Compressive strength prediction and optimization design of sustainable concrete based on squirrel search algorithm-extreme gradient boosting technique

被引:10
|
作者
Li, Enming [1 ]
Zhang, Ning [2 ]
Xi, Bin [3 ]
Zhou, Jian [4 ]
Gao, Xiaofeng [5 ]
机构
[1] Univ Politecn Madrid, ETSI Minas & Energia, Madrid 28003, Spain
[2] Leibniz Inst Ecol Urban & Reg Dev IOER, D-01217 Dresden, Germany
[3] Politecn Milan, Dept Civil & Environm Engn, I-20133 Milan, Italy
[4] Cent South Univ, Sch Resources & Safety Engn, Changsha 410083, Peoples R China
[5] Chongqing Univ, Coll Environm & Ecol, Key Lab Three Gorges Reservoir Reg Ecoenvironm, Minist Educ, Chongqing 400045, Peoples R China
来源
关键词
sustainable concrete; fly ash; slay; extreme gradient boosting technique; squirrel search algorithm; parametric analysis; MECHANICAL-PROPERTIES; FURNACE SLAG; FLY-ASH; PERFORMANCE; AGGREGATE;
D O I
10.1007/s11709-023-0997-3
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Concrete is the most commonly used construction material. However, its production leads to high carbon dioxide (CO2) emissions and energy consumption. Therefore, developing waste-substitutable concrete components is necessary. Improving the sustainability and greenness of concrete is the focus of this research. In this regard, 899 data points were collected from existing studies where cement, slag, fly ash, superplasticizer, coarse aggregate, and fine aggregate were considered potential influential factors. The complex relationship between influential factors and concrete compressive strength makes the prediction and estimation of compressive strength difficult. Instead of the traditional compressive strength test, this study combines five novel metaheuristic algorithms with extreme gradient boosting (XGB) to predict the compressive strength of green concrete based on fly ash and blast furnace slag. The intelligent prediction models were assessed using the root mean square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), and variance accounted for (VAF). The results indicated that the squirrel search algorithm-extreme gradient boosting (SSA-XGB) yielded the best overall prediction performance with R2 values of 0.9930 and 0.9576, VAF values of 99.30 and 95.79, MAE values of 0.52 and 2.50, RMSE of 1.34 and 3.31 for the training and testing sets, respectively. The remaining five prediction methods yield promising results. Therefore, the developed hybrid XGB model can be introduced as an accurate and fast technique for the performance prediction of green concrete. Finally, the developed SSA-XGB considered the effects of all the input factors on the compressive strength. The ability of the model to predict the performance of concrete with unknown proportions can play a significant role in accelerating the development and application of sustainable concrete and furthering a sustainable economy.
引用
收藏
页码:1310 / 1325
页数:16
相关论文
共 50 条
  • [21] Prediction of mining induced subsidence by sparrow search algorithm with extreme gradient boosting and TOPSIS method
    Chun Xu
    Keping Zhou
    Xin Xiong
    Feng Gao
    Yan Lu
    Acta Geotechnica, 2023, 18 : 4993 - 5009
  • [22] Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm
    Feng, Mengdan
    Duan, Yonghui
    Wang, Xiang
    Zhang, Jingyi
    Ma, Lanlan
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [23] Carbon price prediction based on decomposition technique and extreme gradient boosting optimized by the grey wolf optimizer algorithm
    Mengdan Feng
    Yonghui Duan
    Xiang Wang
    Jingyi Zhang
    Lanlan Ma
    Scientific Reports, 13
  • [24] Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach
    Feng, De-Cheng
    Liu, Zhen-Tao
    Wang, Xiao-Dan
    Chen, Yin
    Chang, Jia-Qi
    Wei, Dong-Fang
    Jiang, Zhong-Ming
    CONSTRUCTION AND BUILDING MATERIALS, 2020, 230
  • [25] Prediction of the Compressive Strength of Rubberized Concrete Based on Machine Learning Algorithm
    Hai-Bang Ly
    CIGOS 2021, EMERGING TECHNOLOGIES AND APPLICATIONS FOR GREEN INFRASTRUCTURE, 2022, 203 : 1907 - 1915
  • [26] Forensic-based investigation-optimized extreme gradient boosting system for predicting compressive strength of ready-mixed concrete
    Chou, Jui-Sheng
    Chen, Li-Ying
    Liu, Chi-Yun
    JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, 2023, 10 (01) : 425 - 445
  • [27] Shear Strength Prediction of FRP-reinforced Concrete Beams Using an Extreme Gradient Boosting Framework
    Kaveh, Ali
    Javadi, Seyed Mohammad
    Moghanni, Roya Mahdipour
    PERIODICA POLYTECHNICA-CIVIL ENGINEERING, 2022, 66 (01): : 18 - 29
  • [28] Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs
    Andalib, Amir
    Aminnejad, Babak
    Lork, Alireza
    ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2022, 2022
  • [29] Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization附视频
    Wengang Zhang
    Chongzhi Wu
    Haiyi Zhong
    Yongqin Li
    Lin Wang
    Geoscience Frontiers, 2021, (01) : 469 - 477
  • [30] A novel ant colony-optimized extreme gradient boosting machine for estimating compressive strength of recycled aggregate concrete
    Nhat-Duc Hoang
    Multiscale and Multidisciplinary Modeling, Experiments and Design, 2024, 7 : 375 - 394