Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach

Abstract

This study presents a prediction model for estimating the compressive strength of pervious concrete through the utilisation of machine learning techniques. The models were trained and tested using 437 datasets sourced from published literature. This work employed a collection of six machine learning algorithms as statistical evaluation tools to determine the optimal and dependable model for forecasting the compressive strength of pervious concrete. Out of all the models considered, the eXtreme Gradient Boosting model had greater performance in predicting the compressive strength. The coefficient of determination value for the train data is 0.99, indicating a strong correlation between the predicted and actual values. The root mean squared error for the train data is 0.86 MPa, representing the average deviation between the predicted and measured values. Similarly, the coefficient of determination value for the test datasets is determined to be 0.95, accompanied by a root mean squared error of 2.53 MPa. The eXtreme Gradient Boosting model's sensitivity analysis findings suggest that the aggregate size is the greatest parameter on forecasting the compressive strength of pervious concrete. This study delivers a systematic assessment of the compressive strength of pervious concrete, contributing to the current knowledge base and practical implementation in this field.