Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete-Reference-Cited by-同舟云学术

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Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete

Published:2024-10 Issue: Volume:136 Page:109053
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Shafighfard Torkan^ORCID,Kazemi Farzin^ORCID,Asgarkhani Neda^ORCID,Yoo Doo-Yeol

Publisher

Elsevier BV

Reference58 articles.

1. Assessment of compressive strength of Ultra-high Performance Concrete using deep machine learning techniques;Abuodeh;Appl. Soft Comput.,2020

2. Proposing several model techniques including ANN and M5P-tree to predict the compressive strength of geopolymer concretes incorporated with nano-silica;Ahmed;Environ. Sci. Pollut. Control Ser.,2022

3. Innovative modeling techniques including MEP, ANN and FQ to forecast the compressive strength of geopolymer concrete modified with nanoparticles;Ahmed;Neural Comput. Appl.,2023

4. Cements of yesterday and today: concrete of tomorrow;Aıtcin;Cement Concr. Res.,2000

5. Greenhouse gas emissions in the construction industry: an analysis and evaluation of a concrete supply chain;Akan;J. Clean. Prod.,2017

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of Ultra-High-Performance Concrete (UHPC) Properties Using Gene Expression Programming (GEP);Buildings;2024-08-28

2. Experimental Investigation on the Seismic Performance of Novel Prefabricated Composite RC Shear Walls with Concrete-Filled Steel Tube Frame;Buildings;2024-08-27

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