دورية أكاديمية
أعرض في EDS

Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning.

التفاصيل البيبلوغرافية
العنوان: Compressive strength prediction and low-carbon optimization of fly ash geopolymer concrete based on big data and ensemble learning.
المؤلفون: Jiang P; Zhejiang Tongji Vocational College of Science and Technology, Zhejiang, China., Zhao D; Zhejiang University of Technology, Zhejiang, China., Jin C; Zhejiang University of Technology Engineering Design Group Co. Ltd, Zhejiang, China., Ye S; Zhejiang Tongji Vocational College of Science and Technology, Zhejiang, China., Luan C; Institute of Advanced Study, Chengdu University, Chengdu, China.; School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen, China., Tufail RF; Civil Engineering Department, Wah Campus, COMSATS University Islamabad, Rawalpindi, Pakistan.
المصدر: PloS one [PLoS One] 2024 Sep 12; Vol. 19 (9), pp. e0310422. Date of Electronic Publication: 2024 Sep 12 (Print Publication: 2024).
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Construction Materials*/analysis , Coal Ash*/chemistry , Coal Ash*/analysis , Compressive Strength*, Carbon/chemistry ; Carbon/analysis ; Carbon Dioxide/chemistry ; Carbon Dioxide/analysis ; Machine Learning ; Polymers/chemistry
مستخلص: Portland cement concrete (PCC) is a major contributor to human-made CO2 emissions. To address this environmental impact, fly ash geopolymer concrete (FAGC) has emerged as a promising low-carbon alternative. This study establishes a robust compressive strength prediction model for FAGC and develops an optimal mixture design method to achieve target compressive strength with minimal CO2 emissions. To develop robust prediction models, comprehensive factors, including fly ash characteristics, mixture proportions, curing parameters, and specimen types, are considered, a large dataset comprising 1136 observations is created, and polynomial regression, genetic programming, and ensemble learning are employed. The ensemble learning model shows superior accuracy and generalization ability with an RMSE value of 1.81 MPa and an R2 value of 0.93 in the experimental validation set. Then, the study integrates the developed strength model with a life cycle assessment-based CO2 emissions model, formulating an optimal FAGC mixture design program. A case study validates the effectiveness of this program, demonstrating a 16.7% reduction in CO2 emissions for FAGC with a compressive strength of 50 MPa compared to traditional trial-and-error design. Moreover, compared to PCC, the developed FAGC achieves a substantial 60.3% reduction in CO2 emissions. This work provides engineers with tools for compressive strength prediction and low carbon optimization of FAGC, enabling rapid and highly accurate design of concrete with lower CO2 emissions and greater sustainability.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2024 Jiang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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المشرفين على المادة: 0 (Coal Ash)
7440-44-0 (Carbon)
142M471B3J (Carbon Dioxide)
0 (Polymers)
تواريخ الأحداث: Date Created: 20240912 Date Completed: 20240912 Latest Revision: 20240914
رمز التحديث: 20240914
مُعرف محوري في PubMed: PMC11392388
DOI: 10.1371/journal.pone.0310422
PMID: 39264969
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0310422