用于预测纤维增强混凝土抗弯疲劳寿命的堆叠组合模型

IF 5.7 2区 材料科学 Q1 ENGINEERING, MECHANICAL International Journal of Fatigue Pub Date : 2024-09-12 DOI:10.1016/j.ijfatigue.2024.108599
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引用次数: 0

摘要

在混凝土中加入纤维可增强其抗疲劳性能,同时也会增加弯曲疲劳寿命的变化,因此有必要开发先进的预测模型。为应对这一挑战,本研究创新性地开发了一种堆叠集合预测模型,旨在准确预测纤维增强混凝土(FRC)的抗弯疲劳寿命。该模型集成了深度自动编码器网络、XGBoost、随机森林和经灰狼优化算法优化的深度神经网络。最初,我们精心建立了一个关于 FRC 挠曲疲劳的数据集。随后,利用该数据集对该模型进行了严格评估,结果表明该模型能有效准确地预测 FRC 的疲劳寿命。此外,还利用 SHAP 分析解释了输入特征与 FRC 疲劳寿命之间的关系。总之,这项研究为 FRC 的疲劳寿命提供了一个全面而灵活的预测框架,提高了建筑和工程项目中对这种材料的理解和实际利用,并为材料科学和工程学的未来发展提供了一个前景广阔的途径。
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A stacking ensemble model for predicting the flexural fatigue life of fiber-reinforced concrete

The incorporation of fibers into concrete enhances its fatigue resistance while also increasing the variability in flexural fatigue life, necessitating the development of advanced predictive models. To address this challenge, this study innovates by developing a stacking ensemble prediction model aimed at accurately predicting the flexural fatigue life of fiber-reinforced concrete (FRC). This model integrates the Deep Autoencoder Network, XGBoost, Random Forest, and Deep Neural Networks optimized by the Grey Wolf Optimizer algorithm. Initially, a dataset on the flexural fatigue of FRC was meticulously established. Subsequently, the model was rigorously evaluated using this dataset, with the results demonstrating its effectiveness in accurately predicting the fatigue life of FRC. Furthermore, SHAP analysis was utilized to interpret the relationship between input features and the fatigue life of FRC. In essence, this research offers a comprehensive and flexible predictive framework for the fatigue life of FRC, enhancing the comprehension and practical utilization of this material in construction and engineering projects, and presenting a promising avenue for future advancements in materials science and engineering.

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来源期刊
International Journal of Fatigue
International Journal of Fatigue 工程技术-材料科学:综合
CiteScore
10.70
自引率
21.70%
发文量
619
审稿时长
58 days
期刊介绍: Typical subjects discussed in International Journal of Fatigue address: Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements) Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions) Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation) Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering Smart materials and structures that can sense and mitigate fatigue degradation Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.
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