Perspective on the Application of Machine Learning Algorithms for Flow Parameter Estimation in Recycled Concrete Aggregate

The constantly expanding civilization and construction industry pose new challenges for a sustainable development economy. Aiming to protect the environment is often associated with waste management, thereby reducing the number of landfills. The management of recycled concrete aggregate (RCA) from building demolition and its reuse in construction perfectly fits into this trend. The characteristics of post-industrial and recycled materials are not homogeneous as is usually the case with natural materials. This leads to a search for solutions to determine the parameters in the simplest possible manner and with as few resources as possible, while eliminating estimation risks. This task can be solved using machine learning, whose algorithms are increasingly used and developed in many areas of life and industry. The research in this study is aimed at comparing the effectiveness of k-Nearest Neighbors (k-NN) and Artificial Neural Network (ANN) algorithms in determining the permeability coefficient to a linear regression model. This parameter has an important role from the perspective of the application of RCA in civil engineering, particularly in earth construction. Two different RCA materials with different origins and properties were used in the study. The filtration test for each sample was pre-prepared using different compaction energies of 0.17 and 0.59 J/cm(3) and for loosely packed samples. Differences in the structures of the test results are presented for both materials. The lowest prediction errors were obtained for the k-NN model. This algorithm obtained for the training sample a coefficient of determination (R(2)) equal to 0.947 and for the test sample an R(2) equal to 0.980. In the case of ANN, the coefficient of determination was in the range of 0.877–0.936. An important part of the study was the interpretation with SHAP of the obtained models, allowing insight into which parameters influenced the predictions. That is significant and novel, considering the heterogeneity of the materials studied, and provides a rationale for further research in this area.