Structure Design Research Articles

Design and Analysis of Surface-Mounted Permanent-Magnet Field-Modulation Machine for Achieving High Power Factor

Along with the establishment of general air-gap field modulation theory, a new type of field-modulation machine has attracted increasing attention. However, the low power factor always plagued its popularization. This paper is to focus on the simplest structure surface-mounted permanent-magnet field-modulation (SMPMFM) machine, aiming at exploring the implementation of high power-factor design. The key is that the power factor calculation formula is derived parametrically, hence the originally vague causal relationship between the structural parameters and power factor become clear, guided by which the initial structure design for high power factor achievement is determined, meanwhile the power factor adjustment evaluation and the electromagnetic performance comparison with the help of finite element analysis (FEA) are conducted to verify the validity of the proposed power factor calculation formula. Finally, a feasible high-power-factor SMPMFM prototype is fabricated and the experimental testing is performed, which has a good agreement with the formular calculation and FEA results. Hence, the validity of the proposed analysis for achieving high power-factor design of field-modulation machines is verified.

Mode-locked figure-9 fiber laser assisted by a SESAM for improving signal-to-noise ratio and stability

The mode-locked figure-9 fiber laser has been proved to exhibit excellent performance, but improvement of its output signal-to-noise ratio(SNR) and stability are restricted by the laser structure design. In this paper, we propose and verify a modified laser cavity to increase SNR and output stability by introducing a saturable absorber mirror(SESAM) to the single arm end of the figure-9 laser as a mode-locked auxiliary device instead of the typical mirror. Numerical simulations based on the model of the figure-9 fiber and SESAM lasers clearly indicate that modified structure has better output performance than typical one. Additionally, the related experimental results also demonstrate that at the same pump power of 150mW and compare the typical structure, the modified one outputs a higher SNR of 70 dB, together with 12 h output lower RMS of 0.23 %. We believe that the presented laser design could be a stable and reliable ultrafast optical seed for improving the performance of high-power laser facilities, which also has potential applications in optical frequency comb and all-optical modulation.

Design and Development Chairless Chair

Abstract: It is very difficult to stand and work for overall shift in the company by a worker. This will reduce the efficiency of the worker. The solution to this problem is to have a portable device which has an ergonomic design, low cost exoskeletons. In this work a mechanical ergonomics device that is designed around the shape and function of the human body, with segments and joints corresponding to those of the person it is externally coupled with. It functions as a chair whenever it is needed and is coined as Chair less Chair. Worker in industrial can wear it on legs like an exoskeleton. It locks into place and you can sit down on it. The device never touches the ground, which makes it easier to wear: a belt secures it to the hips and it has straps that wrap around the thighs. These are specially designed and part of the mechanism, but an alternate version works with any footwear and touches the ground only when in a stationary position. The user just moves into the desired pose. It will fit closely to lower part of the body as an external body part on which maximum body forces act upon. It is a cost effective product and any error in design may fail the structure which creates loss. So, these forces should be carefully analyzed during the design of structure. The best way to predict these forces during pre-manufacturing stage is to make an analysis on the structure with the help of software. This helps in estimating the stresses induced on the structure which is one of the most important criteria for evaluation of the model.

Reduced Volume Expansion of Micron‐Sized SiOx via Closed‐Nanopore Structure Constructed by Mg‐Induced Elemental Segregation

AbstractThe inherently huge volume expansion during Li uptake has hindered the use of Si‐based anodes in high‐energy lithium‐ion batteries. While some pore‐forming and nano‐architecting strategies show promises to effectively buffer the volume change, other parameters essential for practical electrode fabrication, such as compaction density, are often compromised. Here we propose a new in situ Mg doping strategy to form closed‐nanopore structure into a micron‐sized SiOx particle at a high bulk density. The doped Mg atoms promote the segregation of O, so that high‐density magnesium silicates form to generate closed nanopores. By altering the mass content of Mg dopant, the average radii (ranged from 5.4 to 9.7 nm) and porosities (ranged from 1.4 % to 15.9 %) of the closed pores are precisely adjustable, which accounts for volume expansion of SiOx from 77.8 % to 22.2 % at the minimum. Benefited from the small volume variation, the Mg‐doped micron‐SiOx anode demonstrates improved Li storage performance towards realization of a 700‐(dis)charge‐cycle, 11‐Ah‐pouch‐type cell at a capacity retention of >80 %. This work offers insights into reasonable design of the internal structure of micron‐sized SiOx and other materials that undergo conversion or alloying reactions with drastic volume change, to enable high‐energy batteries with stable electrochemistry.

