Transportation Engineering最新文献_第3页

Conceptualization of an electromagnetic automatic transmission via mathematical modeling for vehicle drive-train applications 电磁自动变速器在汽车传动系统应用中的数学建模概念化

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-11 DOI: 10.1016/j.treng.2025.100398

Garbe Chukulu , Rajendiran Gopal , Ramesh Babu Nallamothu , Gang Gyoo Jin

This research presents a comprehensive investigation of electromagnetic gearing systems for automotive powertrain applications, evaluating their potential to replace conventional mechanical automatic transmissions. Three fundamental electromagnetic architectures - Variable Volume Approach (VVA), Fixed Volume Approach (FVA or separate drive of individually magnetized ring), and Fixed Volume Approach Pole-based (FVAP) - are analyzed through five distinct design configurations employing harmonic and geometric sequencing. A rigorous analytical framework is developed to assess electromagnetic coupling dynamics, evaluating critical parameters including energy density, volumetric constraints, magnetic flux distribution, and torque transmission characteristics. Performance assessment using Gray Relational Analysis (GRA) reveals the FVAP configuration as superior (GRA grade: 0.95), demonstrating optimal torque density (3.01 MNm/m³ FEM) and specific torque (320-344 Nm/kg). Comparative analysis shows electromagnetic systems achieve 20–22 % higher specific torque than optimal range of mechanical planetary gears about (267–282 Nm/kg), though their volumetric torque density remains below conventional systems (3–5 MNm/m³). The study identifies key performance trade-offs: VVAH (0.72) and VVAG (0.68) deliver competitive torque but exhibit elevated flux leakage, while FVAG (0.58) and FVAH (0.64) show balanced but limited performance. The research highlights electromagnetic gearing as a transformative technology for weight-sensitive applications, offering inherent advantages including contactless operation, adaptive torque control, and reduced maintenance. Findings suggest these systems are particularly suitable for electric and hybrid vehicle architectures where power density and efficiency are critical. Future development pathways focus on advanced magnetic materials and superconducting implementations to bridge the volumetric performance gap. This work provides a foundational framework for next-generation transmission systems, establishing quantitative performance benchmarks and design guidelines for electromagnetic gearing in automotive applications.

这项研究提出了一个全面的调查电磁传动系统的汽车动力总成应用，评估其潜力，以取代传统的机械自动变速器。三种基本的电磁结构-可变体积方法（VVA），固定体积方法（FVA或单独磁化环的单独驱动）和基于固定体积方法极点(FVAP) -通过采用谐波和几何排序的五种不同设计配置进行分析。开发了一个严格的分析框架来评估电磁耦合动力学，评估关键参数包括能量密度、体积约束、磁通分布和扭矩传递特性。使用灰色关联分析（GRA）进行性能评估，结果显示FVAP配置更优（GRA等级：0.95），显示出最佳扭矩密度（3.01 MNm/m³FEM）和比扭矩（320-344 Nm/kg）。对比分析表明，电磁系统的比扭矩比机械行星齿轮的最佳范围（267-282 Nm/kg）高20 - 22%，但其体积扭矩密度仍低于传统系统（3-5 MNm/m³）。该研究确定了关键的性能权衡：VVAH（0.72）和VVAG（0.68）提供有竞争力的扭矩，但表现出更高的磁通泄漏，而FVAG（0.58）和FVAH（0.64）表现出平衡但有限的性能。该研究强调，电磁传动是一项对重量敏感应用的变革性技术，具有非接触式操作、自适应扭矩控制和减少维护等固有优势。研究结果表明，这些系统特别适用于功率密度和效率至关重要的电动和混合动力汽车架构。未来的发展路径集中在先进的磁性材料和超导实现上，以弥合体积性能差距。这项工作为下一代传动系统提供了基础框架，为汽车应用中的电磁传动装置建立了定量性能基准和设计指南。

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引用次数: 0

Blended wing body designs for aerodynamic, stability, and control optimization: A comprehensive review 用于气动、稳定性和控制优化的混合翼身设计：综合综述

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-10 DOI: 10.1016/j.treng.2025.100397

Ussama Ali , Mohamed Okasha , Tarek N. Dief , Mostafa S.A. ElSayed , Mohamed M. Kamra

