• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

高温作用下废陶瓷混凝土的力学性能、裂缝宽度及扩展:一项综合研究

Mechanical Properties, Crack Width, and Propagation of Waste Ceramic Concrete Subjected to Elevated Temperatures: A Comprehensive Study.

作者信息

Najm Hadee Mohammed, Nanayakkara Ominda, Ahmad Mahmood, Sabri Sabri Mohanad Muayad

机构信息

Department of Civil Engineering, Zakir Husain Engineering College, Aligarh Muslim University, Aligarh 202002, India.

Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215000, China.

出版信息

Materials (Basel). 2022 Mar 23;15(7):2371. doi: 10.3390/ma15072371.

DOI:10.3390/ma15072371
PMID:35407705
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8999623/
Abstract

Waste ceramic concrete (WOC) made from waste ceramic floor tiles has several economic and environmental benefits. Fire is one of the most common disasters in buildings, and WOC is a brittle construction material; therefore, the mechanical properties of WOC structures under high temperatures should be considered. According to previous studies, hybrid fiber can further reduce damage to concrete under high temperatures. Meanwhile, crack width and propagation are among the key characteristics of concrete materials that need to be considered, but few studies have focused on their behavior when subjected to elevated temperatures. The new concrete materials proposed by the authors are WOC and WOC-Hybrid. WOC was prepared with Natural Coarse Aggregates (NCA), Natural Fine Aggregate (NFA), Ordinary Portland Cement (OPC 43 grade), and ceramic waste tiles with 20% replacements for coarse aggregates, 10% replacements for fine aggregates, and 10% replacement for cement. In contrast, WOC-Hybrid was prepared with the addition of hybrid fiber (1% crimped steel fiber and 1% polyvinyl alcohol fiber) in WOC. The specimens were exposed to temperatures of 100-300 °C, and then the specimens were tested for tensile and compressive strength. The present study aims to find a new method to improve concrete resistance to elevated temperatures at the lowest costs by experimental and computational analysis via machine learning models. The application of machine learning models such as artificial neural networks (ANN) and multiple linear regression (MLR) was employed in this study to predict the compressive and tensile strength of concrete. The linear coefficient correlation (R) and mean square error (MSE) were evaluated to investigate the performance of the models. Based on the experimental analysis, the results show that the effect of hybrid fiber on the crack width and propagation is greater than that on the crack width and propagation of WOC and PC after exposure to high temperatures. However, the enhanced effect of hybrid fiber on the mechanical properties, rack width, and propagation decreases after subjecting it to a high-temperature treatment, owing to the melting and ignition of hybrid fibers at high temperatures. Regarding the computational analysis, it was found that the developed MLR model shows higher efficiency than ANN in predicting the compressive and tensile strength of PC, WOC, and WOC-Hybrid concrete.

