International Journal of Fatigue最新文献_第10页

Rolling contact fatigue behavior of bainite/martensite bearing steel with rare earth addition 添加稀土贝氏体/马氏体轴承钢的滚动接触疲劳行为

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-10 DOI: 10.1016/j.ijfatigue.2025.109434

Zan Li , Chaoyun Yang , Peng Liu , Xing Li , Yikun Luan , Chengwu Zheng , Dianzhong Li

This study aims to investigate the influence of rare earth (RE) elements on the rolling contact fatigue (RCF) behaviors of bainite/martensite (B/M) bearing steel. For this purpose, RCF tests were conducted on heat treated B/M bearing steels with and without RE addition. Detailed microstructural characterization and RCF failure analyses were performed on the tested specimens. The results indicate that RE addition can significantly improve the RCF property of B/M bearing steel. As the surface roughness decreases, the failure type changes from a surface to a subsurface nucleated, accompanied by the continuous increase in RCF life. For surface crack initiation failure, crack initiation originates from the connection of surface flaking pits, and martensite/austenite (M/A) blocks facilitate the formation of streamline structure and crack propagation. The high toughness and smaller M/A blocks for RE-treated steel make the formation of flaking pits and streamline structure more difficult, thereby leading to a longer RCF life. For subsurface crack initiation failure, the evolved ribbon-like structures dominate crack initiation and fatigue failure. Their formation primarily involves bainite deformation, microstructure fragmentation, rotation of fragmented blocks to form {101} texture, and rotation of refined nanograins into ribbon-like structures. The slower formation of ribbon-like structure and longer RCF life for RE-treated steel can be attributed to the smaller M/A blocks and more difficult austenite transformation, as well as the easier connection and strain coordination of different evolution regions caused by a larger volume fraction of bainite and smaller M/A blocks.

研究稀土元素对贝氏体/马氏体轴承钢滚动接触疲劳（RCF）行为的影响。为此，对添加和不添加稀土的热处理B/M轴承钢进行了RCF试验。对试样进行了详细的微观结构表征和RCF失效分析。结果表明，稀土元素的加入能显著改善B/M轴承钢的RCF性能。随着表面粗糙度的降低，失效类型由表面成核转变为亚表面成核，同时RCF寿命持续增加。对于表面裂纹起裂失效，裂纹起裂源于表面剥落坑的连接，马氏体/奥氏体（M/A）块体有利于流线组织的形成和裂纹扩展。再处理钢的高韧性和更小的M/A块使剥落坑和流线结构的形成更困难，从而导致更长的RCF寿命。对于亚表面裂纹起裂破坏，演化出的带状结构主导裂纹起裂和疲劳破坏。它们的形成主要包括贝氏体变形、微观结构破碎、碎片块旋转形成{101}织构以及精细纳米颗粒旋转成带状结构。re处理钢的带状组织形成较慢，RCF寿命较长，这可归因于M/A块较小，奥氏体转变困难；贝氏体体积分数较大，M/A块较小，不同演化区域之间更容易连接和应变协调。

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引用次数: 0

Analytical and machine learning-based fatigue life prediction of welded joints under multiaxial loading 多轴载荷下焊接接头疲劳寿命分析与机器学习预测

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-24 DOI: 10.1016/j.ijfatigue.2025.109459

Marten Beiler , Niklas Michael Bauer , Jörg Baumgartner , Moritz Braun

Evaluating the fatigue life of welded joints under multiaxial loading is a key challenge in structural engineering. This study explores machine learning (ML) methods for predicting fatigue life and compares their performance against the novel super ellipse criterion, which is an analytical approach that aims to improve current design standard methods (e.g., Eurocode 3, IIW). Using a dataset of uniaxial and multiaxial fatigue tests with varying phase angles, ML models—including artificial neural networks and extreme gradient boosting (XGBoost)—are trained on features like stress amplitudes, phase differences, and material properties. Artificial neural networks provide high accuracy, while tree-based models like XGBoost offer better interpretability via model agnostic interpretation using Explainable Artificial Intelligence. Results show ML models can outperform traditional criteria, especially under non-proportional loading, but face limitations near the edges of the training data. This work highlights the potential and challenges of ML in fatigue prediction and highlights their value for enhancing the safety and reliability of welded structures.

