are critical tools for precision surface generation. In precision grinding processes, the coolant supply at the wheel-workpiece interface is critical for reducing grinding temperature and detrimental friction. For better lubrication and chip removal performance, the cavities or pores need to be generated throughout the grinding wheel. The ideal morphology of pores in grinding wheels should be inter-connected and capable of providing sufficient coolant. Conventional grinding wheel design and fabrication methods can only passively generate closed circular pores by using pore-forming agents. Increasing the percentage of pores in this way generally leads to an uncontrollable reduction in mechanical strength, while the closed pores are insufficient in cooling performance. In this paper, the grinding wheels with triply periodic minimal surface lattice structure are designed and fabricated, which achieves the regulable and inter-connected internal structure of grinding wheels. Meanwhile, to balance the relationship between the structural properties and performance indicators of porous grinding wheels, an optimal design method for the porous structure of grinding wheels is proposed. Finally, the grinding performance of the novel grinding wheels in comparison to electroplated diamond grinding wheels is investigated in terms of grinding force, specific grinding energy and ground surface roughness.

Optimization and Evaluation of Accelerated Corrosion Tests Based on Mechanism Equivalence Principles.

Conventional indoor corrosion test design methods primarily focus on the rapid evaluation of material corrosion resistance, often neglecting the impact of environmental stress levels on the equivalence of corrosion mechanisms. This study introduces a novel indoor corrosion test design method based on the principle of corrosion mechanism equivalence, aimed at improving the accuracy of indoor accelerated corrosion simulations. We define the characteristic of corrosion mechanism equivalence as the Corrosion Mechanism Equivalence Degree (CMed), which quantifies the similarity between corrosion mechanisms in indoor accelerated tests and field tests. Then, modified conventional link function models are defined, integrating the probability distribution of environmental factors to estimate corrosion model parameters more precisely. Finally, an optimization problem is constructed for accelerated corrosion tests based on CMed, incorporating constraints on environmental stress levels and acceleration factors. A case study demonstrates the proposed method's ability to accurately simulate the actual service environment of materials, determining the appropriate stress levels for indoor accelerated corrosion tests while ensuring the desired acceleration factor and corrosion mechanism equivalence.

Response of group piles subjected to coupled vertical-eccentric lateral-seismic loads

In seismic prone regions, analyzing loaded pile groups can be challenging because of limitations in conventional design methods and the inability of small-scale laboratory tests to accurately replicate the real behavior. Studying how pile group response is affected by the combined impact of vertical, concentric, and seismic loads is challenging. One of the limitations pertains to the intricacy of scaling up experiments, particularly in creating large-scale laboratory models. Thus, a numerical analysis may be appropriate for capturing the response. This study aimed to understand the pile group response in dense sand subjected to vertical, eccentric lateral, and seismic loadings via a large-scale 3D nonlinear finite element model. The validation results indicate that the numerical approach accurately predicts the behavior of pile groups in dense sand. Then, two different layouts of pile groups are analyzed under various loading scenarios on the basis of the recorded data from the El Centro earthquake. The results revealed that the number and arrangement of piles significantly affect the induced settlement. Compared with the 5-pile groups, the 3-pile groups presented higher maximum bending moments under vertical loading. However, the response of the 5-pile groups varied, as they experienced higher bending moment values without vertical loading. The peak lateral soil pressures were higher in the 3-pile groups. Some significant variations appeared in the induced torque resistances when the 5-pile groups were compared with the 3-pile groups. This study demonstrated the reliability of the used numerical approach for analyzing the response of pile groups in dense sand, as an alternative to laboratory test models.

A Dataset Generation Framework for Symmetry-Induced Mechanical Metamaterials

Abstract The surge in machine learning research and recent advancements in 3D printing technologies have significantly enriched materials science and engineering, particularly in the domain of mechanical metamaterials, which commonly consist of periodic truss materials. Despite the extensive exploration of their tailorable properties, truss-based metamaterial design has predominantly adhered to cubic and orthotropic unit-cells, a limitation arising from the conventional design method, where the type of symmetry related to the designed truss-based material is determined after the design process is done. To overcome this issue, this work introduces a groundbreaking 3D truss material designing framework that departs from this constraint by employing six distinctive material symmetries (cubic, hexagonal, tetragonal, orthotropic, trigonal, and monoclinic) within the design process. This innovative approach represents a versatile paradigm shift compared to previous design approaches. Furthermore, we are able to integrate anisotropy into the design framework, thus enhancing the property space exploration capability of the proposed design framework. Probing materials property space using our design framework demonstrates its capacity to achieve a diverse range of mechanical properties, surpassing even the most extensive datasets available in the literature. The proposed method facilitates the generation of a comprehensive truss dataset, which can be represented in a trainable continuous format suitable for machine learning and data-driven approaches. This advancement paves the way for the development of robust inverse design tools for truss materials, marking a significant contribution to the mechanical metamaterial community.