Applications of Nearest Neighbor Search Algorithm Toward Efficient Rubber-Based Solid Waste Management in Concrete

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: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۲-۰۸-۰۴-۰۶ Full Text: PDF

Keywords:

Rubberized Concrete , Recycled Aggregate , Concrete Properties , Nearest Neighbour , Machine Learning.