Machine learning-aided design and prediction of cementitious composites containing graphite and slag powder

被引:88
作者
Sun, Junbo [1 ]
Ma, Yongzhi [2 ]
Li, Jianxin [2 ]
Zhang, Junfei [3 ]
Ren, Zhenhua [4 ]
Wang, Xiangyu [5 ,6 ]
机构
[1] Curtin Univ, Sch Design & Built Environm, Perth, WA 6102, Australia
[2] Deakin Univ, Sch Informat Technol, Geelong, Vic, Australia
[3] Hebei Univ Technol, Sch Civil & Transportat Engn, 5340 Xiping Rd, Tianjin 300401, Peoples R China
[4] Hunan Inst Engn, Sch Bldg Engn, Xiangtan 411228, Hunan, Peoples R China
[5] East China Jiao Tong Univ, Sch Civil Engn & Architecture, Nanchang 330013, Jiangxi, Peoples R China
[6] Curtin Univ, Australasian Joint Res Ctr Bldg Informat Modellin, Perth, WA 6102, Australia
来源
JOURNAL OF BUILDING ENGINEERING | 2021年 / 43卷
关键词
Random forest; Beetle antennae search; Machine learning; Graphite; Waste slag; Compressive strength; Electrical resistivity;
D O I
10.1016/j.jobe.2021.102544
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The electrically conductive cementitious composite (ECCC) offers plenty of advantages such as high conductivity and strain sensitivity. The ECCC can also act as a conductive sensor in a cathodic protection system for structural health monitoring. Before the ECCC application, it is essential to understand and predict the uniaxial compressive stress (UCS) and electrical resistivity. In this study, we produced ECCC with three conductive fillers: graphite powder (GP), waste steel slag (SS) as well as ground granulated blast-furnace slag (GGBS). By changing the content levels of the three conductive fillers, cement and curing ages, we prepared 81 mixture proportions for UCS test and 108 mixture proportions for resistivity test. The results show that although GP improves the conductivity more significantly than the other conductive fillers but it simultaneously has a higher negative influence on UCS. Meanwhile, slag solids (GGBS and SS) enhance the conductive performance but reduce UCS after their replacement ratio is larger than 20%. Compared with GGBS, ECCC containing SS has higher UCS and conductivity. Besides, we proposed a random forest (RF) based machine learning model to predict the UCS and resistivity. The hyperparameters of the RF model were tuned by the beetle antennae search (BAS) algorithm. This hybrid BAS-RF model has high prediction accuracy, as indicated by high correlation coefficients on test sets (0.986 for UCS and 0.98 for resistivity, respectively). We simulated the influence of different conductive fillers on UCS and conductivity using the developed BAS-RF model. The simulation results agree well with the results obtained by laboratory experiments. This study offers a new idea to use waste slags to produce ECCC and paves the way to intelligent construction.
引用
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页数:14
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