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A platform for research: civil engineering, architecture and urbanism
Soft computing techniques to predict the electrical resistivity of pervious concrete
https://orcid.org/https://orcid.org/0000-0002-1023-1617
https://orcid.org/https://orcid.org/0000-0003-0476-3964
https://orcid.org/https://orcid.org/0000-0001-8570-0580
The objective of the present study was to assess how electrical resistivity (ER) of pervious concrete changes with three parameters: aggregate size, aggregate/cement (A/C) ratio and compaction energy. The pervious concrete cubes were cast using three sizes of aggregates, five A/C ratios (3.5, 4.0, 4.5 and 5.0) and five levels of compaction energy (0, 15, 30, 45 and 60 blows by protector hammer) to evaluate the effect of these parameters on ER. The aggregate sizes were 5–12, 12–18 and 18–25 mm. The study produced 225 pervious concrete cubes with 15 different mix designs, and ER was measured. The study analyzed the test data and developed a prediction model using machine-learning (ML) techniques to establish the associations between the three design parameters and the ER. Out of six machine-learning models examined, the random forest regression model and the K nearest neighbor model performed the best in predicting the ER.
Soft computing techniques to predict the electrical resistivity of pervious concrete
https://orcid.org/https://orcid.org/0000-0002-1023-1617
https://orcid.org/https://orcid.org/0000-0003-0476-3964
https://orcid.org/https://orcid.org/0000-0001-8570-0580
The objective of the present study was to assess how electrical resistivity (ER) of pervious concrete changes with three parameters: aggregate size, aggregate/cement (A/C) ratio and compaction energy. The pervious concrete cubes were cast using three sizes of aggregates, five A/C ratios (3.5, 4.0, 4.5 and 5.0) and five levels of compaction energy (0, 15, 30, 45 and 60 blows by protector hammer) to evaluate the effect of these parameters on ER. The aggregate sizes were 5–12, 12–18 and 18–25 mm. The study produced 225 pervious concrete cubes with 15 different mix designs, and ER was measured. The study analyzed the test data and developed a prediction model using machine-learning (ML) techniques to establish the associations between the three design parameters and the ER. Out of six machine-learning models examined, the random forest regression model and the K nearest neighbor model performed the best in predicting the ER.
Soft computing techniques to predict the electrical resistivity of pervious concrete
https://orcid.org/https://orcid.org/0000-0002-1023-1617
https://orcid.org/https://orcid.org/0000-0003-0476-3964
https://orcid.org/https://orcid.org/0000-0001-8570-0580
The objective of the present study was to assess how electrical resistivity (ER) of pervious concrete changes with three parameters: aggregate size, aggregate/cement (A/C) ratio and compaction energy. The pervious concrete cubes were cast using three sizes of aggregates, five A/C ratios (3.5, 4.0, 4.5 and 5.0) and five levels of compaction energy (0, 15, 30, 45 and 60 blows by protector hammer) to evaluate the effect of these parameters on ER. The aggregate sizes were 5–12, 12–18 and 18–25 mm. The study produced 225 pervious concrete cubes with 15 different mix designs, and ER was measured. The study analyzed the test data and developed a prediction model using machine-learning (ML) techniques to establish the associations between the three design parameters and the ER. Out of six machine-learning models examined, the random forest regression model and the K nearest neighbor model performed the best in predicting the ER.
Soft computing techniques to predict the electrical resistivity of pervious concrete
Asian J Civ Eng
Subramaniam, Daniel Niruban (author) / Jeyananthan, Pratheeba (author) / Sathiparan, Navaratnarajah (author)
Asian Journal of Civil Engineering ; 25 ; 711-722
2024-01-01
12 pages
Article (Journal)
Electronic Resource
English
Soft computing techniques to predict the electrical resistivity of pervious concrete
| Springer Verlag | 2024
Pervious concrete for pervious pavement and preparation method of pervious concrete
| European Patent Office | 2021