Abdellatief, Mohamed and Wong, Leong Sing and Din, Norashidah Md and Mo, Kim Hung and Ahmed, Ali Najah and El-Shafie, Ahmed (2024) Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms. Materials Today Communications, 40. p. 110022. ISSN 2352-4928, DOI https://doi.org/10.1016/j.mtcomm.2024.110022.
Full text not available from this repository.Abstract
Artificial intelligence algorithms have recently demonstrated their efficacy in accurately predicting concrete properties by optimizing mixing proportions and overcoming design limitations. In this regard, foam concrete (FC) production presents a unique challenge, necessitating extensive experimental trials to attain specific properties such as compressive strength (CS). In this context, linear regression (LR), support vector regression (SVR), a multilayer-perceptron artificial neural network (MLP-ANN), and Gaussian process regression (GPR) algorithms, were used to predict the CS of FC. 261 experimental results were utilized, incorporating input variables such as density, water-to-cement ratio, and fine aggregate-to-cement ratio. During the training phase, 75 % of the experimental dataset was utilized. The experimental data is then validated using metrics such as coefficient of determination (R2), 2 ), root mean square error, and root mean error. In comparison, the GPR algorithm reveals high-accuracy towards the estimation of CS, as proved by its high R2 2-value, which equals 0.98, while the R2 2 for ANN, SVR, and LR are 0.97, 0.90, and 0.89, respectively. Additionally, parametric and sensitivity analyses were used to assess the performance of the GPR and LR algorithms. Results revealed that density exerted the most significant influence on CS, with the GPR model showing a pronounced negative impact of fine aggregate-to- cement ratio on CS, particularly in low-density FC, contrasting with the LR model. This study confirmed that the GPR algorithm provided reliable accuracy in predicting the CS of FC. Therefore, it is recommended to utilize the prediction algorithms within the range of input variables employed in this investigation for optimal results.
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Higher Education Malaysia through the Higher Institution Center of Excellence (HICoE 2023-JPT (BPKI) 1000/016/018/34 (5)) |
| Uncontrolled Keywords: | Foam concrete; Machine learning algorithms; Compressive strength prediction; Parametric; Analysis |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Faculty of Engineering > Department of Civil Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 09 Apr 2025 04:53 |
| Last Modified: | 09 Apr 2025 04:53 |
| URI: | http://eprints.um.edu.my/id/eprint/46713 |
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