Currently, chemical attacks, including acid attacks and sulphate attacks, pose a significant problem for the long-term durability of concrete infrastructures that encounter many types of water, including swamp water, marine water, sewage water, drinkable water, and groundwater. Therefore, the intention of this work is to enhance the durability and resistance of concrete against chemical attack by blending titanium dioxide (TiO 2) as nanoparticles into designed cementitious composites. The purpose of current study is to obtain an appropriate TiO 2 based on the cement’s weight and polyvinyl alcohol (PVA) fiber in composites using multi-objective optimisation. Thirteen mixtures comprising diverse combinations of variables (TiO 2: 1–2%, PVA: 1–2%) were formulated utilising RSM modelling. Seven responses were assessed for these mixtures, including weight loss, compressive strength, expansion, a rapid chloride permeability test (RCPT) and a pH test. Analysis of variance, on the other hand, was utilised to construct and assess eight response models (one linear and six quadratics in nature). The R 2 values for models spanning from 88 to 99%. The multi-objective optimisation generated optimal response values and ideal variable values (1% PVA and 1.5% TiO 2). Experimental verification revealed that the predicted values correlated exceedingly well with the experimental data, with an error rate of less than 5%. The outcomes indicate that a 30% rise in compressive strength was noted when 1.5% TiO 2 nanomaterial was incorporated into ECC. Furthermore, the expansion caused by sulphate attack decreases when TiO 2 used as a nanomaterial increases in composites. Besides, when the concentration of TiO 2 in ECC increased, the pH value, and weight loss caused by acid attack reduced. In addition, the RCPT is recorded reducing when the content of TiO 2 increases but it increases with addition of PVA fiber. It has been shown that including 1.5% TiO 2 and 1% PVA fiber yields the optimal results for the building sector.
See how this article has been cited at scite.ai
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.