Evaluating the mechanical strength prediction performances of fly ash-based MPC mortar with artificial intelligence approaches. (25th June 2022)

• Record Type: Journal Article
• Authors: Haque, M. Aminul; Chen, Bing; Javed, Muhammad Faisal; Jalal, Fazal E.
• Is Part Of: Journal of cleaner production. Volume 355(2022)
• Publication Date: 2022-06-25
• Subjects: Magnesium phosphate cement -- Mechanical strength -- Deep neural network -- Gene expression programming -- Sensitivity analysis; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2022.131815
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Introduction of Fly ash (FA) in the magnesium phosphate cement (MPC) mortars is considered as sustainable way to advance the microstructural characteristics and reduce the manufacturing cost of MPC products. However, artificial intelligence (AI) approaches are still need to forecast the strength properties of MPC compositions blended with FA and estimate the governing input elements for appropriate mix design with suitable contents. For this aims, the current research elected five AI models based on deep neural network (DNN), optimizable gaussian process regressor (OGPR) and gene expression programming (GEP) to judge the prediction accuracy of mechanical strength values of the MPC-FA compounds, where the literature data was collected for training the models. In addition, laboratory tests were conducted in this study for producing the data and validating the recommended AI methods. As is observed, DNN2 having 3 hidden layer and Bayesian optimization based Gaussian process regressor techniques presented prediction skills above 95% with errors below 5% at the training and validation phases. Moreover, sensitivity analysis of each input variable revealed that FA content has the prime impact on strength achievement of MPC-FA mixtures, which was corroborated by the correlation analysis between inputs and outputs of whole data points. Finally, forecasting the mechanical strength properties of FA-based MPC mortars using the DNN2 and OGPR methods might be applied in the practical field for reducing the workload, labor and material ingesting through optimizing the mix combinations. Highlights: Fly ash was added in the MPC mixtures as supplementary cementitious materials. Literature data was collected on strength properties of Fly ash based MPC matrices. Five different AI approaches were nominated to justify the forecasting performances. DNN2 and OGPR showed well performance to training and validation phases. Fly ash was detected as prime dominating input variable for MPC with FA matrices.