Blended wing body (BWB) aircraft design represents a transformative innovation in aerospace engineering, seamlessly integrating aerodynamic, structural, and propulsion advancements to achieve unprecedented efficiency and sustainability. This comprehensive review highlights the unique aerodynamic features of BWB configurations, including their superior lift-to-drag ratio, enhanced payload capacity, and reduced fuel consumption, offering a viable pathway to decarbonizing aviation. The study examines critical aspects of stability, control, and propulsion integration, addressing challenges such as the absence of traditional stabilizers, dynamic coupling of control axes, and manufacturing complexity. By leveraging multidisciplinary optimization frameworks, advanced computational tools, and smart material innovations, BWB designs are shown to hold promise for diverse applications, from commercial aviation to military and UAV systems. This review highlights the importance of future research in overcoming scalability, regulatory, and structural challenges to unlock the full potential of BWB technology.

混合翼身（BWB）飞机设计代表了航空航天工程的革命性创新，无缝集成了空气动力学、结构和推进技术，实现了前所未有的效率和可持续性。这项全面的评估强调了BWB配置独特的空气动力学特征，包括其优越的升阻比、增强的有效载荷能力和降低的燃油消耗，为航空脱碳提供了一条可行的途径。该研究考察了稳定性、控制和推进一体化的关键方面，解决了传统稳定器缺失、控制轴动态耦合以及制造复杂性等挑战。通过利用多学科优化框架、先进的计算工具和智能材料创新，BWB设计被证明具有从商业航空到军事和无人机系统等多种应用的前景。这篇综述强调了未来研究在克服可扩展性、监管和结构挑战方面的重要性，以释放BWB技术的全部潜力。

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引用次数: 0

Comparative study of the drag coefficient of NACA airfoil profiles as applied to vehicles using different reference areas NACA翼型阻力系数的比较研究，适用于不同参考区域的车辆

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-10 DOI: 10.1016/j.treng.2025.100396

Thong Duc Hong , Bao Quoc Nguyen Dinh , Thac Vinh Le , Duy Bao Huynh Hoang , Ly Khanh Thi Nguyen , Minh Quang Pham , Tai De Duong

The aerodynamic drag coefficient is a dimensionless parameter essential for evaluating aerodynamic efficiency. This paper investigates the drag coefficient, including pressure and friction coefficients, using two kinds of reference areas: actual and assumed, across four NACA airfoils (0012, 4415, 631-412, and 642-015) with halved and shortened configurations at velocities from 40 to 160 km/h. The height-over-length ratio of airfoils strongly influences drag components. For ratios ranging from 0.05 to 0.35, the relative change of total drag coefficient between the assumed and actual areas decreases from 81.10% to 5.13 % at 100 km/h and from 78.26 % to 4.82 % at 160 km/h across all airfoils. In commercial vehicles with typical ratios of 0.20 - 0.38, this discrepancy is from 10 to only 5 %, while racing cars (Formula 1, Formula 2) and metros with general height-over-length ratios of 0.16 - 0.20 may experience differences up to 18%. These findings emphasize insights into selecting reference areas precisely, a concern often overlooked in the literature, for vehicle aerodynamic drag calculations to achieve reliable solutions within specific height-over-length ratios while balancing computational resources. With advancements in computational technologies and simulation software, the precise determination of reference areas to minimize calculating errors warrants greater attention and serious consideration.

气动阻力系数是评价气动效率所必需的无量纲参数。本文研究了阻力系数，包括压力和摩擦系数，使用两种参考区域：实际和假设，跨越四个NACA翼型（0012,4415,631-412和642-015），在速度从40到160公里/小时减半和缩短配置。翼型的高长比对阻力分量影响很大。在0.05 ~ 0.35的比率范围内，所有翼型的总阻力系数在100 km/h时从81.10%下降到5.13%，在160 km/h时从78.26%下降到4.82%。在典型的比率为0.20 - 0.38的商用车中，这种差异从10%到5%不等，而赛车（一级方程式，二级方程式）和一般高长比为0.16 - 0.20的地铁可能会出现高达18%的差异。这些发现强调了精确选择参考区域的见解，这是文献中经常忽视的一个问题，用于车辆气动阻力计算，以在特定的高度-长度比内获得可靠的解决方案，同时平衡计算资源。随着计算技术和仿真软件的进步，精确确定参考区域以最小化计算误差需要更多的关注和认真考虑。

