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作者:

Fan, Mengtian (Fan, Mengtian.) | Li, Yue (Li, Yue.) (学者:李悦) | Shen, Jiale (Shen, Jiale.) | Jin, Kaikai (Jin, Kaikai.) | Shi, Junjie (Shi, Junjie.)

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摘要:

This paper employs Support Vector Regression (SVR), Random Forest Regression (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB) algorithms to establish the compressive strength prediction models for Recycled Aggregate Concrete (RAC) and analyze the influence of ten inputs on RAC compressive strength. Combined with the best prediction model, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied for multi-objective optimization of mixture proportions in RAC addressing cost, carbon emissions, and compressive strength as key objectives. The results demonstrate that the RAC compressive strength prediction model using the GB algorithm exhibits the highest accuracy, with the R2 value of 0.99 for the training set and 0.85 for the testing set. Feature importance and SHAP analysis reveal that cement and water contents are the primary factors affecting the compressive strength of RAC. Among the three mineral admixtures including Silica Fume (SF), Fly Ash (FA), and Ground Granulated Blast Furnace Slag (GGBFS), SF exhibits superior improvement in the compressive strength of RAC compared to FA and GGBFS. Partial dependency plot analysis indicates that concrete strength remains unaffected by recycled coarse aggregate content within the range of 0 to 300 kg/m3. Pareto fronts of a tri-objective mixture optimization problem for RAC are successfully obtained through the GB model combined with the NSGA-II algorithm, which can guide the optimization of RAC preparation.

关键词:

Multi -objective optimization Strength prediction Machine learning Recycled aggregate concrete

作者机构:

  • [ 1 ] [Fan, Mengtian]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China
  • [ 2 ] [Li, Yue]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China
  • [ 3 ] [Shen, Jiale]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China
  • [ 4 ] [Jin, Kaikai]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China
  • [ 5 ] [Shi, Junjie]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China

通讯作者信息:

  • [Li, Yue]Beijing Univ Technol, Key Lab Urban Secur & Disaster Engn, Beijing Key Lab Earthquake Engn & Struct Retrofit, Minist Educ, Beijing 100124, Peoples R China;;

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来源 :

ADVANCES IN ENGINEERING SOFTWARE

ISSN: 0965-9978

年份: 2024

卷: 192

被引次数:

WoS核心集被引频次:

SCOPUS被引频次: 18

ESI高被引论文在榜: 0 展开所有

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