An intelligent body frame structure modeling and optimization system based on conceptual design

The conceptual design stage is an indispensable and important stage in the development of the modern body. However, due to the lack of sufficient body structure parameter support, the design defects generated at this time are difficult to make up for in the subsequent design stage. Therefore, it is of great significance to develop a conceptual design software for body structure that integrates design, analysis, and optimization. This paper proposes an intelligent body frame structure modeling and optimization system based on conceptual design, S-iVCD (Intelligent System for Conceptual Design of Vehicle Body Structure). Based on deep learning methods and body design sketches, a conceptual model of body structure is quickly established. Based on the data parameters stored in the Excel table, the system is seamlessly connected to the domestic independent finite element software SIPESC to calculate the simulated working conditions. The body structure optimization module is driven by the MMA (method of moving asymptotes), and takes the body structure performance and total mass as the optimization goals or constraints of the mathematical model to achieve body structure performance improvement and lightweight design.

Molecular Engineering for Modulating Photocatalytic Hydrogen Evolution of Fully Conjugated 3D Covalent Organic Frameworks.

Covalent organic frameworks (COFs) have recently shown great potential for photocatalytic hydrogen production. Currently almost all reports are focused on two-dimensional (2D) COFs, while the 3D counterparts are rarely explored due to their non-conjugated frameworks derived from the sp3 carbon based tetrahedral building blocks. Here, we rationally designed and synthesized a series of fully conjugated 3D COFs by using the saddle-shaped cyclooctatetrathiophene derivative as the building block. Through molecular engineering strategies, we thoroughly discussed the influences of key factors including the donor-acceptor structure, hydrophilicity, specific surface areas, as well as the conjugated/non-conjugated structures on their photocatalytic hydrogen evolution properties. The as-synthesized fully conjugated 3D COFs could generate the hydrogen up to 40.36 mmol h-1 g-1. This is the first report on intrinsic metal-free 3D COFs in photocatalytic hydrogen evolution application. Our work provides insight on the structure design of 3D COFs for highly-efficient photocatalysis, and also reveals that the semiconducting fully conjugated 3D COFs could be a useful platform in clear energy-related fields.

Mesoscale modelling on shear behavior of RC beams at low temperature: Influences of structural size and shear span-to-depth ratio

The low-temperature brittle failure of materials could aggravate the brittle characteristics of shear failure of RC beams, which urgently needs scientific research. This paper aims to investigate the shear behavior and corresponding size effect of reinforced concrete (RC) beams at low temperature by numerical analysis. Firstly, a two-stage thermo-mechanical coupled mesoscale simulation method was developed, which considered the meso-structure characteristics of concrete as well as the ice-strengthening effect. Based on the mesoscale simulation method validated through a series of tests, the progressive shear failure process of RC beams at room and low temperatures was captured. The influences of temperature (T = 20, −30, −60 and −90 °C), structural size (cross-sectional height H = 300, 600, and 1200 mm) and shear span-to-depth ratio (a/h0 = 1.0, 1.6, and 2.3) on key indexes of shear behavior were quantitatively analysed. The results indicate that compared to that at room temperature, the shear failure process of RC beams at low temperature is more rapid, exhibiting more obvious brittle characteristic. When the temperature drops from 20 °C to −90 °C, the nominal shear strength of RC beams is enhanced with a maximum increase of 57.0 % (for H = 300 mm) while the mid-span displacement is reduced with a maximum decrease of 46.8 % (for H = 300 mm). Compared to that at room temperature, the size effect on nominal shear strength of RC beams at low temperature is enhanced, with a maximum increase of 20.4 %. Finally, considering the influences of low temperature and shear span-to-depth ratio on the nominal shear strength, an improved size effect analytical model for was established, which can predict the shear capacity of different sized RC beams at low temperatures. This paper can provide a reference for the safe application of reinforced concrete structures in low temperature environments, and lay a foundation for the formulation of reinforced concrete structure design specifications at low temperature.

Bridging the gap between academic research and industrial development in advanced all-solid-state lithium-sulfur batteries.