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引用次数: 0

Capturing local vehicle behavior in city-specific typical driving cycles using machine learning: Case of Bogota, Colombia 使用机器学习捕捉城市特定典型驾驶周期中的本地车辆行为：以哥伦比亚波哥大为例

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-10 DOI: 10.1016/j.treng.2025.100399

Daniel Robayo-Rueda , Miguel Angel Uribe-Laverde , Cesar L. Barraza-Botet

This work presents a novel methodology based on machine learning for developing city-representative typical driving cycles (TDCs) of light-duty vehicles. The methodology incudes the combination of principal component analysis to reduce dimensionality of the parameters space, k-means clustering with metaheuristics for micro-trips clusterization, and Markov chains to connect representative micro-trips into fully computed TDCs. Existing on-road datasets from the U.S. cities of El Paso, San Antonio, and Houston/Galveston were used to validate the methodology by computing three corresponding local TDCs. The computed TDCs were assessed in their representativeness of each city’s dataset and compared to standard TDCs. Representativeness assessments show that the proposed methodology is robust, and that the various metaheuristics applied do not have a significant effect on the average error between the computed TDCs and the datasets. Comparisons demonstrate that the computed city-specific TDCs present much smaller mean error values than the standard TDCs used for regulatory purposes. Finally, the validated methodology was applied to on-road test data in Bogota metro areas, producing a local ‘Bogota TDC’ with average mean errors lower than 5 % with respect to the dataset. The typical driving emissions and fuel consumption obtained from measurements on a chassis dynamometer using the Bogota TDC are also reported. Notably, the dynamometer tests with the Bogota TDC reveal ∼25 % higher fuel consumption compared to standard TDCs. These findings highlight the importance of developing representative local TDCs to better estimate vehicles’ near-Real World behavior, as well as fuel economy and emission measurements for regulatory compliance with environmental standards.

这项工作提出了一种基于机器学习的新方法，用于开发轻型车辆的城市代表性典型驾驶周期（tdc）。该方法包括结合主成分分析来降低参数空间的维数，结合k-means聚类和元启发式聚类来进行微行程聚类，以及将具有代表性的微行程连接到完全计算的tdc的马尔可夫链。来自美国埃尔帕索、圣安东尼奥和休斯顿/加尔维斯顿等城市的现有道路数据集通过计算三个相应的当地tdc来验证该方法。计算出的tdc在每个城市数据集中的代表性进行了评估，并与标准tdc进行了比较。代表性评估表明，所提出的方法是稳健的，并且应用的各种元启发式对计算的tdc和数据集之间的平均误差没有显着影响。比较表明，与用于监管目的的标准tdc相比，计算的城市特定tdc的平均误差值要小得多。最后，将经过验证的方法应用于波哥大都市区的道路测试数据，生成了本地“波哥大TDC”，相对于数据集，其平均误差低于5%。典型的驾驶排放和燃料消耗的测量从底盘测力仪上使用波哥大TDC也报告。值得注意的是，波哥大TDC的测力机测试显示，与标准TDC相比，燃油消耗高出约25%。这些发现强调了开发具有代表性的本地tdc的重要性，以更好地估计车辆的接近真实世界的行为，以及燃油经济性和排放测量，以符合环境标准的监管要求。

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引用次数: 0

Metaheuristic-optimized neural networks for travel time index prediction: A comparative study of wild horse and coot optimization algorithms 用于旅行时间指数预测的元启发式优化神经网络：野马优化算法与库特优化算法的比较研究

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-08 DOI: 10.1016/j.treng.2025.100395

Navid Khorshidi , Soheil Rezashoar , Pegah Amini , Shahriar Afandizadeh Zargari , Hamid Mirzahossein

