Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete
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 associa...
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| Vydáno v: | Engineering applications of artificial intelligence Ročník 136; s. 109053 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.10.2024
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| Témata: | |
| ISSN: | 0952-1976 |
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| 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.
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| AbstractList | 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.
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| ArticleNumber | 109053 |
| Author | Yoo, Doo-Yeol Kazemi, Farzin Shafighfard, Torkan Asgarkhani, Neda |
| Author_xml | – sequence: 1 givenname: Torkan orcidid: 0000-0002-4210-3150 surname: Shafighfard fullname: Shafighfard, Torkan organization: Institute of Fluid Flow Machinery, Polish Academy of Sciences, Generala Jozefa Fiszera 14, 80-231, Gdańsk, Poland – sequence: 2 givenname: Farzin orcidid: 0000-0002-2448-1465 surname: Kazemi fullname: Kazemi, Farzin email: Farzin.kazemi@pg.edu.pl organization: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233, Gdańsk, Poland – sequence: 3 givenname: Neda orcidid: 0000-0002-0756-8438 surname: Asgarkhani fullname: Asgarkhani, Neda organization: Faculty of Civil and Environmental Engineering, Gdańsk University of Technology, ul. Narutowicza 11/12, 80-233, Gdańsk, Poland – sequence: 4 givenname: Doo-Yeol surname: Yoo fullname: Yoo, Doo-Yeol email: dyyoo@yonsei.ac.kr organization: Department of Architecture and Architectural Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Republic of Korea |
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| Keywords | Steel fiber Machine learning algorithms Compressive strength Cost and carbon emission High-performance alkali-activated concrete |
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| Snippet | High-performance alkali-activated concrete (HP-AAC) is acknowledged as a cementless and environmentally friendly material. It has recently received a... |
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| SubjectTerms | Compressive strength Cost and carbon emission High-performance alkali-activated concrete Machine learning algorithms Steel fiber |
| Title | Machine-learning methods for estimating compressive strength of high-performance alkali-activated concrete |
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