Concrete mechanical properties could be improved through adding different materials at the mixing stage. Quarry dust (QD) is the waste produced by manufactured sand machines and comprise approximately 30–40% of the total quantity of QD generated. When it dries, it transforms into a fine dust that poses a tremendous hazard to the environment by contaminating the soil and water and seriously endangering human health. QD utilization in concrete is one of the best options. Though a lot of scholars focus on imitation of QD in concrete, knowledge is scattered, and a detailed review is required. This review collects the information regarding QD-based concrete, including fresh properties, strength, durability, and microstructure analysis. The results indicate that QD is suitable for concrete to a certain extent, but higher percentages adversely affect properties of concrete due to absence of fluidity. The review also indicates that up to 40–50% substitution of QD as a fine aggregate can be utilized in concrete with no harmful effects on strength and durability. Furthermore, although QD possesses cementitious properties and can be used as cement substitute to some extent, less research has explored this area.

The slope-stability analysis is one of the most important parameters for ensuring a safe design of road embankments. Currently, various traditional approaches to computing this variable can be seen in the literature. Among them, the finite element method is considered an accurate way to define the safety factor of road embankments. Previous research has investigated the capability of artificial neural networks for rapid safety-factor estimation to overcome the long process of modeling and calculations required in the aforementioned approach. However, most of these studies have focused on a single type of neural network and did not investigate the capabilities of other approaches. Therefore, this study is intended to evaluate the performance of various artificial neural network techniques in predicting the safety factor of road embankments. Within this context, the feed-forward back-propagation, cascade forward neural networks, and general regression neural network results will be compared and benchmarked against various methods used to predict this parameter. Moreover, it is intended to report the influence of neural network architecture on the accuracy of the estimation. Generally, the study results have shown that an artificial neural network provides a rapid and accurate method for calculating road embankments' safety factors. Besides, the best neural network model achieved a coefficient of determination of about 0.91 and a root mean square error of 0.236, which proves the efficiency of this technique. Moreover, the reliability assessment by comparing the neural network models against the traditional methods has shown that they provide better agreement with the finite element technique.

Indeed, natural processes of discarding rubber waste have many disadvantages for the environment. As a result, multiple researchers suggested addressing this problem by recycling rubber as an aggregate in concrete mixtures. Previously, numerous studies have been undertaken experimentally to investigate the properties of rubberized concrete. Furthermore, investigations were carried out to develop estimating techniques to precisely specify the generated concrete's characteristics, making its use in real-life applications easier. However, there is still a gap in the conducted studies on the performance of the k-nearest neighbor algorithm. Hence, this research explores the accuracy of using the k-nearest neighbor's algorithm in predicting the compressive and tensile strength and the modulus of elasticity of rubberized concrete. It will be done by developing an optimized machine learning model using the aforementioned method and then benchmarking its results to the outcomes of multiple linear regression and artificial neural networks. The study's findings have shown that the k-nearest neighbor's algorithm provides significantly higher accuracy than other methods. This kind of study needs to be discussed in the literature so that people can better deal with rubber waste in concrete. Doi: 10.28991/CEJ-2022-08-04-06 Full Text: PDF

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