Accurate travel time prediction is essential for effective Intelligent Transportation Systems (ITSs). Unlike traditional gradient-based methods, which may get trapped in local optima, metaheuristic algorithms efficiently navigate complex optimization landscapes without needing derivative computations. This study compares two metaheuristic algorithms—Wild Horse Optimization (WHO) and Coot Optimization Algorithm (COA)—for optimizing the weights and biases of a Multilayer Perceptron (MLP) neural network to predict the Travel Time Index (TTI). The MLP uses four input variables (traffic volume, weather, crash rate, and section length), one hidden layer with 12 neurons, and one output neuron. The model was trained and tested on a dataset of 472 observations from Virginia’s transportation network (2014–2017), with input variables showing mean values of 1.19 (traffic volume), 6.92 mm (precipitation), 0.17 (crash rate), and 2.76 miles (section length), and standard deviations of 0.20, 5.21, 0.12, and 1.64, respectively. Performance was assessed using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Correlation Coefficient (R). The WHO-optimized model outperformed COA, achieving an MSE of 0.0026, RMSE of 0.0511, and R of 0.7516, compared to COA’s MSE of 0.0028, RMSE of 0.0533, and R of 0.7262. WHO also showed better computational efficiency (147 s vs. 160 s for COA). These results highlight the WHO-MLP model’s superior accuracy and speed, making it ideal for real-time navigation systems.

准确的出行时间预测是智能交通系统有效运行的基础。传统的基于梯度的方法可能会陷入局部最优，而元启发式算法不需要导数计算，可以有效地导航复杂的优化景观。本研究比较了两种元启发式算法——野马优化算法（WHO）和库特优化算法（COA）——用于优化多层感知器（MLP）神经网络的权重和偏差，以预测旅行时间指数（TTI）。MLP使用四个输入变量（交通量、天气、崩溃率和路段长度）、一个包含12个神经元的隐藏层和一个输出神经元。该模型在弗吉尼亚州交通网络（2014-2017年）的472个观测数据集上进行了训练和测试，输入变量的平均值分别为1.19（交通量）、6.92毫米（降雨量）、0.17（碰撞率）和2.76英里（路段长度），标准差分别为0.20、5.21、0.12和1.64。使用均方误差（MSE）、均方根误差（RMSE）和相关系数(R)评估性能。who优化模型优于COA， MSE为0.0026，RMSE为0.0511，R为0.7516，而COA的MSE为0.0028，RMSE为0.0533，R为0.7262。WHO也显示出更好的计算效率（147秒vs. 160秒）。这些结果突出了WHO-MLP模型卓越的准确性和速度，使其成为实时导航系统的理想选择。

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引用次数: 0

Measurement and modelling effect of street trees distress on asphalt pavements by using experimental and 3-D finite element methods 采用试验和三维有限元方法对沥青路面行道树损伤进行测量及建模效果分析

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-08 DOI: 10.1016/j.treng.2025.100394

Shadi Hanandeh , Frank I. ANEKE , Wassel Al-Bodour , Rana Imam , Bara’ Al-Mistarehi

This study used Artificial Neural Networks (ANN) to investigate correlations between the type and severity of distresses with a group of factors related to the trees and site conditions to identify the tree species that lead to pavement distresses and damage by using Pavement Condition Index (PCI). A Finite Element Model (FEM) was developed to represent two scenarios, with and without the trees, for both curb trees and median trees to highlight the effects attributed solely to the trees. The analysis results provide strategies and plan for tree farming along roads depend on the research results and scientific. In a finite element model that ignores tree-root effects, deformation achieves an upper limit of 0.57 mm below the outer vehicle wheel, while the internal load produces 0.43 mm of deformation, with strains in the x, y, and z directions far below failure limits. When tree-root movements are observed on a traffic-free road, deformations around the roots may reach over 25 mm, affecting a seven-fold wider area than the root zone. In Model 1, vertical displacements above the roots vary between 0.6 cm and 3 cm, indicating pavement expansion. Root pressure causes tension and compression zones to emerge, which can lead to injury. The stress and displacement characteristics in Model 2 are unbalanced due to the trees being planted in the median. Vertical displacements around the roots might reach four centimeters, suggesting serious damage. Longitudinal displacements indicate tensile cracking, suggesting a potential for additional pavement failure due to root growth. The observed types of pavement failure were longitudinal cracking and swelling. For roadside trees located at the outer edge, the failures occurred within 0 to 2 meters of the pavement edge. In the case of median trees, the pavement failures extended from 0 to 5 meters from the median. According to the field study, these failures correspond to the distress types associated with Cluster 1 and Cluster 2 species. The extent and type of modeled distresses were consistent with the on-site observations. In contrast, such distress patterns were absent in areas with Cluster 3 species (California Fan Palm, Elm, Golden Rain, Olea europaea, Pagoda Tree, and Quercus robur), where the induced strains were well below the failure threshold.