多轴载荷作用下焊接接头的疲劳寿命评估是结构工程中的一个关键问题。本研究探索了用于预测疲劳寿命的机器学习（ML）方法，并将其性能与新型超椭圆准则进行了比较，超椭圆准则是一种旨在改进当前设计标准方法（例如，Eurocode 3， IIW）的分析方法。使用不同相角的单轴和多轴疲劳测试数据集，ML模型（包括人工神经网络和极端梯度增强（XGBoost））可以根据应力幅值、相位差和材料特性等特征进行训练。人工神经网络提供了很高的准确性，而像XGBoost这样基于树的模型通过使用可解释的人工智能（explable Artificial Intelligence）进行模型不可知的解释，提供了更好的可解释性。结果表明，机器学习模型可以优于传统的标准，特别是在非比例负载下，但在训练数据的边缘附近面临限制。这项工作强调了机器学习在疲劳预测中的潜力和挑战，并强调了它们在提高焊接结构的安全性和可靠性方面的价值。

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引用次数: 0

Competitive propagation of multiple surface fatigue cracks in railheads: compact tension tests and peridynamic simulations 铁路道头多个表面疲劳裂纹的竞争扩展：紧致张力试验和周围动力学模拟

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-21 DOI: 10.1016/j.ijfatigue.2025.109453

Xiaoming Wang , Yitong Shi , Weijia Dong , Qing He , Boyang An , Bing Yang , Jun Huang , Ping Wang

Significant interactions exist among multiple surface cracks in railheads. This study investigates the competitive propagation behavior of rail surface cracks using the compact tension (CT) tests and a peridynamic (PD) model. Four sets of CT tests for multi-crack propagation were designed with U75V railhead material, and corresponding PD fatigue models were established. Significant shielding effects were observed among cracks during propagation, with the PD model accurately replicating crack propagation paths and fatigue lives from CT tests. A PD model was constructed to simulate the dynamic crack propagation on rail surfaces under rolling wheel loading, revealing significant promotion and suppression effects among cracks dominantly influenced by crack number and spacing. PD-predicted crack branching and coalescence align with field rail damage patterns.

铁路道头多个表面裂缝之间存在着显著的相互作用。本文研究了钢轨表面裂纹的竞争扩展行为，采用了致密拉伸（CT）试验和周动力（PD）模型。采用U75V轨道口材料设计了4套多裂纹扩展CT试验，建立了相应的PD疲劳模型。在裂纹扩展过程中观察到明显的屏蔽效应，PD模型准确地复制了CT试验的裂纹扩展路径和疲劳寿命。建立了滚动车轮荷载作用下钢轨表面裂纹动态扩展的PD模型，发现裂纹数量和裂纹间距对钢轨表面裂纹扩展具有显著的促进和抑制作用。pd预测的裂纹分支和合并与现场钢轨损伤模式一致。

引用次数: 0

Fatigue life prediction of multi jet fusion-manufactured polyamide12 lattice structures using the average strain energy density method 用平均应变能密度法预测多喷流熔接聚酰胺12晶格结构的疲劳寿命

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-21 DOI: 10.1016/j.ijfatigue.2025.109452

Raffaele De Biasi , Lorenzo Romanelli , Ciro Santus , Matteo Perini , Filippo Berto , Matteo Benedetti

The industrial sector continues to explore innovative strategies to exploit the full potential of Additive Manufacturing (AM). Among its many advantages, AM enables the fabrication of lattice structures; these are lightweight metamaterials with tunable mechanical properties and excellent energy absorption capabilities. Despite their promise, the widespread industrial use of such structures is limited by the difficulty in accurately assessing their fatigue behavior. This study presents a methodology aimed at predicting the fatigue life of polymer-based lattice components, with a specific focus on PA12 manufactured using the Multi Jet Fusion (MJF) process. This is an industrially relevant technology offering large production volumes, high printing quality and low production costs. The approach begins with fatigue testing of bulk PA12 specimens to establish baseline material behavior. Based on these results, a predictive algorithm is developed to estimate the fatigue performance of lattice structures. The model adopts an energy-based framework inspired by the Average Strain Energy Density (ASED) method, previously used for metallic materials, and adapts it to the characteristics of polymer lattices. The proposed methodology contributes to the development of efficient fatigue assessment tools, supporting the broader adoption of lattice structures in cost-sensitive industrial applications where polymer-based materials are effective.

