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

Archivos

Mostrando 1 - 1 de 1