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作者:

Li, Yue (Li, Yue.) | Shen, Jiale (Shen, Jiale.) | Lin, Hui (Lin, Hui.) | Li, Yaqiang (Li, Yaqiang.)

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EI Scopus SCIE

摘要:

Traditional mix proportion design methods are difficult to consider the multiple objectives, while the design of economical and environmentally friendly alkali-activated slag-fly ash geopolymer concrete on the premise of guaranteeing compressive strength is an essential and meaningful task. The novelty of this paper is that the optimization design model for alkali-activated slag-fly ash geopolymer concrete considering 28 days compressive strength, cost, and carbon emission was developed based on machine learning and Particle Swarm Optimization (PSO) algorithm. The results show that Random Forest (RF), GB, and Back Propagation Neural Network (BPNN) can achieve a good prediction effect for compressive strength with R2 over 0.85 and 0.70 for training and testing set. Slag and sodium hydroxide (NaOH) contents have remarkable effects on the compressive strength of alkali-activated slag-fly ash geopolymer concrete. The addition of slag is beneficial to enhancement of compressive strength, and the relatively optimal coarse and fine aggregate contents are 1200 kg/m3 and 750 kg/m3. It is noted that alkali-activated geopolymer is suitable for preparation of high-strength concrete. Production cost is need to be paid more attention in the optimization process. The alkali-activated slag-fly ash geopolymer concretes with production cost reduction of 7.6%-10.6% and carbon emission reduction of 77.3%-80.7% at strength grade of C30, and with production cost reduction of 22.5%-27.0% and carbon emission reduction of 76.9%-81.3% at strength grade of C50 are achieved.

关键词:

Strength prediction Alkali-activated geopolymer concrete Optimization design Artificial intelligence

作者机构:

  • [ 1 ] [Li, Yue]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 2 ] [Shen, Jiale]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 3 ] [Lin, Hui]Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Key Lab Urban Secur & Disaster Engn, Minist Educ, Beijing, Peoples R China
  • [ 4 ] [Li, Yaqiang]Beijing Forestry Univ, Coll Soil & Water Conservat, Dept Civil Engn, Beijing 100083, Peoples R China
  • [ 5 ] [Shen, Jiale]Beijing Univ Technol, Coll Architecture & Civil Engn, 100 Pingleyuan, Beijing 100124, Peoples R China

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来源 :

JOURNAL OF BUILDING ENGINEERING

年份: 2023

卷: 75

6 . 4 0 0

JCR@2022

被引次数:

WoS核心集被引频次: 69

SCOPUS被引频次: 74

ESI高被引论文在榜: 0 展开所有

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