This paper aims to develop a novel prediction tool based on the machine learning framework to evaluate the compressive strength and effective porosity of pervious concrete material from its compositions. To address this difficult task, 14 data sources were collected from the literature to build a dataset of 164 samples. The dataset included seven mixture design features (e.g., aggregate-to-cement ratio, water-to-cement ratio, minimum coarse aggregate size, the presence of sand or silica fume, effective porosity, and the compressive strength). This dataset was trained and tested by the most relevant machine learning methods: the extreme gradient boosting method (XGB), the random forest regression method, and the support vector machine method. The Particle Swarm Optimization method was applied to tune the models’ hyperparameters. It was observed that the extreme gradient boosting method significantly outperformed the accuracy of the other methods. Relatively high R-squared values of 0.92 and 0.88 were obtained for the compressive strength and effective porosity predictions. Furthermore, to account for the role of compaction, the original database was refined to obtain a 36 samples subset that considered compaction energy. Based on our assessment of this subset, results yielded superior R-squared values up to 0.99 for compressive strength, and 0.97 for effective porosity, revealing the effectiveness and accuracy of this research.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods


    Additional title:

    KSCE J Civ Eng


    Contributors:

    Published in:

    Publication date :

    2022-11-01


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Investigation of the Effects of Compaction on Compressive Strength and Porosity Characteristics of Pervious Concrete

    Anburuvel, Arulanantham / Niruban Subramaniam, Daniel | Transportation Research Record | 2022


    Clogging Resistance of High Strength Pervious Concrete

    Lee, Ming-Gin / Wang, Yung-Chih / Wang, Wei-Chien et al. | TIBKAT | 2022


    Pervious Concrete Pavement

    Henderson, Vimy / Tighe, Susan L. / Norris, Jodi | Transportation Research Record | 2009


    Optimizing Sustainable Construction Materials with Machine Learning Algorithms: Predicting Compressive Strength of Concrete Composites

    Mohammad, Toaha / Ansari, Saad Shamim / Ibrahim, Syed Muhammad et al. | Springer Verlag | 2024


    Paving with pervious concrete

    Garber, George | TIBKAT | 2010