AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda
{"title":"AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete.","authors":"Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda","doi":"10.1038/s41598-025-06725-z","DOIUrl":null,"url":null,"abstract":"<p><p>Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R<sup>2</sup> > 0.90) outperformed ANN, RF, and GB models. Water, superplasticizer, curing temperature, and fiber content emerged as the most significant controlling parameters affecting early-age CS. The analysis of the interaction among the significant input variables and early-age CS suggests recommended inclusion levels for optimal performance. Specifically, it is recommended that the water content be maintained between 145 and 155 kg/m<sup>2</sup>, the superplasticizer content between 30 and 40 kg/m<sup>2</sup>, and the fiber content exceed 200 kg/m<sup>2</sup>. These recommendations are aimed at achieving desirable early-age CS characteristics. The overall findings reveal that the AI models can effectively improve the monitoring of early-age CS of UHPFRC.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"20316"},"PeriodicalIF":3.9000,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12202810/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-06725-z","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Ultra-high-performance fiber-reinforced concrete (UHPFRC) is an exceptional type of cementitious composite with superior mechanical and durability performances. Achieving these properties involves maintaining a low water-to-cement ratio, optimizing aggregate size distribution, and integrating fiber reinforcement. Recently, there has been a notable trend in the development and application of UHPFRCs. However, there is still a requirement for artificial intelligence (AI) methods to predict the early-age compressive strength (CS) of UHPFRC and to define the key input factors for optimal mix design with appropriate proportions. Therefore, five AI models were chosen to assess the predictive accuracy of early-age CS in the current study. These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). As part of evaluating model performance and conducting error analysis, this study investigated differences in prediction accuracy among five models across training and testing datasets. Additionally, feature importance analysis was implemented to explore the influence of the input variables on the early-age CS. Results indicate that GPR and SVR models with high predictive accuracy (R2 > 0.90) outperformed ANN, RF, and GB models. Water, superplasticizer, curing temperature, and fiber content emerged as the most significant controlling parameters affecting early-age CS. The analysis of the interaction among the significant input variables and early-age CS suggests recommended inclusion levels for optimal performance. Specifically, it is recommended that the water content be maintained between 145 and 155 kg/m2, the superplasticizer content between 30 and 40 kg/m2, and the fiber content exceed 200 kg/m2. These recommendations are aimed at achieving desirable early-age CS characteristics. The overall findings reveal that the AI models can effectively improve the monitoring of early-age CS of UHPFRC.

人工智能驱动的超高性能纤维混凝土早期抗压强度预测。
超高性能纤维增强混凝土(UHPFRC)是一种特殊类型的胶凝复合材料,具有优异的机械和耐久性性能。实现这些性能需要保持较低的水灰比,优化骨料粒度分布,并整合纤维增强。近年来,UHPFRCs的开发和应用有一个显著的趋势。然而,仍需要人工智能(AI)方法来预测UHPFRC的早期抗压强度(CS),并确定关键的输入因素,以实现最佳配合比的优化设计。因此,本研究选择5个AI模型来评估早期CS的预测准确性。这些模型包括支持向量回归(SVR)、随机森林(RF)、人工神经网络(ANN)、梯度增强(GB)和高斯过程回归(GPR)。作为评估模型性能和进行误差分析的一部分,本研究调查了五个模型在训练和测试数据集之间的预测准确性差异。此外,通过特征重要性分析,探讨输入变量对早期CS的影响。结果表明,GPR和SVR模型具有较高的预测精度(R2 > 0.90),优于ANN、RF和GB模型。水、高效减水剂、养护温度和纤维含量是影响早期CS最显著的控制参数。通过分析显著输入变量和早期CS之间的相互作用,提出了最佳表现的推荐包含水平。具体建议水分含量保持在145 ~ 155 kg/m2之间,高效减水剂含量保持在30 ~ 40 kg/m2之间,纤维含量保持在200 kg/m2以上。这些建议旨在实现理想的早期CS特征。综上所述,人工智能模型可以有效改善UHPFRC早期CS的监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信