本研究利用人工神经网络（ANN）研究路面病害类型和严重程度与树木和立地条件的相关关系，并利用路面状况指数（PCI）识别导致路面病害和破坏的树种。一个有限元模型（FEM）被开发来表示两种情况，有和没有树木，路边树和中间树，以突出仅归因于树木的影响。分析结果为公路沿线林木种植提供了依据研究成果和科学依据的策略和规划。在忽略树根效应的有限元模型中，变形上限为车辆外轮以下0.57 mm，而内荷载产生的变形上限为0.43 mm， x、y、z方向的应变均远低于破坏极限。当在无交通的道路上观察树根运动时，树根周围的变形可能达到25毫米以上，影响的面积是树根区域的7倍。在模型1中，根部以上的垂直位移在0.6 cm ~ 3cm之间变化，表明路面膨胀。牙根压力会导致紧张和受压区出现，从而导致损伤。模型2中的应力和位移特性由于在中间位置种植树木而不平衡。根部周围的垂直位移可能达到4厘米，表明严重受损。纵向位移表明拉伸开裂，表明根系生长可能导致额外的路面破坏。观察到的路面破坏类型为纵向开裂和膨胀。对于位于外缘的路边树木，故障发生在距离路面边缘0 ~ 2米的范围内。在中间树的情况下，路面破坏从中间延伸0到5米。根据野外研究，这些失败对应于与集群1和集群2物种相关的窘迫类型。模拟的痛苦程度和类型与现场观察一致。相比之下，在3类物种（加利福尼亚扇棕榈、榆树、金雨树、油橄榄、槐树和栎树）的区域没有这种痛苦模式，诱导菌株远低于失败阈值。

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引用次数: 0

Leveraging artificial intelligence for predictive health assessment of Lithium-Ion batteries 利用人工智能对锂离子电池进行预测性健康评估

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-04 DOI: 10.1016/j.treng.2025.100391

Alaa Alqaryuti , Karim Moawad , Ahmad Mayyas

Predictive maintenance has become an essential strategy for improving the reliability and safety of critical systems, particularly those dependent on lithium-ion batteries in applications such as electric vehicles, aerospace, and renewable energy storage. This study presents a data-driven framework for predicting the end-of-life (EOL) of lithium-ion batteries by analyzing both charging and discharging cycles from the NASA battery dataset. Engineered features were extracted using quartile segmentation and min–max normalization to capture key patterns in voltage, current, and temperature. Multiple classification algorithms, including XGBoost, Logistic Regression, Support Vector Classifier, K-Nearest Neighbors, and Gaussian Naïve Bayes, were evaluated based on accuracy, precision, recall, and F1-score. XGBoost demonstrated superior performance with 99% accuracy for charging and 97.3% for discharging cycles. To improve interpretability, explainable AI techniques were applied, namely feature importance and SHAP, to uncover the most influential predictors of battery degradation. The analysis revealed that the instability of the early cycle voltage was a key driver in the prediction of EOL based on charging, while the variability of the temperature was most significant during discharge. These insights support the development of transparent and reliable predictive maintenance systems for safety-critical battery applications.

预测性维护已成为提高关键系统可靠性和安全性的重要策略，特别是在电动汽车、航空航天和可再生能源存储等应用中依赖锂离子电池的系统。本研究通过分析NASA电池数据集的充电和放电周期，提出了一个数据驱动的框架，用于预测锂离子电池的寿命终止（EOL）。使用四分位数分割和最小-最大归一化来提取工程特征，以捕获电压，电流和温度的关键模式。多种分类算法，包括XGBoost、Logistic回归、支持向量分类器、k近邻和高斯Naïve贝叶斯，基于准确率、精密度、召回率和f1评分进行评估。XGBoost表现出优异的性能，充电精度为99%，放电精度为97.3%。为了提高可解释性，应用了可解释的AI技术，即特征重要性和SHAP，以揭示最具影响力的电池退化预测因素。分析表明，循环初期电压的不稳定性是基于充电预测EOL的关键驱动因素，而放电时温度的变化最为显著。这些见解支持为安全关键型电池应用开发透明可靠的预测性维护系统。

