Estimation of the coefficient of permeability as an example of the application of the Random Forest algorithm in Civil Engineering

Abstract

A new world record for crude steel production was recorded in 2021, which increased by 3.8% over 2020. This also affected the amount of slag produced with this production. Total waste from industrial and construction production throughout the European Union accounts for as much as 48%. Therefore, waste management should provide for the recovery of as many resources as possible. European Union strategies in line with the circular economy objectives focus on ensuring policy coherence in the areas of climate, energy efficiency, construction and demolition waste management and resource efficiency. Slags are a material of interest to researchers in terms of their use in construction. Slags, on the one hand, are materials that are becoming better understood on the other hand, we are making sure of the heterogeneity of these materials. The characteristics of physical properties of slags are influenced by many factors, including the furnace split in which they are produced. This prompts the search for tools to help determine the parameters of slags based on already available data. The study aimed to verify the hypothesis that it is possible to determine the parameter of the filtration coefficient, relevant to applications in earth structures using the machine learning algorithm – Random Forest. In the study, two types of material were analysed: blast furnace slag and furnace slag. The results of the analysis yielded a high coefficient of determination (R2) – 0.84–0.92. This leads us to believe that the algorithm may prove useful in determining filtration parameters in slags.

Keywords

• circular construction
• slag
• machine learning
• random forest
• coefficient of permeability