摘要

由废弃陶瓷地砖制成的废弃陶瓷混凝土(WOC)具有若干经济和环境效益。火灾是建筑物中最常见的灾害之一,而WOC是一种脆性建筑材料;因此,应考虑WOC结构在高温下的力学性能。根据以往的研究,混杂纤维可以进一步减少混凝土在高温下的损伤。同时,裂缝宽度和扩展是混凝土材料需要考虑的关键特性之一,但很少有研究关注它们在高温下的行为。作者提出的新型混凝土材料是WOC和WOC-混杂纤维混凝土。WOC由天然粗骨料(NCA)、天然细骨料(NFA)、43级普通硅酸盐水泥(OPC)和陶瓷废料制成,其中粗骨料替代率为20%,细骨料替代率为10%,水泥替代率为10%。相比之下,WOC-混杂纤维混凝土是在WOC中添加了混杂纤维(1%的卷曲钢纤维和1%的聚乙烯醇纤维)制备而成。将试件暴露在100-300℃的温度下,然后对试件进行抗拉和抗压强度测试。本研究旨在通过机器学习模型进行实验和计算分析,找到一种以最低成本提高混凝土耐高温性能的新方法。本研究应用了人工神经网络(ANN)和多元线性回归(MLR)等机器学习模型来预测混凝土的抗压和抗拉强度。评估了线性系数相关性(R)和均方误差(MSE)以研究模型的性能。基于实验分析,结果表明,混杂纤维对高温后WOC和PC裂缝宽度和扩展的影响大于对其的影响。然而,由于混杂纤维在高温下熔化和着火,高温处理后混杂纤维对力学性能、裂缝宽度和扩展的增强作用降低。关于计算分析,发现所开发的MLR模型在预测PC、WOC和WOC-混杂纤维混凝土的抗压和抗拉强度方面比ANN具有更高的效率。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/09d7e73b86b7/materials-15-02371-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/c9cf8ca84ae8/materials-15-02371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/3ba8eefe1139/materials-15-02371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a366922b7f9d/materials-15-02371-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/8e6ce265dd1a/materials-15-02371-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/caa8d27b822e/materials-15-02371-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a3e222458303/materials-15-02371-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/73148512f4c5/materials-15-02371-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/12d994b37b2d/materials-15-02371-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/6b75d04df17c/materials-15-02371-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/86f662faa90e/materials-15-02371-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/2cce976579ab/materials-15-02371-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/ef0457c8547d/materials-15-02371-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/10ecfad19456/materials-15-02371-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/72d00ccaa0b5/materials-15-02371-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/f57361eaef27/materials-15-02371-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/ba62c2d74d48/materials-15-02371-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/7036825b4b86/materials-15-02371-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/cedc99a800d2/materials-15-02371-g018a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a326baf82ca7/materials-15-02371-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/09aeaa0653cb/materials-15-02371-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/18f58212c58e/materials-15-02371-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/8b88153655a6/materials-15-02371-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/18b44e98ecf5/materials-15-02371-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/09d7e73b86b7/materials-15-02371-g024.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/c9cf8ca84ae8/materials-15-02371-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/3ba8eefe1139/materials-15-02371-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a366922b7f9d/materials-15-02371-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/8e6ce265dd1a/materials-15-02371-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/caa8d27b822e/materials-15-02371-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a3e222458303/materials-15-02371-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/73148512f4c5/materials-15-02371-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/12d994b37b2d/materials-15-02371-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/6b75d04df17c/materials-15-02371-g009a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/86f662faa90e/materials-15-02371-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/2cce976579ab/materials-15-02371-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/ef0457c8547d/materials-15-02371-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/10ecfad19456/materials-15-02371-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/72d00ccaa0b5/materials-15-02371-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/f57361eaef27/materials-15-02371-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/ba62c2d74d48/materials-15-02371-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/7036825b4b86/materials-15-02371-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/cedc99a800d2/materials-15-02371-g018a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/a326baf82ca7/materials-15-02371-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/09aeaa0653cb/materials-15-02371-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/18f58212c58e/materials-15-02371-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/8b88153655a6/materials-15-02371-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/18b44e98ecf5/materials-15-02371-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/95a4/8999623/09d7e73b86b7/materials-15-02371-g024.jpg

相似文献

1
Mechanical Properties, Crack Width, and Propagation of Waste Ceramic Concrete Subjected to Elevated Temperatures: A Comprehensive Study.高温作用下废陶瓷混凝土的力学性能、裂缝宽度及扩展:一项综合研究
Materials (Basel). 2022 Mar 23;15(7):2371. doi: 10.3390/ma15072371.
2
Colour Change of Sustainable Concrete Containing Waste Ceramic and Hybrid Fibre: Effect of Temperature.含废陶瓷和混杂纤维的可持续混凝土的颜色变化:温度的影响
Materials (Basel). 2022 Mar 15;15(6):2174. doi: 10.3390/ma15062174.
3
Experimental Prognostication of Ultra-High-Performance Lightweight Hybrid Fiber-Reinforced Concrete by Using Sintered Fly Ash Aggregate, Palm Oil Shell Aggregate, and Supplementary Cementitious Materials.利用烧结粉煤灰集料、棕榈油壳集料和辅助胶凝材料对超高性能轻质混合纤维增强混凝土进行试验预测
Materials (Basel). 2022 Jul 20;15(14):5051. doi: 10.3390/ma15145051.
4
Hybrid nonlinear regression model versus MARS, MEP, and ANN to evaluate the effect of the size and content of waste tire rubber on the compressive strength of concrete.混合非线性回归模型与多元自适应回归样条、多元逐步回归和人工神经网络用于评估废轮胎橡胶的尺寸和含量对混凝土抗压强度的影响。
Heliyon. 2024 Feb 11;10(4):e25997. doi: 10.1016/j.heliyon.2024.e25997. eCollection 2024 Feb 29.
5
Analyzing the Compressive Strength of Ceramic Waste-Based Concrete Using Experiment and Artificial Neural Network (ANN) Approach.采用实验和人工神经网络(ANN)方法分析陶瓷废料基混凝土的抗压强度。
Materials (Basel). 2021 Aug 11;14(16):4518. doi: 10.3390/ma14164518.
6
Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF.利用机器学习算法(决策树、多层感知器神经网络、支持向量机和随机森林)及实验数据预测塑性混凝土的力学性能
Polymers (Basel). 2022 Apr 13;14(8):1583. doi: 10.3390/polym14081583.
7
Tension Stiffening and Cracking Behavior of Axially Loaded Alkali-Activated Concrete.轴向加载碱激发混凝土的拉伸硬化与开裂行为
Materials (Basel). 2023 May 31;16(11):4120. doi: 10.3390/ma16114120.
8
Mechanical Properties and Crack Resistance of Basalt Fiber Self-Compacting High Strength Concrete: An Experimental Study.玄武岩纤维自密实高强混凝土的力学性能与抗裂性:一项试验研究
Materials (Basel). 2023 Jun 14;16(12):4374. doi: 10.3390/ma16124374.
9
Effect of Short Fibers on Fracture Properties of Epoxy-Based Polymer Concrete Exposed to High Temperatures.短纤维对高温下环氧基聚合物混凝土断裂性能的影响。
Polymers (Basel). 2023 Feb 21;15(5):1078. doi: 10.3390/polym15051078.
10
Influence of Crimped Steel Fibre on Properties of Concrete Based on an Aggregate Mix of Waste and Natural Aggregates.基于废弃与天然骨料混合集料的卷曲钢纤维对混凝土性能的影响
Materials (Basel). 2020 Apr 17;13(8):1906. doi: 10.3390/ma13081906.

