Work

Beneficiating ceramic waste in sustainable concrete pavement: multi-objective optimization using artificial neural networks

Year: 2025
Type: article
Abstract: Mitigating natural resource depletion and ensuring that byproducts and wastes are reused effectively has become paramount for fostering sustainable development. Accordingly, this study explores using low-cost, readily available, and ecologically benign ceramic wastes to replace cement and fine/coarse aggregates in developing new types of mortar/concrete as pavement. The first phase of this work examines the durability, mechanical strength, and long-term performance of mortar/concrete that uses beneficiated ceramic waste as a secondary cementitious material and crushed ceramic waste as fine/coarse aggregates. To characterize the morphology, structure, and thermal properties of the proposed mixtures, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR) were deployed. In the second phase, artificial neural networks (ANN) coupled with a Bayesian regularization training algorithm were tailored to estimate waste ceramic-based concrete’s compressive strength and CO 2 emissions. Sensitivity and regression analyses were developed to examine each parameter’s influence on the compressive strength and CO 2 emissions and facilitate the design of beneficiated ceramic-based concrete. Subsequently, a multi-objective optimization study was developed to identify the values of input predictor variables that maximize the predicted compressive strength and minimize CO 2 emissions. The findings show that using ceramic waste as a binder and source of fine/coarse aggregates vastly improved the mechanical strength performance of the mortar/concrete and reduced drying shrinkage and porosity. Moreover, given the favorable interactions between fine aggregates and ceramic waste, porosity decreased and microstructure enhanced.
Authors: Iman Faridmehr, Ghasan Fahim Huseien
Language: English
Open Access status: diamond
OpenAlex ID: W4415659717
FWCI: 1.308
Cites: 50
Cited by: 3
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