The energy storage and vehicle industries are heavily investing in advancing all-solid-state batteries to overcome critical limitations in existing liquid electrolyte-based lithium-ion batteries, specifically focusing on mitigating fire hazards and improving energy density. All-solid-state lithium-sulfur batteries (ASSLSBs), featuring earth-abundant sulfur cathodes, high-capacity metallic lithium anodes, and non-flammable solid electrolytes, hold significant promise. Despite these appealing advantages, persistent challenges like sluggish sulfur redox kinetics, lithium metal failure, solid electrolyte degradation, and manufacturing complexities hinder their practical use. To facilitate the transition of these technologies to an industrial scale, bridging the gap between fundamental scientific research and applied R&D activities is crucial. Our review will address the inherent challenges in cell chemistries within ASSLSBs, explore advanced characterization techniques, and delve into innovative cell structure designs. Furthermore, we will provide an overview of the recent trends in R&D and investment activities from both academia and industry. Building on the fundamental understandings and significant progress that has been made thus far, our objective is to motivate the battery community to advance ASSLSBs in a practical direction and propel the industrialized process.

Neutronic Analysis of the Conceptual Molten Uranium Breeder Reactor Using MCNP and SCALE Tools

The Molten Uranium Breeder Reactor (MUBR) is a radically new reactor concept with a mixed-energy spectrum. MUBR is fueled with molten uranium metal in large-diameter fuel tubes and is cooled by circulating molten uranium fuel through a heat exchanger. The reactor has heavy water as moderator, and the reactivity of the reactor is primarily controlled by the voiding effect of the moderator through an innovative control cavity structure design. Because the MUBR design is vastly different from most existing fission reactors, neutronics analysis must be performed for many different combinations of design parameters to identify viable and optimum design configurations. To facilitate the neutronics analysis, a proprietary program called MUBR6gen is being developed to provide a pipeline tool to expedite the process. MUBR6gen employs two well-established neutronics codes, i.e., MCNP and SCALE, to perform standard neutronics calculations for MUBR by automating input preparation and output processing. In addition, MUBR6gen ensures consistency of the MCNP and SCALE inputs and compares the outputs of the two codes to warrant the simulation results. Augmented with MUBR6gen, standard neutronics analysis was carried out on a small-scale MUBR design, which serves as a model problem in the paper. The neutronics performance characteristics of the model reactor were obtained and discussed in a code-to-code pattern. An overall very good agreement between the results of the two neutronics codes was established. Based on the success of the model problem analysis, further neutronics analysis using MUBR6gen was extended for a set of MUBR variant designs. Meaningful and promising fuel cycle analysis results for the 10 different designs were achieved and discussed. These results are used to identify the best MUBR candidates in terms of fuel lifetime and utilization efficiency for future applications.

The investigation of flame-retardant fiber mats for high performance composites: flame retardancy and structure performance

The flame-retardant performance of carbon fiber reinforced composites serves as a critical metric for structural stability. Nonetheless, the prevalent methodologies for improving the flame retardancy of composites struggle to reconcile the dual objectives of flame retardancy and mechanical robustness, due in part to the constraints imposed by the conventional additive-based approach on the material interface. This study introduced a novel method involving a glass fiber mat, which was augmented with a polyurethane-based treatment integrated with flame-retardant substances, in particular ammonium polyphosphate and nickel hydroxide. This fiber mat was strategically applied to the composite surface, conferring both flame retardancy and enhanced structural resilience. The structure performance and flame retardancy of composites were characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, and the cone calorimeter test. Experimental comparisons with nontreated controls indicated that the innovative composites exhibited a reduction in total heat release and total smoke production by 13.7% and 18.8%, respectively. Concurrently, a notable enhancement in mechanical properties was observed, with increases of 20.9% and 23.1% for tensile and flexural strength. This well-balanced performance is attributable to the structure design, with toughened glass fiber mats to protect the composite surfaces from structural failure, and flame-retardant agent composition for combustion resistance and smoke suppression. Consequently, the proposed integrative flame-retardant structural design, enriched with specific flame-retardant treatments, offers a promising avenue for fabricating high-performance composite materials with potential utility in the aviation and aerospace sectors.