引用本文的文献

1
Influence of elevated temperature exposure on the residual compressive strength and radiation shielding efficiency of ordinary concrete incorporating granodiorite and ceramic powders.
Sci Rep. 2025 Jan 28;15(1):3572. doi: 10.1038/s41598-024-85043-2.
2
Systematic literature review on the application of machine learning for the prediction of properties of different types of concrete.关于机器学习在预测不同类型混凝土性能方面应用的系统文献综述。
PeerJ Comput Sci. 2024 May 16;10:e1853. doi: 10.7717/peerj-cs.1853. eCollection 2024.
3
Properties and Applications of Geopolymer Composites: A Review Study of Mechanical and Microstructural Properties.地质聚合物复合材料的性能与应用:力学和微观结构性能综述研究

本文引用的文献

1
Colour Change of Sustainable Concrete Containing Waste Ceramic and Hybrid Fibre: Effect of Temperature.含废陶瓷和混杂纤维的可持续混凝土的颜色变化:温度的影响
Materials (Basel). 2022 Mar 15;15(6):2174. doi: 10.3390/ma15062174.
2
Prediction of irrigation groundwater quality parameters using ANN, LSTM, and MLR models.使用 ANN、LSTM 和 MLR 模型预测灌溉地下水质量参数。
Environ Sci Pollut Res Int. 2022 Mar;29(14):21067-21091. doi: 10.1007/s11356-021-17084-3. Epub 2021 Nov 8.
3
Supervised Learning Methods for Modeling Concrete Compressive Strength Prediction at High Temperature.
Materials (Basel). 2022 Nov 21;15(22):8250. doi: 10.3390/ma15228250.
4
Fly Ash-Based Geopolymer Composites: A Review of the Compressive Strength and Microstructure Analysis.基于粉煤灰的地质聚合物复合材料:抗压强度与微观结构分析综述
Materials (Basel). 2022 Oct 12;15(20):7098. doi: 10.3390/ma15207098.
5
Predictive Modeling of Compressive Strength for Concrete at Super Early Age.超早期混凝土抗压强度的预测模型
Materials (Basel). 2022 Jul 14;15(14):4914. doi: 10.3390/ma15144914.
6
Destructive and Non-Destructive Evaluation of Fibre-Reinforced Concrete: A Comprehensive Study of Mechanical Properties.纤维增强混凝土的破坏性与非破坏性评估:力学性能综合研究
Materials (Basel). 2022 Jun 23;15(13):4432. doi: 10.3390/ma15134432.
用于高温下混凝土抗压强度预测建模的监督学习方法
Materials (Basel). 2021 Apr 15;14(8):1983. doi: 10.3390/ma14081983.
4
Solid industrial wastes and their management in Asegra (Granada, Spain).阿塞格拉(西班牙格拉纳达)的固体工业废弃物及其管理
Waste Manag. 2005;25(10):1075-82. doi: 10.1016/j.wasman.2005.02.023. Epub 2005 Jun 4.