工业部门继续探索创新战略，以充分利用增材制造（AM）的潜力。在其众多优点中，增材制造可以制造晶格结构；这些都是重量轻的超材料，具有可调的机械性能和出色的能量吸收能力。尽管它们很有前途，但由于难以准确评估其疲劳行为，这种结构的广泛工业应用受到了限制。本研究提出了一种方法，旨在预测基于聚合物的晶格部件的疲劳寿命，特别关注使用多喷射融合（MJF）工艺制造的PA12。这是一项工业相关技术，可提供大批量，高印刷质量和低生产成本。该方法首先进行散装PA12试样的疲劳试验，以建立基准材料性能。在此基础上，提出了一种预测网格结构疲劳性能的算法。该模型采用了一种基于能量的框架，其灵感来自于之前用于金属材料的平均应变能密度（ASED）方法，并使其适应于聚合物晶格的特性。提出的方法有助于开发有效的疲劳评估工具，支持在成本敏感的工业应用中更广泛地采用晶格结构，其中聚合物基材料是有效的。

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引用次数: 0

Real-Time fatigue crack growth prediction for welded structures based on digital twin framework considering residual stress and variable amplitude loading 考虑残余应力和变幅载荷的焊接结构疲劳裂纹扩展实时预测

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ijfatigue.2025.109475

Wenyue Zhang , Yong Chen , Xing He , Fang Xue , Peng Xu , Wentao He

This paper proposes a dynamic digital twin framework driven by real-time physical information, which integrates a Radial Basis Function (RBF) neural network and a Dynamic Bayesian Network (DBN). A time-varying fatigue crack growth program is developed to update uncertain crack growth parameters and to enable real-time life prediction under welding residual stress and variable-amplitude loading conditions. A finite element model of welding residual stress is established based on thermo-elastic–plastic theory, and the associated stress intensity factor is calculated using the weight function method. A nonlinear mapping between the stress intensity factor and crack length is constructed using the RBF neural network, accounting for both welding residual stress and variable-amplitude loading. The physics-informed digital twin framework, where the Particle Filter (PF) algorithm drives the DBN, is applied to predict fatigue crack growth and update uncertain parameters in Middle Tension (MT) specimens. Under conditions of periodic multiple overloads, the predicted fatigue life closely matches the experimental results, with an error under 1%. The crack growth process is validated through the co-simulation of ABAQUS and FRANC3D using the updated parameters, with the error between simulated and experimental results remaining below 1%, which demonstrates the high accuracy and robustness of the proposed digital twin framework for fatigue life prediction.

本文提出了一种基于实时物理信息驱动的动态数字孪生框架，该框架将径向基函数（RBF）神经网络和动态贝叶斯网络（DBN）相结合。开发了时变疲劳裂纹扩展程序，以更新不确定裂纹扩展参数，实现焊接残余应力和变幅加载条件下的实时寿命预测。基于热弹塑性理论建立了焊接残余应力有限元模型，采用权函数法计算了相关应力强度因子。在考虑焊接残余应力和变幅加载的情况下，利用RBF神经网络建立了应力强度因子与裂纹长度的非线性映射关系。采用基于物理的数字孪生框架，其中粒子滤波（PF）算法驱动DBN，用于预测中张力（MT）试样的疲劳裂纹扩展和更新不确定参数。在周期性多次过载条件下，预测疲劳寿命与试验结果吻合较好，误差小于1%。利用更新后的参数，通过ABAQUS和FRANC3D联合仿真验证了裂纹扩展过程，仿真结果与实验结果的误差小于1%，验证了所提出的数字孪生框架对疲劳寿命预测的精度和鲁棒性。

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引用次数: 0

Fatigue properties of Fe-30.5Mn-8Al-1C austenitic low-density steel: Critical impact of κ-carbide precipitation state Fe-30.5Mn-8Al-1C奥氏体低密度钢疲劳性能：κ-碳化物析出状态的临界影响

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ijfatigue.2025.109477

J.H. Du, P. Chen, Z.P. Jia, X.W. Li

This study systematically investigates the tension–tension fatigue behavior and deformation mechanisms of solution-treated and aging-treated Fe-30.5Mn-8Al-1C (wt%) austenitic low-density steels, focusing on the critical role of κ-carbide precipitation state in controlling fatigue properties. In aged samples, intragranular κ-carbides induce planar dislocation slip through a “glide plane softening” mechanism, enhancing slip reversibility under cyclic loading and thereby improving fatigue life. Strengthening is primarily due to the interaction between dislocations and intragranular κ-carbides. An appropriate increase in the size of intragranular κ-carbides significantly enhances fatigue life and fatigue strength at low stress amplitudes. Conversely, intergranular κ-carbide precipitation impedes slip transmission, intensifies localized stress concentration, and accelerates damage, thus reducing fatigue life at high stress amplitudes. These findings strongly demonstrate that accelerating the precipitation of intragranular κ-carbides while suppressing intergranular precipitation is an effective microstructural pathway to concurrently enhance fatigue performance of Fe-Mn-Al-C austenitic low-density steels across the entire range of stress amplitudes.

