A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Predicting the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network
Recycled aggregate concrete (RAC), where recycled concrete aggregates replace natural ones, has received increased attention over the past decades, and appears as a promising technology for conserving natural resources, reducing the environmental impact of concrete. However, the complexities in the mixture optimization of RAC, due to the variability of recycled aggregates and lack of accuracy in estimating the compressive strength, require novel and sophisticated techniques. This study aims at developing a machine learning model, based on neural networks, to predict the RAC compressive strength. The RAC database in this investigation is constructed from the available literature, divided into two parts, namely the training and testing parts. Well-known statistical indicators, namely the correlation coefficient (R), root mean square error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed machine learning model. The results indicate that the outputs of the proposed model are in good agreement with the experimental compressive strength values, and may be helpful for engineers to save time, as well as avoiding costly experiments.
Predicting the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network
Recycled aggregate concrete (RAC), where recycled concrete aggregates replace natural ones, has received increased attention over the past decades, and appears as a promising technology for conserving natural resources, reducing the environmental impact of concrete. However, the complexities in the mixture optimization of RAC, due to the variability of recycled aggregates and lack of accuracy in estimating the compressive strength, require novel and sophisticated techniques. This study aims at developing a machine learning model, based on neural networks, to predict the RAC compressive strength. The RAC database in this investigation is constructed from the available literature, divided into two parts, namely the training and testing parts. Well-known statistical indicators, namely the correlation coefficient (R), root mean square error (RMSE), absolute mean error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the performance of the proposed machine learning model. The results indicate that the outputs of the proposed model are in good agreement with the experimental compressive strength values, and may be helpful for engineers to save time, as well as avoiding costly experiments.
Predicting the Compressive Strength of Recycled Aggregate Concrete Based on Artificial Neural Network
Lecture Notes in Civil Engineering
Ha-Minh, Cuong (editor) / Tang, Anh Minh (editor) / Bui, Tinh Quoc (editor) / Vu, Xuan Hong (editor) / Huynh, Dat Vu Khoa (editor) / Ly, Hai-Bang (author)
CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure ; Chapter: 191 ; 1887-1895
2021-10-28
9 pages
Article/Chapter (Book)
Electronic Resource
English
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
| British Library Online Contents | 2013
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
| Online Contents | 2013