Data-driven moment-carrying capacity prediction of hybrid beams consisting of UHPC-NSC using machine learning-based models
Author:
Publisher
Elsevier BV
Subject
Safety, Risk, Reliability and Quality,Building and Construction,Architecture,Civil and Structural Engineering
Reference70 articles.
1. Improvement of fresh and hardened properties of a sustainable HFRSCC using various powders as multi-blended binders;Donmez;Constr Build Mater,2023
2. Flexural performance of V-shaped RC folded plates: The role of plate thickness and fiber hybridization;Katlav;Constr Build Mater,2023
3. Flexural strengthening of reinforced concrete beams or slabs using ultra-high performance concrete (UHPC): A state of the art review;Zhu;Eng Struct,2020
4. Mechanical properties of ultra-high-performance fiber-reinforced concrete: A review;Yoo;Cem Concr Compos,2016
5. Crack propagation behavior of ultra-high-performance concrete (UHPC) reinforced with hybrid steel fibers under flexural loading;Niu;Constr Build Mater,2021
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Machine learning-based estimation of the out-of-plane displacement of brick infill exposed to earthquake shaking;Engineering Applications of Artificial Intelligence;2024-10
2. AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface;Materials Today Communications;2024-08
3. Estimation of the shear strength of UHPC beams via interpretable deep learning models: Comparison of different optimization techniques;Materials Today Communications;2024-08
4. Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms;Structural Concrete;2024-05-19
5. Machine and deep learning-based prediction of flexural moment capacity of ultra-high performance concrete beams with/out steel fiber;Asian Journal of Civil Engineering;2024-05-10
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3