Structured mesh material with gradient surface wettability for high-power-density proton exchange membrane fuel cells

ABSTRACT A compact cell structure is highly desirable for next-generation high-power-density proton exchange membrane (PEM) fuel cell (>9.0 kW L−1). A novel integrated bipolar plate (BP) – gas diffusion layer (GDL) structure utilizing porous material is a promising configuration, which not only improves output power due to the reduced transport resistance but also reduces cell thickness. Herein, we propose a structured mesh material with gradient surface wettability for this cell structure, which is characterized by an ordered skeleton in the three-dimensional (3D) space with high structure design flexibility. It is experimentally found that the structured mesh material shows higher performance than serpentine and parallel flow fields. Meanwhile, 3D modeling work on an automobile fuel cell (cell area: 245.76 cm2) with the distribution zones of dot matrix structure for hydrogen, air and coolant flow, is conducted, and it is found that the structured mesh material effectively decreases the concentration and electric ohmic losses in comparison with the traditional “BP/flow field + GDL” structure, improving power density with uniform distribution characteristics. Moreover, the gradient wettability on the mesh surface helps promote the water detach the GDL and hold the residual liquid water in the top region, optimizing the water management.

Experimental study and refined numerical simulation of ultimate pressure-bearing performance of rope-reinforced airship envelope structures

Stratospheric airships require a lightweight envelope to contain lighter-than-air buoyancy gas, making the lightweight design and pressure-bearing performance of the envelope structure a key research issue. The stress state at different cross-sectional positions of the airship envelope structure is different, resulting in a low utilization rate of the overall material performance of the envelope structure. This paper proposes a design scheme for reinforcing envelope structures with sliding reinforcing cable to improve the bearing capacity of the composite fabric structure while reducing its weight, ultimately achieving the optimal strength-to-weight ratio. Two types of composite fabric structures (A-airship and B-airship) were subjected to inflatable burst tests, and the strain changes in the envelope gores were analyzed by digital image correlation. Through re-assembly of the broken composite fabric pieces and analysis of their tear textures, crack origination positions, failure causes, and the stress behavior and state at the failure position were identified. An envelope structural model with consideration of the cutting pattern effect was established, allowing the stress distribution of the envelope to be analyzed and the damage positions to be more accurately predicted. Based on the analysis of the ultimate pressure-bearing performance of an airship envelope structure, a novel idea of incorporating coupled tensile-shear stress into the strength criterion was proposed. Through the data in the study and existing references, it is verified that the strength criterion can accurately predict the ultimate pressure-bearing performance of the envelope structure.

Nacre-Mimetic Structure Multifunctional Ion-Conductive Hydrogel Strain Sensors with Ultrastretchability, High Sensitivity, and Excellent Adhesive Properties.

Recently, conductive hydrogels have emerged as promising materials for smart, wearable devices. However, limited mechanical properties and low sensitivity greatly restrict their lifespan. Based on the design of biomimetic-layered structure, the conductive hydrogels with nacre-mimetic structure were prepared by using layered acrylic bentonite (AABT) and phytic acid (PA) as multifunctional "brick" and "mortar" units. Among them, the unique rigid cyclic multihydroxyl structure of the "organic mortar" PA preserves both ultrastretchability (4050.02%) and high stress (563.20 kPa) of the hydrogel, which far exceeds most of the reported articles. Because of the synergistic effect of AABT and PA, the hydrogel exhibits an excellent adhesive strength (87.74 kPa). The role of AABT in the adhesive properties of hydrogels is proposed for the first time, and a general strategy for improving the adhesive properties of hydrogels by using AABT is demonstrated. Furthermore, AABT provides ion channels and PA ionizes abundant H+, conferring a high gauge factor (GF = 14.95) and excellent antimicrobial properties to the hydrogel. Also, inspired by fruit batteries, simple self-powered flexible sensors were developed. Consequently, this study provides knowledge for functional bentonite filler modified hydrogel, and the prepared multifunctional ionic conductive hydrogel shows great application potential in the field of intelligent wearable devices.

