A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods
https://orcid.org/http://orcid.org/0000-0001-6816-6216
https://orcid.org/http://orcid.org/0000-0003-1995-2872
https://orcid.org/http://orcid.org/0000-0001-9709-5699
https://orcid.org/http://orcid.org/0000-0001-8968-3394
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.
Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods
https://orcid.org/http://orcid.org/0000-0001-6816-6216
https://orcid.org/http://orcid.org/0000-0003-1995-2872
https://orcid.org/http://orcid.org/0000-0001-9709-5699
https://orcid.org/http://orcid.org/0000-0001-8968-3394
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.
Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods
https://orcid.org/http://orcid.org/0000-0001-6816-6216
https://orcid.org/http://orcid.org/0000-0003-1995-2872
https://orcid.org/http://orcid.org/0000-0001-9709-5699
https://orcid.org/http://orcid.org/0000-0001-8968-3394
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.
Predicting the Compressive Strength and the Effective Porosity of Pervious Concrete Using Machine Learning Methods
KSCE J Civ Eng
Le, Ba-Anh (author) / Vu, Viet-Hung (author) / Seo, Soo-Yeon (author) / Tran, Bao-Viet (author) / Nguyen-Sy, Tuan (author) / Le, Minh-Cuong (author) / Vu, Thai-Son (author)
KSCE Journal of Civil Engineering ; 26 ; 4664-4679
2022-11-01
16 pages
Article (Journal)
Electronic Resource
English
High-Strength High-Porosity Pervious Concrete Pavement
| British Library Conference Proceedings | 2013
High-Strength High-Porosity Pervious Concrete Pavement
| Tema Archive | 2013
| Taylor & Francis Verlag | 2023
Prediction of compressive strength of fly ash blended pervious concrete: a machine learning approach
| Taylor & Francis Verlag | 2023