本研究系统地研究了固溶处理和时效处理的Fe-30.5Mn-8Al-1C （wt%）奥氏体低密度钢的拉伸-拉伸疲劳行为和变形机制，重点研究了κ-碳化物析出态在控制疲劳性能中的关键作用。在时效试样中，晶内碳化物通过“滑动面软化”机制诱导位错滑移，增强了循环载荷下滑移的可逆性，从而提高了疲劳寿命。强化主要是位错与晶内碳化物相互作用的结果。适当增加晶内碳化物的尺寸可显著提高低应力幅值下的疲劳寿命和疲劳强度。相反，晶间的κ碳化物析出阻碍了滑移传递，加剧了局部应力集中，加速了损伤，从而降低了高应力幅值下的疲劳寿命。这些结果有力地表明，在抑制晶间析出的同时，加速晶内碳化物的析出是同时提高Fe-Mn-Al-C奥氏体低密度钢在整个应力幅值范围内疲劳性能的有效微观组织途径。

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引用次数: 0

Total vibration fatigue life of TC4 alloy: Experiments and modelling TC4合金总振动疲劳寿命：实验与建模

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-13 DOI: 10.1016/j.ijfatigue.2025.109433

Yiwen Lei , Yue Liu , Huiping Wu , Xifeng Li

This study investigated the fatigue properties of diffusion-bonded TC4 titanium alloy under bending vibration loading, through experimental and computational methods. When interfacial atoms achieve complete metallurgical bonding, vibration fatigue crack initiates at the region of maximum stress concentration, rather than at the diffusion-bonded interface. To accurately model this behavior, a modified continuum damage mechanics model is developed to account for different damage evolution under tensile and compressive loading. Furthermore, an enhanced crack propagation rate model is proposed by modifying the Paris law to better characterize short-crack growth behavior after crack initiation. These models are integrated into a unified finite element framework that successfully predicts both crack initiation life and propagation life, as well as crack initiation site and propagation path. Numerical results obtained from a notched specimen demonstrate that damaged elements are distributed at the notch region, exhibiting pronounced damage localization. The damage evolutions of elements exhibit significant differences between tensile and compressive stress states. The total fatigue life prediction follows a two-stage computational process where crack initiation analysis first identifies critical damaged elements, which then serve as initial cracks for propagation analysis. Validation results, considering two stress amplitudes of 373 MPa and 400 MPa, confirm the framework’s accuracy, showing relative errors of 1.4 % and 11.4 % for total fatigue lives.

采用实验和计算相结合的方法，研究了扩散粘结TC4钛合金在弯曲振动载荷下的疲劳性能。当界面原子达到完全的冶金结合时，振动疲劳裂纹产生于最大应力集中区域，而不是扩散结合界面。为了准确地模拟这种行为，建立了一个改进的连续损伤力学模型，以考虑拉伸和压缩载荷下不同的损伤演变。在此基础上，通过修正Paris定律，提出了一种增强的裂纹扩展速率模型，以更好地表征裂纹起裂后的短裂纹扩展行为。这些模型被整合到一个统一的有限元框架中，成功地预测了裂纹的起裂寿命和扩展寿命，以及裂纹的起裂位置和扩展路径。数值结果表明，损伤单元分布在缺口区域，表现出明显的损伤局部化。构件的损伤演化在拉压应力状态下表现出显著差异。总疲劳寿命预测遵循两个阶段的计算过程，其中裂纹萌生分析首先确定临界损伤单元，然后作为扩展分析的初始裂纹。考虑373 MPa和400 MPa两个应力幅值，验证结果证实了框架的准确性，总疲劳寿命的相对误差为1.4 %和11.4 %

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引用次数: 0

Defect-sensitive fatigue assessment of heavy-section ductile cast irons: a comparative study of pearlitic and high-silicon ferritic grades 大断面球墨铸铁的缺陷敏感疲劳评估：珠光体和高硅铁素体等级的比较研究

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-27 DOI: 10.1016/j.ijfatigue.2025.109455