Kirigami-inspired, three-dimensional piezoelectric pressure sensors assembled by compressive buckling

Piezoelectric sensors whose sensing performances can be flexibly regulated hold significant promise for efficient signal-acquisition applications in the healthcare field. The existing methods for regulating the properties of polyvinylidene fluoride (PVDF) films mainly include material modification and structural design. Compared to material modification, which has a long test period and an unstable preparation process, structural design is a more efficient method. The irigami structure combined with compressive buckling can endow the flexible film with rich macrostructural features. Here, a method is fabricated to modulate the sensing performance by employing distinct 3D structures and encapsulation materials with varying Young’s moduli. The relationship among the aspect ratio (α), pattern factor (η), elastic modulus of encapsulation materials, and equivalent stiffness is obtained by finite element simulation, which provides theoretical guidance for the design of the 2D precursor and the selection of encapsulation materials. In the demonstration applications, the sensor accurately captures pulse waveforms in multiple parts of the human body and is employed for the pressure monitoring of different parts of the sole under various posture states. This method of structure design is efficient, and the preparation process is convenient, providing a strategy for the performance control of piezoelectric pressure sensors.

Synchronous Control of High-Speed Train Lift Wing Angle of Attack Drive System Based on Chaotic Particle Swarm Optimization and Linear Auto-Disturbance Resistant Controller

In this study, a control scheme is proposed based on Chaotic Particle Swarm Optimization (CPSO) to enhance the Linear Auto-Disturbance Rejection Controller (LADRC). The focus is on addressing the challenge of high-precision variations in angle-of-attack through dual-motor cooperative control within the lifting wing of a high-speed train. The scheme initiates with the design of a dual-loop structure for LADRC, integrating position and current control. The position loop is further refined. Subsequently, the CPSO algorithm is employed to optimize the parameters of the LADRC controller. Ultimately, the loop is closed by feeding back the position error in the cross-coupled structure to the current loop, thereby achieving high-precision control. The performance of the proposed structure is validated through both Matlab/Simulink simulations and an experimental platform. The experimental results demonstrate that CPSO-LADRC, in comparison to traditional LADRC and Proportion-Integration-Differentiation (PID) control, exhibits an increase in the maximum response time by 3.76 s and 3.3 s, respectively, a reduction in overshoot by 1.12% and 0.8%, as well as a decrease in the maximum synchronization error by 0.45 cm and 1 cm, respectively. These findings validate the effectiveness of the proposed synchronous loop controller method in simplifying computational complexity, enhancing system responsiveness, robustness, and synchronization performance. Additionally, our approach facilitates precise angle-of-attack transformation for the lifting wings of high-speed trains effectively.

Univariate versus multivariate flood frequency analysis in tropical region: Employing two classes of hydrological models

AbstractFlood frequency analysis is critical in flood planning and management and hydraulic structures design. While univariate flood frequency analysis (using the peak flow) is still widely employed in developing countries, how does it compare to the robust copula‐based bivariate flood frequency analysis remains unknown. Moreover, there is also a decade‐long critical question whether less data requiring hydrological models can be an alternate to the data‐intensive models in flood prediction, especially in a developing tropical country like India? To answer these questions, this study aims in comparing two types of hydrological models (IHACRES, a less data requiring model, and VIC‐3L, a data‐intensive model) in simulating the peak flows, following which the simulated peak flows are used in a detailed comparison of the univariate and bivariate flood frequency analysis. The results indicate that the data‐intensive fully distributed hydrological model performs poorly relative to the conceptually lumped IHACRES model at the study catchment in simulating the peak flows. Moreover, both univariate and copula‐based bivariate flood frequency analyses show similar peak flows for a given return period at the study catchment. Given that bivariate flood frequency analysis accounts for both peak flow and flood volume, it is recommended over the univariate flood frequency analysis since the results are widely applicable for flood planning and hydraulic structure designing the developing countries.

A Robust Hydrogel Electrolyte with Ultrahigh Ion Transference Number Derived from Zincophilic “Chain‐Gear” Network Structure for Dendrite‐Free Aqueous Zinc Ion Battery

