Search
Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
Abstract
High-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a substantial amount of interest not only due to the potential it has for being used instead of ordinary concrete but also owing to the concerns associated with climate change, sustainability, reduction of CO2 emissions, and energy consumption. The characteristics and amounts of the ingredients used to produce HP-AAC influence its compressive strength. This study performs a comparative analysis based on machine learning (ML) algorithms to present an ensemble model capable of predicting the compressive strength of HP-AAC. This is in response to the development of sophisticated prediction approaches that seek to lower the cost of experimental tools and labor. An extensive framework including 538 experimental datasets with 18 input parameters are extracted. In addition, stacked ML (SM) models are developed to provide their best base estimator combination with the highest capability. The results show that stacked model (SM-5) with score of 14, and prediction accuracy of 98% following by the largest experiment-to-predicted ratio, provide the best estimations of compressive strength of HP-AAC, which has the lowest error values compare to other 18 ML models. Thereafter, a graphical user interface (GUI) is provided and validated by extra experimental tests for estimating the compressive strength, cost, and carbon emission of HP-AAC. Overall, the significance of the current study highlight the outstanding performance of developed stacked ML and GUI for predicting the compressive strength of HP-ACC, which contribute for the on-going research in this area.
Citations
-
2 4
CrossRef
-
0
Web of Science
-
1 8
Scopus
Authors (4)
-
Torkan Shafighfard
-
Doo-Yeol Yoo
Cite as
Full text
full text is not available in portal
Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
no. 136,
ISSN: 0952-1976 - Language:
- English
- Publication year:
- 2024
- Bibliographic description:
- Shafighfard T., Kazemi F., Asgarkhani N., Yoo D.: Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete// ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE -Vol. 136,iss. Part B (2024), s.109053-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.engappai.2024.109053
- Sources of funding:
-
- Free publication
- Verified by:
- Gdańsk University of Technology
seen 40 times
Recommended for you
Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
- M. Arif,
- F. Jan,
- A. Rezzoug
- + 6 authors
Machine learning-based prediction of preplaced aggregate concrete characteristics
- F. Omidi Moaf,
- F. Kazemi,
- H. S. Abdelgader
- + 1 authors