Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

• Published in: Measurement : journal of the International Measurement Confederation Vol. 225; p. 114006
• Main Authors: Sathiparan, Navaratnarajah, Jeyananthan, Pratheeba, Subramaniam, Daniel Niruban
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.02.2024
• Subjects: Electrical resistivity; Machine learning; Pervious concrete; Surface response regression; Ultrasonic pulse velocity; Surface response regression; Ultrasonic pulse velocity; Pervious concrete; Electrical resistivity; Machine learning
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2023.114006

•Surface response regression and machine learning techniques were used to predict pervious concrete characteristics.•moderate association between UPV, ER, porosity, and compressive strength of pervious concrete.•Quadratic model for porosity prediction and a cubic model for compressive strength prediction was recommended.•the BDT model showed excellent performance in predicting porosity and compressive strength. It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.