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引用次数: 0

Centrality analysis for railway network planning: constrained measures for High Speed 铁路网规划的中心性分析：高速铁路的约束措施

Q1 Engineering

Transportation Engineering

Pub Date : 2025-10-02 DOI: 10.1016/j.treng.2025.100393

Corrado Rindone , Antonio Russo

High-Speed Rail (HSR) represents a sustainable travel option for serving passenger mobility at a medium–long distances. This research concerns the analysis of HSR with specific reference to transport services at a national territorial scale. The Transport System Models (TSM) lack the capacity to generate synthetic measures and indicators of the transport supply structure. This paper proposes to combine classical TSM approaches and network analysis (NA) focusing on two main research's issues: a) the possibility to develop a method based on NA to study the structure and topology of High-Speed Train (HST) service networks, using classical TSM approach; b) the necessity to specify appropriate centrality measures for a ‘line-based’ transport system. According to the two research’s issues, the paper has two main objectives: 1) to propose a criterion for extending, in a systematic way, NA tool to HST services, combining them with TSM approach; 2) to propose an advancement of centrality measures for analysing the level of connectivity between selected territories, by introducing temporal constraints that influence the quality of the connections served by the HSR. Classical and constrained centrality measures can support the planning of lines and their characteristics (e.g., travel times and frequencies). The proposed methodology is applied in a prototypal case study, for analyzing in a synthetical way the HST services available in Italy. The paper interests public and private decision-makers who want to design, formulate and evaluate a national organisation of HST services.

高速铁路（HSR）代表了一种可持续的出行选择，为中长距离的客运提供服务。本研究关注的是高铁的分析，具体参考了国家领土尺度上的运输服务。运输系统模型（TSM）缺乏生成运输供给结构综合测度和指标的能力。本文提出将经典TSM方法与网络分析（NA）相结合，重点研究两个主要问题：a)利用经典TSM方法开发基于NA的高速列车业务网络结构和拓扑研究方法的可能性；B)为“以线路为基础”的运输系统指定适当的中心度量的必要性。根据这两个研究问题，本文的主要目标有两个：1)结合TSM方法，提出将NA工具系统地扩展到HST服务的标准；2)通过引入影响高铁服务的连接质量的时间限制，提出一种改进的中心性措施，以分析选定地区之间的连通性水平。经典的和受限的中心性度量可以支持线路的规划及其特性（例如，行程时间和频率）。提出的方法应用于一个原型案例研究，以综合的方式分析意大利可用的HST服务。这篇论文吸引了想要设计、制定和评估HST服务的国家组织的公共和私人决策者。

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引用次数: 0

Evaluation of the performance of asphalt pavements subjected to frost heave and thaw settlement: A mechanistic modeling approach 沥青路面在冻胀和融化沉降作用下的性能评价：一种力学建模方法

Q1 Engineering

Transportation Engineering

Pub Date : 2025-09-27 DOI: 10.1016/j.treng.2025.100392

Saeed Vosoughian, Romain Balieu

This study presents a mechanics-based framework for evaluating the performance of asphalt pavements subjected to annual ground surface deformations induced by frost heave and thaw settlement. Within this framework, a novel thaw settlement model is developed and coupled with a thermomechanical frost heave model to represent the heaving and subsequent thawing behavior of the soil beneath pavements. In the proposed thaw settlement model, excess water generated during the melting of ice lenses is introduced as a damage parameter within a continuum mechanics formulation, resulting in a reduction of material stiffness. The frost heave–thaw settlement model is further coupled with a thermodynamics-based asphalt damage model to capture the progressive degradation of asphalt layers. The capabilities of the proposed framework are demonstrated through two study scenarios: (i) uniform frost heave and thaw settlement occurring across the entire subgrade layer. (ii) non-uniform frost heave and thaw settlement localized within a specific region of the subgrade. The results show that the proposed framework not only captures damage in the asphalt layer but also predicts temperature distributions, ice formation, and water content during freeze–thaw cycles. Furthermore, the analysis reveals that uniform heaving and settlement cause relatively minor asphalt damage but lead to surface unevenness, whereas non-uniform frost heave and settlement result in both surface irregularities and severe damage to the asphalt layer.

