In this paper, the effect of GO/Al₂O₃ and Al₂O₃ synthesized nanoparticles on the durability of concrete is studied. To this end, after the synthesis of nanoparticles and confirmation of nanoparticles fabrication by SEM and FT‐IR spectra, three concrete samples for each experiment related to each mix design were prepared and subjected to freeze‐thaw cycles, high temperature, and acidic environment. The results show that the samples containing GO/Al₂O₃ nanoparticles had the least weight loss in freeze‐thaw cycles as well as better resistance against acidic environment and the lowest apparent changes at high temperature compared to the samples containing nano‐Al₂O₃ and the samples without nanoparticles. The replacement of 2 wt.% of cement with GO/Al₂O₃ nanoparticles results in the highest increase in concrete durability. The presence of nanoparticles in the concrete microstructure and the validation of the results are investigated by FT‐IR, SEM, and EDX spectra.

The effect of nano KCC-1/Ag and nano SiO2 in concrete have been investigated in this study. Nano KCC-1/Ag and nano SiO2 are synthesized and their characterization was investigated by FTIR, SEM, and TEM analysis. After that, these materials use as cement replacement in 1, 2, and 3 percent amounts. SEM images illustrate the denser structure of KCC-1/Ag nanoparticles. Furthermore, in the FTIR spectra 3640 cm-1 is related to C-S-H, which is sharper and more severe in samples with nano KCC-1/Ag and nano SiO2. The results revealed that nano SiO2 and nano KCC-1/Ag both improved the microstructure of cement paste and increased the concrete compressive and splitting tensile strength. For comparison the performance of nano KCC-1/Ag and nano SiO2, the results indicated that nano KCC-1/Ag improved the microstructure of concrete better than nano SiO2. Hence, it has a better performance in enhancing the strength of concrete. The study showed that the optimal percentage of using nano KCC-1/Ag was 2%.

In order to build high‐quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning‐enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion‐learning‐based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an optimization method for finding the kernel radial basis function (RBF) parameters based on improving the innovative gunner algorithm (AIG). As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion‐learning‐based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%.

The utilization of nanoparticles in concrete has been propelled by their advantageous attributes, such as their fine particle size and remarkable reactivity. To enhance these properties, various nanoparticles can be integrated into the lightweight concrete matrix. We introduce a novel technique to produce TiO₂ structures resembling dandelions, featuring interfaces between anatase and TiO₂ phases as well as a distinct outer layer. This approach utilizes a deep eutectic solvent-tuning method that is both user- and environmentally friendly. The formation of this remarkable external covering is attributed to the hierarchical arrangement of two-dimensional ultrathin nanosheets with mesopores in a three-dimensional configuration. The primary focus of this study is the utilization of DFNT, a nanoparticle possessing a three-dimensional structure, within the matrix of lightweight concrete. To conduct this study, concrete cases with congestion of 1000 kg/m³ were fabricated and subjected to testing. We sought to evaluate the effect of different weight ratio proportions of DFNT on the enduring characteristics of lightweight concrete, such as the contraction caused by drying, the degree of openness, the capacity to absorb water, and the speed at which ultrasonic waves travel. The inclusion of DFNT induces a transformation in the microstructural composition of lightweight concrete, shifting it from a loose needle-like structure to a more compact and cohesive microstructure characteristic of cementitious composites. Furthermore, DFNT enhances the lightweight concrete matrix by occupying the empty spaces, tiny fractures, and gaps present within it.