Inhomogeneities of carbonation depth distributions in recycled aggregate concretes: A visualisation and quantification study

被引：12
作者：
Mi, Renjie [1 ,2 ,3 ,4 ]
Pan, Ganghua [1 ,2 ]
机构：
[1] Southeast Univ, Sch Mat Sci & Engn, Nanjing 211189, Peoples R China
[2] Southeast Univ, Jiangsu Key Lab Construct Mat, Nanjing 211189, Peoples R China
[3] City Univ Hong Kong, Dept Architecture & Civil Engn, Hong Kong, Peoples R China
[4] Hong Kong Polytech Univ, Dept Civil & Environm Engn, Hong Kong, Peoples R China
来源：
CONSTRUCTION AND BUILDING MATERIALS | 2022年 / 330卷
基金：
中国国家自然科学基金;
关键词：
Recycled aggregate concrete; Carbonation depth distribution; Inhomogeneity; Visualisation; Quantification; COARSE AGGREGATE; FLY-ASH; RESISTANCE; PERFORMANCE; CO2;
D O I：
10.1016/j.conbuildmat.2022.127300
中图分类号：
TU [建筑科学];
学科分类号：
0813 ;
摘要：
The carbonation depths nonuniformly distribute in recycled aggregate concrete (RAC) because it has two kinds of mortars and three kinds of interfacial transition zones; but the inhomogeneity of the distribution is underestimated. Hereby, the inhomogeneities in RACs prepared with modelled industrially-produced and laboratory produced recycled coarse aggregates (RCAs) were visualised and quantified using a proposed method. Afterwards, the influences of RCA types, RCA replacement ratios and strengthening methods of RCA on the average carbonation depths, their errors and the maximum carbonation depths were examined. The results showed that the carbonation depth distributions of RACs presented clear inhomogeneities. Utilising the CO2 curing technology for RCA might not reduce the inhomogeneities; but light effects can be observed through selecting a proper RCA or using a nano-SiO2 slurry soaking method for RCA. Reducing the maximum carbonation depth of RAC by selecting an appropriate RCA was more effective compared with by using the nano-SiO2 slurry soaking method. Besides, predicting the carbonation degree of RAC by using the maximum carbonation depth was more conservative than by using the average carbonation depth.
引用
收藏
页数：11
相关论文
共 15 条
  • [1] Carbonation resistance study and inhomogeneity evolution of recycled aggregate concretes under loading effects
    Mi, Renjie
    Liew, K. M.
    Pan, Ganghua
    Kuang, Tong
    CEMENT & CONCRETE COMPOSITES, 2021, 118
  • [2] Prediction model of carbonation depth for recycled aggregate concrete
    Zhang, Kaijian
    Xiao, Jianzhuang
    CEMENT & CONCRETE COMPOSITES, 2018, 88 : 86 - 99
  • [3] A comparison study of aggregate carbonation and concrete carbonation for the enhancement of recycled aggregate pervious concrete
    Tang, Bowen
    Fan, Meng
    Yang, Zhengquan
    Sun, Yongshuai
    Yuan, Linjuan
    CONSTRUCTION AND BUILDING MATERIALS, 2023, 371
  • [4] Machine learning prediction of carbonation depth in recycled aggregate concrete incorporating SCMs
    Nunez, Itzel
    Nehdi, Moncef L.
    CONSTRUCTION AND BUILDING MATERIALS, 2021, 287
  • [5] Carbonation depth assessment of recycled aggregate concrete: An application of conformal prediction intervals
    Hajibabaee, Parisa
    Behnood, Ali
    Ngo, Tuan
    Golafshani, Emadaldin Mohammadi
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 268
  • [6] Model for practical carbonation depth prediction for high volume fly ash concrete and recycled aggregate concrete
    Carevic, Vedran
    Ignjatovic, Ivan
    Dragas, Jelena
    CONSTRUCTION AND BUILDING MATERIALS, 2019, 213 : 194 - 208
  • [7] Microscopic Mechanism Study on Carbonation Resistance of Recycled Aggregate Concrete
    Yang, Haifeng
    Deng, Zhiheng
    Li, Xueliang
    ADVANCED ENGINEERING MATERIALS, PTS 1-3, 2011, 194-196 : 1001 - 1006
  • [8] A robust carbonation depth model in recycled aggregate concrete (RAC) using neural network
    Concha, Nolan C.
    EXPERT SYSTEMS WITH APPLICATIONS, 2024, 237
  • [9] Modeling carbonation depth of recycled aggregate concrete containing chlorinated salts
    Qu, Feng
    Xia, Wen
    Sun, Congtao
    Hou, Hailong
    Huang, Bo
    Wang, Gongxun
    Hu, Song
    CONSTRUCTION AND BUILDING MATERIALS, 2024, 430
  • [10] Prediction of carbonation depth for recycled aggregate concrete using ANN hybridized with swarm intelligence algorithms
    Liu, Kaihua
    Alam, M. Shahria
    Zhu, Jiang
    Zheng, Jiakai
    Chi, Lin
    CONSTRUCTION AND BUILDING MATERIALS, 2021, 301 (301)
12