本研究提出了一个基于力学的框架，用于评估沥青路面在冻胀和融化沉降引起的年度地表变形下的性能。在此框架下，建立了一种新的融化沉降模型，并将其与热力冻胀模型相结合，以表示路面下土壤的隆起和随后的融化行为。在提出的融化沉降模型中，在连续介质力学公式中引入了冰透镜融化过程中产生的过量水作为损伤参数，导致材料刚度降低。进一步将冻胀-融沉降模型与基于热力学的沥青损伤模型相结合，以捕捉沥青层的渐进退化。提出的框架的能力通过两种研究情景来证明：(i)整个路基层发生均匀的冻胀和融化沉降。（ii）不均匀的冻胀和融化沉降局限在路基的特定区域内。结果表明，所提出的框架不仅可以捕获沥青层的损伤，还可以预测冻融循环过程中的温度分布、冰形成和含水量。分析表明，均匀的冻胀沉降对沥青路面造成的损伤较小，但会导致路面不平整，而不均匀的冻胀沉降既会造成路面不平整，又会对沥青路面造成严重的损伤。

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引用次数: 0

Investigating the dynamic creep and the indirect tensile performance of sasobit-modified warm asphalt mixtures 研究了sasobt改性温沥青混合料的动态蠕变和间接拉伸性能

Q1 Engineering

Transportation Engineering

Pub Date : 2025-09-16 DOI: 10.1016/j.treng.2025.100390

Khalid A. Ghuzlan , Bara’ W. Al-Mistarehi , Saleh O. Lafi

This study aims to investigate the dynamic creep, indirect tensile, and moisture damage performance of Sasobit-modified asphalt mixtures. The asphalt mixture specimens were prepared using the optimum asphalt content determined by the Superpave mixture design method. The performance in rutting and fatigue for asphalt mixtures has been estimated through dynamic creep and indirect tensile resilient modulus tests using the Universal Testing Machine (UTM). The dynamic creep test was conducted at two temperatures (25 °C and 40 °C) using a single loading frequency of 8 Hz. The indirect tensile test was performed at two temperatures (25 °C and 40 °C) with a frequency of 1 Hz. Based on the dynamic creep test results, the Sasobit modified asphalt mixtures have lower accumulated strains and higher creep stiffness than the control mixture at both 25 °C and 40 °C temperatures. Based on the indirect tensile test, the resilient modulus of the Sasobit-modified mixtures was greater than the control mixture. The moisture effect test results indicated that all modified mixtures pass the minimum required tensile strength ratio (TSR). Generally, the modified mixtures have shown enhanced behavior in terms of rutting, fatigue, and moisture damage resistance. Asphalt mixtures modified with 2 % Sasobit showed the best performance at 40 °C since it had the lowest accumulated strain, and the highest creep stiffness and resilient modulus.

本研究旨在探讨sasobit改性沥青混合料的动态蠕变、间接拉伸和湿损伤性能。采用Superpave混合料设计法确定的最佳沥青掺量制备沥青混合料试件。利用通用试验机（UTM）对沥青混合料进行了动态蠕变和间接拉伸弹性模量试验，对其车辙和疲劳性能进行了评价。动态蠕变试验在25°C和40°C两种温度下进行，单加载频率为8 Hz。间接拉伸试验在25°C和40°C两种温度下进行，频率为1hz。动态蠕变试验结果表明，在25℃和40℃温度下，Sasobit改性沥青混合料的累积应变和蠕变刚度均低于对照混合料。间接拉伸试验表明，sasobit改性混合料的弹性模量大于对照混合料。湿效应试验结果表明，改性混合料均通过了最小抗拉强度比（TSR）要求。总的来说，改性混合物在车辙、疲劳和抗湿损伤方面表现出增强的性能。添加2% Sasobit的沥青混合料在40℃时的累积应变最低，蠕变刚度和弹性模量最高，表现出最佳的性能。

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引用次数: 0