Experimental and Data-Driven analysis on compressive strength of steel fibre reinforced high strength concrete and mortar at elevated temperature

被引:16
|
作者
Li, Shan [1 ]
Liew, J. Y. Richard [1 ]
机构
[1] Natl Univ Singapore, Dept Civil & Environm Engn, E1A-07-03,1 Engn Dr 2, Singapore 117576, Singapore
基金
新加坡国家研究基金会;
关键词
Data analytic models; Fire; High temperature tests; High strength concrete; Machine learning; steel fibre; HIGH-PERFORMANCE CONCRETE; REACTIVE POWDER CONCRETE; MECHANICAL-PROPERTIES; FIRE RESISTANCE; RESIDUAL STRENGTH; STRAIN BEHAVIOR; CEMENT PASTE; EXPOSURE; AGGREGATE; MICROSTRUCTURE;
D O I
10.1016/j.conbuildmat.2022.127845
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The compressive strength of concrete and mortar under thermal exposure can be determined using the method of elevated-temperature strength test or residual strength test after cooling from heating. Based on the experimental findings reported in this study, the results obtained by these two methods should not be used interchangeably because the tested high strength mortar exhibits distinctively different trend in the change of its compressive strength and elastic modulus when tested under these two conditions. The addition of steel microfibres is found to improve the compressive strength, elastic modulus and post-peak ductility of high strength mortar at elevated temperatures, but such beneficial effect becomes insignificant in residual strength tests after cooling. A database containing 674 concrete and mortar specimens tested at elevated temperatures is established. Using this database, a prediction model based on XGBoost algorithm is developed for the compressive strength retention factors of steel fibre reinforced concrete and mortar at elevated temperatures. This model is shown to improve the accuracy of prediction by 21% compared to the tabulated data in EN1992-1-2 and outperform the other seven machine learning methods evaluated in this study, including Random Forests, Artificial Neural Network, AdaBoost, k-Nearest Neighbours Regression, Multivariate Adaptive Regression Splines, Support Vector Regression and Linear Regression. The XGBoost model shows that the top four most important factors that determine the compressive strength of concrete and mortar at elevated temperatures are heating temperature, compressive strength of concrete and mortar at room temperature, content of steel fibre and aspect ratio of specimen. Based on this finding, less important factors are eliminated from the input features to improve the computational efficiency of the prediction model.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Data-driven compressive strength prediction of steel fiber reinforced concrete (SFRC) subjected to elevated temperatures using stacked machine learning algorithms
    Shafighfard, Torkan
    Bagherzadeh, Faramarz
    Rizi, Rana Abdollahi
    Yoo, Doo-Yeol
    JOURNAL OF MATERIALS RESEARCH AND TECHNOLOGY-JMR&T, 2022, 21 : 3777 - 3794
  • [2] Effect of Temperature on Compressive Strength of Steel Fibre Reinforced Concrete
    Jessie, Anita J.
    Santhi, A. S.
    JOURNAL OF APPLIED SCIENCE AND ENGINEERING, 2019, 22 (02): : 233 - 238
  • [3] A data-driven study for evaluating the compressive strength of high-strength concrete
    Wei, Yufeng
    Han, Aiguo
    Xue, Xinhua
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2021, 12 (12) : 3585 - 3595
  • [4] Influence of high temperature on the mechanical properties of hybrid fibre reinforced normal and high strength concrete
    Varona, F. B.
    Baeza, F. J.
    Bru, D.
    Ivorra, S.
    CONSTRUCTION AND BUILDING MATERIALS, 2018, 159 : 73 - 82
  • [5] Data-driven prediction of compressive strength for ultra-high performance concrete exposed to elevated temperatures
    Yu, Xiaoqing
    Chen, Canhui
    Xie, Jinwei
    Dong, Shuxiong
    Dai, Kaiyao
    Lin, Youzhu
    Liu, Kaihua
    MATERIALS TODAY COMMUNICATIONS, 2025, 42
  • [6] EXPERIMENTAL STUDY ON MECHANICAL PROPERTIES OF FIBRE REINFORCED HIGH STRENGTH CONCRETE WITH POLYPROPYLENE AND STEEL FIBRE
    Kumar, M. Sathish
    Arunachalam, K.
    JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY, 2019, 20 (01): : 313 - 325
  • [7] Compressive strength and failure behaviour of fibre reinforced concrete at elevated temperatures
    Shaikh, F. U. A.
    Taweel, M.
    ADVANCES IN CONCRETE CONSTRUCTION, 2015, 3 (04) : 283 - 293
  • [8] Machine learning-driven predictive models for compressive strength of steel fiber reinforced concrete subjected to high temperatures
    Alyousef, Rayed
    Rehman, Muhammad Faisal
    Khan, Majid
    Fawad, Muhammad
    Khan, Asad Ullah
    Hassan, Ahmed M.
    Ghamry, Nivin A.
    CASE STUDIES IN CONSTRUCTION MATERIALS, 2023, 19
  • [9] A data-driven study for evaluating the compressive strength of high-strength concrete
    Yufeng Wei
    Aiguo Han
    Xinhua Xue
    International Journal of Machine Learning and Cybernetics, 2021, 12 : 3585 - 3595
  • [10] Residual strength of steel fibre reinforced rubberised UHPC under elevated temperatures
    Lyu, Xin
    Elchalakani, Mohamed
    Ahmed, Tanvir
    Sadakkathulla, Mohamed Ali
    Youssf, Osama
    JOURNAL OF BUILDING ENGINEERING, 2023, 76