prikaz prve stranice dokumenta The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
  Download
PDF 1.65 MB
Scientific paper - Original scientific paper
The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
Computational Engineering and Physical Modeling, 4 (2021), 4; 1-25. https://doi.org/10.22115/cepm.2021.297016.1181
Khademi, Atefehossadat; Behfarnia, Kiachehr; Kalman Šipoš, Tanja; Miličević, Ivana

Cite this document

Khademi, A., Behfarnia, K., Kalman Šipoš, T. & Miličević, I. (2021). The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete. Computational Engineering and Physical Modeling, 4. (4), 1-25. doi: 10.22115/cepm.2021.297016.1181

Khademi, Atefehossadat, et al. "The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete." Computational Engineering and Physical Modeling, vol. 4, no. 4, 2021, pp. 1-25. https://doi.org/10.22115/cepm.2021.297016.1181

Khademi, Atefehossadat, Kiachehr Behfarnia, Tanja Kalman Šipoš and Ivana Miličević. "The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete." Computational Engineering and Physical Modeling 4, no. 4 (2021): 1-25. https://doi.org/10.22115/cepm.2021.297016.1181

Khademi, A., et al. (2021) 'The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete', Computational Engineering and Physical Modeling, 4(4), pp. 1-25. doi: 10.22115/cepm.2021.297016.1181

Khademi A, Behfarnia K, Kalman Šipoš T, Miličević I. The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete. Computational Engineering and Physical Modeling [Internet]. 2021 [cited 2025 February 20];4(4):1-25. doi: 10.22115/cepm.2021.297016.1181

A. Khademi, K. Behfarnia, T. Kalman Šipoš and I. Miličević, "The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete", Computational Engineering and Physical Modeling, vol. 4, no. 4, pp. 1-25, 2021. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:293122. [Accessed: 20 February 2025]

Please login to the repository to save this object to your list.
Metadata
Title (english)The Use of Machine Learning Models in Estimating the Compressive Strength of Recycled Brick Aggregate Concrete
AuthorAtefehossadat Khademi
AuthorKiachehr Behfarnia
AuthorTanja Kalman Šipoš
AuthorIvana Miličević
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
The focus of this study is to investigate the applicability of Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) in modeling the compressive strength of Recycled Brick Aggregate Concrete (RBAC). A comparative study on the application of the aforementioned models is developed based on statistical tools such as coefficient of determination, mean absolute error, root mean squared error, and some others, and the application potential of each of these models is investigated. To study the effects of RBAC factors on the performance of representative data-driven models, the Sensitivity Analysis (SA) method is used. The findings revealed that ANN with R2 value of 0.9102 has a great application potential in predicting the compressive strength of concrete. In the absence of ANN, ANFIS with R2 value of 0.8538 is also an excellent substitute for predictions. MLR was shown to be less effective than the preceding models and is only recommended for preliminary estimations. In addition, Subsequent sensitivity analysis on the database indicates the reliability of the prediction models have a strong correlation to the number of input parameters. The application of ANN and ANFIS as a precursor to traditional methods can eliminate the need for old-style tests, thus, constituting a significant reduction in time and expense needed for design and/or repairs.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleComputational Engineering and Physical Modeling
Numberingvol. 4, no. 4, pp. 1-25
p-ISSN2588-6959
DOIhttps://doi.org/10.22115/cepm.2021.297016.1181
URN:NBNurn:nbn:hr:133:293122
Publication2021
Document URLhttps://www.jcepm.com/article_136944.html
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) icon
Created on2025-02-13 09:21:10