AbstractAs a promising energy storage device, an aqueous zinc ion battery (AZIB) still suffers dendrite growth, hydrogen evolution, and corrosion. Hydrogel electrolyte solves the above issues to some extent. Nevertheless, the mechanical properties of most hydrogel electrolytes are not competitive enough to meet booming demand for flexible electronics. In this work, a robust “chain‐gear” hydrogel electrolyte (PM‐HE) crosslinked by polymeric micelles (PMs) is constructed, in which PMs serve as “gears” and form “chain‐gear” structure with polyanion chains. Specifically, “gears” support molecular chains, constructing hierarchically porous structures and opening up paths for Zn2+. Apart from as crosslinkers, PMs exist competitive mechanism with polyanion chains to promote ion decoupling. Such a “chain‐gear” structure can realize desolvation and accelerate ion transport. Thereby, PM‐HE possesses excellent ionic conductivity (60.6 mS cm−1) and ultrahigh transference number (0.88). Symmetrical cells can realize stable cycling over 1500 h with Zn uniform deposition. Remarkably, PM‐HE has excellent tensile (0.23 MPa) and compressive (11.3 MPa) properties profited from strengthening and toughening effect of PMs. The flexible battery can supply power stably under harsh conditions. This work proposes the strategy of constructing an all‐around hydrogel electrolyte based on reasonable design in network structure, providing more possibilities for the practical application of flexible AZIB.

Modulation of Phase Separation Morphology by Configuration Engineering in Bulk Heterojunction Organic Solar Cells

For bulk‐heterojunction organic solar cells (OSCs), molecular structure design to control molecular stacking is crucial to obtain ideally phase‐separated morphology and high device performance. Herein, the investigation focuses on two polythiophene‐quinoxaline (PTQ) derivatives (PTQ8 and PTQ10) blended with Y6, utilizing coarse‐grained molecular dynamics simulations based on the Lennard–Jones static potential (LJSP) method. The study reveals that the diminished photovoltaic efficiencies of PTQ8:Y6 blends, compared to PTQ10:Y6 blends, are not solely attributed to reduced driving forces. The introduction of fluorine‐substituted sites in the thiophene group of PTQ polymer is identified as a significant factor. This alteration causes PTQ polymers in PTQ8:Y6 blends to coil, compromising the crystalline structure. PTQ8's bifluorine group induces a repulsive effect on the quinoxaline group, leading to a coiled‐chain structure that hinders chain stacking. Conversely, PTQ10 exhibits a straighter chain conformation. Additionally, PTQ8's high solubility in chloroform prevents effective aggregation, further impeding suitable morphology formation. Coarse‐grained simulations employing LJSP prove effective in precisely exploring the morphology of OSCs, offering crucial insights for materials in this field.

Calculation of the mechanical properties of high‐performance concrete employing hybrid and ensemble‐hybrid techniques

AbstractThis study aims to execute machine learning methods to predict the mechanical properties containing TS and CS of HPC. They are essential parameters for the durability, workability, and efficiency of concrete structures in civil engineering. In this regard, obtaining the estimation of the mechanical properties of HPC is complex energy and time‐consuming. Due to this, an observed database was compiled, including 168 datasets for CS and 120 for TS. This database trained and validated two machine learning models: SVR and RT. The models combine the prediction outputs from the meta‐heuristic algorithms to build hybrid and ensemble‐hybrid models, which include dwarf mongoose optimization, PPSO, and moth flame optimization. According to the observed outputs, the ensemble models have great potential to be a recourse to deal with the overfitting problem of civil engineering, thus leading to the development of more supportable and less polluting concrete structures. This research significantly improves the efficiency and accuracy of predicting vital mechanical properties in high‐performance concrete by integrating machine learning and metaheuristic algorithms, offering promising avenues for enhanced concrete structure design and development.

Predicting Rutting Development Using Machine Learning Methods Based on RIOCHTrack Data

As the main cause of asphalt pavement distress, rutting severely affects pavement safety. Establishing an accurate rutting prediction model is crucial for asphalt pavement maintenance, pavement structure design, and pavement repair. This study explores five machine learning methods, namely Support Vector Regression (SVR), Artificial Neural Network (ANN), Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Extra Trees, to predict the development of rutting depth using data from RIOHTRack. The model’s performance is measured by comparing the performance evaluation indicators of different models, such as the coefficient of determination, root mean square error, mean absolute error, and mean absolute percentage error. The results demonstrate that integrated learning techniques such as RF, GBDT, and Extra Trees works best with R2 = 0.9761, 0.9833, and 0.9747. Moreover, the GBFT model can capture the trend of the measured rutting progression curve better than the mechanistic-empirical (M-E) model. The analysis of feature importance reveals that, in addition to external factors such as temperature and axle load, the aggregate of the asphalt concrete layer and air void crucially affect rutting. The higher the base strength, the smaller the rutting depth. The proposed model is highly straightforward and serves as an accessible analysis tool for engineers in practice.