M. Benedetti , M. Pedranz , D. Lusuardi , F. Zanini , S. Carmignato , V. Fontanari

Heavy-section castings of ductile cast iron (DCI) unavoidably contain micro shrinkage porosity due to non-uniform, slow cooling, and service components also feature geometric stress raisers. This study quantifies how these two realities—intrinsic defects and notches—jointly control fatigue resistance and formalizes a design approach that accounts for their interaction. We compare a pearlitic EN-GJS-600–3 (GJS-600–3) and a high-silicon solid solution strengthened ferritic (HSi) DCI, which exhibit different matrix ductility and distinct pore populations. Pore size distributions are characterized (via X-ray computed tomography, CT), and extreme-value statistics are used to estimate the most critical defect expected in the highly stressed region of notched specimens. This defect measure is then coupled to a strain energy density (SED) criterion to predict fatigue limits. Fatigue tests on plain and V-notched specimens with varying notch severity reveal a systematic transition from pore-dominated initiation (plain and mildly notched) to notch-dominated initiation (severe notches). The proposed CT–statistics–SED framework reproduces both the fatigue limits and the observed switch in the governing initiation site. Compared with GJS-600–3, the HSi grade shows lower intrinsic fatigue strength but greater tolerance to distributed microporosity, leading to improved reliability in geometries with large highly stressed volumes. The approach provides a practical route to defect-aware fatigue design of DCI components, suggesting material-and-geometry selection: pearlitic grades for smaller, sharper features where notch control prevails; high-silicon ferritic grades for large, blunt features where defect tolerance is paramount. Overall, the method supports lighter, more reliable cast designs without resorting to overly conservative safety factors.

球墨铸铁（DCI）的大断面铸件由于冷却不均匀、速度慢，不可避免地存在微收缩孔隙，而且服务部件也具有几何应力升高的特点。本研究量化了这两种现实——内在缺陷和缺口——如何共同控制抗疲劳性，并形式化了一种解释它们相互作用的设计方法。我们比较了珠光体EN-GJS-600-3 （GJS-600-3）和高硅固溶体强化铁素体（HSi） DCI，它们具有不同的基体延展性和不同的孔隙数量。表征孔径分布（通过x射线计算机断层扫描，CT），并使用极值统计来估计缺口样品高应力区域中预期的最关键缺陷。然后将该缺陷测量与应变能密度（SED）准则耦合以预测疲劳极限。对不同缺口严重程度的平原和v形缺口试样进行疲劳试验，揭示了从孔隙主导起始（平原和轻度缺口）到缺口主导起始（严重缺口）的系统转变。提出的ct -统计- sed框架再现了疲劳极限和在控制起始位置观察到的开关。与GJS-600-3相比，HSi等级具有较低的固有疲劳强度，但对分布微孔隙的容错性更强，从而提高了高应力体积几何形状的可靠性。该方法为DCI组件的缺陷感知疲劳设计提供了一条实用的途径，建议选择材料和几何形状：珠光体级用于更小、更锋利的特征，其中缺口控制占优；高硅铁素体牌号用于大而钝的特征，其中缺陷容忍度是至关重要的。总体而言，该方法支持更轻，更可靠的铸造设计，而无需诉诸过于保守的安全因素。

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引用次数: 0

A framework for fatigue life prediction of fiber reinforced composites with limited testing data 基于有限试验数据的纤维增强复合材料疲劳寿命预测框架

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2025-12-21 DOI: 10.1016/j.ijfatigue.2025.109446

Lu Yubin , Wu Zhen

To ensure structural integrity, it is essential to establish an accurate fatigue life prediction model. Traditional regression models are constrained by predefined functional forms, which often neglect the effects of material properties. However, purely data-driven methods require large datasets and exhibit poor extrapolation ability. Therefore, this study develops a novel framework to accurately predict fatigue life using limited testing data. The framework consists of two main parts, namely feature selection and iterative generation-estimation process (IGEP). Based on Pearson correlation coefficient, Variance inflation factors and Shapley additive explanations, the stress level, strength, and stiffness are selected as critical features. The IGEP uniquely integrates two synergistic neural networks, namely a generative model L (mapping stress to life) and an estimated model D (mapping life to stress). Seven neural architectures are evaluated, and then Convolutional Neural Network (CNN) and a combined model including Convolutional Neural Network, Long Short-Term Memory, and Attention module (CNN-LSTM-Attention) are selected to construct L and D, respectively. Models L and D form a closed-loop system that iteratively refines life predictions under the constraint of the fundamental S-N relationship. Compared with experimental data, the predictive accuracy of the IGEP has been verified. Despite the paucity of available experimental data, IGEP can generate reliable fatigue life curves across a wide range of stress levels. Moreover, when applied to stress levels, laminate configurations and material systems beyond those represented in the training data, the IGEP demonstrates robust extrapolation capability. The proposed framework provides a practical and generalizable tool for fatigue life prediction in FRPs under data-limited conditions.

