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.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data can be made available on request by interested parties.
Authors’ contributions
N.S: Conceptualisation, Data curation, Analysis, Writing – original draft. P.J: Conceptualisation, Machine learning modelling, Writing – original draft. D.N.S: Conceptualisation, Analysis, Writing – original draft, Writing – review & editing.
Consent to participate
This article does not contain any studies with human participants or animals performed by any of the authors.