Publication: Evaluating enhanced predictive modeling of foam concrete compressive strength using artificial intelligence algorithms
Date
2024
Authors
Abdellatief M.
Wong L.S.
Din N.M.
Mo K.H.
Ahmed A.N.
El-Shafie A.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
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), 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-value, which equals 0.98, while the R2 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. ? 2024 Elsevier Ltd
Description
Keywords
Cements , Concrete aggregates , Concrete mixing , Forecasting , Learning algorithms , Machine learning , Mean square error , Multilayer neural networks , Parameter estimation , Regression analysis , Sensitivity analysis , Artificial intelligence algorithms , Compressive strength prediction- parametric analyse , Foam concretes , Gaussian process regression , Input variables , Machine learning algorithms , Parametric analysis , Regression algorithms , Strength prediction , Support vector regressions , Compressive strength