为了保证结构的完整性，建立准确的疲劳寿命预测模型至关重要。传统的回归模型受到预定义函数形式的约束，往往忽略了材料特性的影响。然而，纯数据驱动的方法需要大型数据集，并且表现出较差的外推能力。因此，本研究开发了一种新的框架，可以使用有限的测试数据准确预测疲劳寿命。该框架由特征选择和迭代生成估计过程（IGEP）两个主要部分组成。基于Pearson相关系数、方差膨胀因子和Shapley加性解释，选择应力水平、强度和刚度作为关键特征。IGEP独特地集成了两个协同神经网络，即生成模型L（将压力映射到生活）和估计模型D（将生活映射到压力）。评估了7种神经结构，然后选择卷积神经网络（CNN）和卷积神经网络、长短期记忆和注意模块（CNN- lstm -Attention）的组合模型分别构建L和D。模型L和D形成了一个闭环系统，在基本S-N关系的约束下迭代地细化寿命预测。通过与实验数据的比较，验证了IGEP预测的准确性。尽管缺乏可用的实验数据，但IGEP可以在广泛的应力水平范围内生成可靠的疲劳寿命曲线。此外，当应用于应力水平、层压板结构和材料系统时，IGEP显示出强大的外推能力。提出的框架为数据有限条件下frp的疲劳寿命预测提供了一个实用的、可推广的工具。

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引用次数: 0

Fatigue behavior of externally bonded and hybrid-bonded carbon fiber reinforced polymer–to–concrete joints 外粘接和混合粘接碳纤维增强聚合物-混凝土节点的疲劳性能

IF 6.8 2区材料科学 Q1 ENGINEERING, MECHANICAL

International Journal of Fatigue

Pub Date : 2026-05-01 Epub Date: 2026-01-04 DOI: 10.1016/j.ijfatigue.2026.109482

Mehdi Aghabagloo , Laura Carreras , José Sena-Cruz , Marta Baena

Although the bond behavior and failure modes of externally bonded reinforcement (EBR) fiber-reinforced polymer (FRP) and hybrid-bonded (HB) systems on concrete have been widely investigated under quasi-static loading, their performance under fatigue loading remains insufficiently understood. Existing research on RC structures strengthened with carbon FRP (CFRP) under cyclic loading has predominantly focused on demonstrating improvements in fatigue life. However, far less attention has been given to examining the bond behavior and the rate of debonding growth, factors that are critical to ensuring the long-term effectiveness and durability of externally bonded CFRP systems. This study experimentally examines the performance of EBR and HB CFRP-to-concrete bonded joints subjected to cyclic fatigue loading. The investigation focuses on the progression of fatigue-induced damage in bonded joints tested under direct pull-out conditions, using CFRP laminates exposed to different maximum cyclic load levels (relative to the static failure load) while keeping a constant load ratio. Under fatigue loading, interfacial debonding between the CFRP laminate and the adhesive was observed, whereas quasi-static tests typically resulted in cohesive failure within the concrete. Results also revealed that increasing the maximum cyclic load markedly accelerated the rate of debonding propagation.

尽管在准静态载荷作用下，外粘结增强（EBR）、纤维增强聚合物（FRP）和混合粘结（HB）体系在混凝土上的粘结行为和破坏模式已经得到了广泛的研究，但它们在疲劳载荷作用下的性能仍然不够清楚。目前对循环荷载下碳纤维增强混凝土结构的研究主要集中在提高疲劳寿命上。然而，对于检查粘结行为和脱粘增长速度的关注远远不够，这些因素对于确保外部粘结CFRP系统的长期有效性和耐久性至关重要。本研究对EBR和HB cfrp -混凝土粘结节点在循环疲劳荷载下的性能进行了试验研究。该研究的重点是在直接拔出条件下，使用CFRP层叠板暴露于不同的最大循环荷载水平（相对于静态破坏荷载），同时保持恒定的荷载比，测试粘结接头疲劳损伤的进展。在疲劳荷载下，观察到CFRP层压板和粘合剂之间的界面剥离，而准静态试验通常导致混凝土内部的粘结破坏。结果还表明，增加最大循环荷载显著加快了脱粘的传播速度。

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引用次数: 0