A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression
In this study, supervised learning and neural networks were applied to predict the compressive strength of concrete mixes with GGBFS and fly ash. Three models: Multilayer perceptron network (MLP), random forest regression (RFR) and k-nearest neighbor (KNN) regression methods were employed using Python to estimate the compressive strength of concrete mixes. Inputs included cement content, water content, coarse aggregate, fine aggregate, superplasticizer and maturity age, and output was concrete compressive strength. The three methods were compared according to their accuracy and stability to predict compressive strength. Results showed that RFR and MLP regression produced close results and both had better performance and produced less amount of error compared to KNN. Stability results showed that RFR was the least influenced by the data splitting process and it was addressed as the most stable model.
Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression
In this study, supervised learning and neural networks were applied to predict the compressive strength of concrete mixes with GGBFS and fly ash. Three models: Multilayer perceptron network (MLP), random forest regression (RFR) and k-nearest neighbor (KNN) regression methods were employed using Python to estimate the compressive strength of concrete mixes. Inputs included cement content, water content, coarse aggregate, fine aggregate, superplasticizer and maturity age, and output was concrete compressive strength. The three methods were compared according to their accuracy and stability to predict compressive strength. Results showed that RFR and MLP regression produced close results and both had better performance and produced less amount of error compared to KNN. Stability results showed that RFR was the least influenced by the data splitting process and it was addressed as the most stable model.
Prediction of concrete compressive strength with GGBFS and fly ash using multilayer perceptron algorithm, random forest regression and k-nearest neighbor regression
Asian J Civ Eng
Ghunimat, Dalin (author) / Alzoubi, Ahmed Essa (author) / Alzboon, Abdelrahman (author) / Hanandeh, Shadi (author)
Asian Journal of Civil Engineering ; 24 ; 169-177
2023-01-01
9 pages
Article (Journal)
Electronic Resource
English
Prediction Compressive Strength of Concrete Containing GGBFS using Random Forest Model
| DOAJ | 2021
Composite Nearest Neighbor Nonparametric Regression to Improve Traffic Prediction
| British Library Online Contents | 2007
Effect of GGBFS on Compressive Strength and Durability of Concrete
| British Library Conference Proceedings | 2018
Effect of GGBFS on time dependent compressive strength of concrete
| British Library Online Contents | 2010