Descriptive and predictive analytics of the cement and concrete production process
| dc.accrualmethod | Learning | |
| dc.accrualmethod | Evolutionary | |
| dc.accrualmethod | Algorithm | |
| dc.accrualmethod | Predictions | |
| dc.contributor.affiliation | Universidad EAFIT | spa |
| dc.contributor.author | Madrid Peláez, Juan Pablo | |
| dc.contributor.author | Cardeño Luján, Salomón | |
| dc.date.accessioned | 2023-10-18T16:04:31Z | |
| dc.date.available | 2023-10-18T16:04:31Z | |
| dc.date.issued | 2022-06 | |
| dc.description.abstract | The paper uses both a Machine Learning and a combined Taylor Modeling with Heuristics approach to predict the Compressive Strength, the main characteristic to determine the physicochemical behaviors of concrete and cement. Implementing a data-based approach, can result in a reduction of the variability of the process by improving the overall reliability. The AdaBoost, ANN and SVM algorithms were implemented, where the AdaBoost performed the best in terms of fitness, error, and predictions, which was expected due to its k-folds nature. To counter the black-box nature of Machine Learning, a Taylor Modeling algorithm was implemented to build the mathematical model of the data with the use of an evolutionary algorithm to find the best parameters for the model. | spa |
| dc.identifier.uri | http://hdl.handle.net/10784/33049 | |
| dc.language.iso | eng | spa |
| dc.subject.keyword | Machine | spa |
| dc.title | Descriptive and predictive analytics of the cement and concrete production process | spa |
| dc.type | article | spa |
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