{"title":"用于预测纤维增强混凝土抗弯疲劳寿命的堆叠组合模型","authors":"","doi":"10.1016/j.ijfatigue.2024.108599","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A stacking ensemble model for predicting the flexural fatigue life of fiber-reinforced concrete\",\"authors\":\"\",\"doi\":\"10.1016/j.ijfatigue.2024.108599\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":14112,\"journal\":{\"name\":\"International Journal of Fatigue\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.7000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Fatigue\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0142112324004584\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fatigue","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142112324004584","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
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.
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.