A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
On the Training Algorithms for Artificial Neural Network in Predicting Compressive Strength of Recycled Aggregate Concrete
Due to the different difficulties of compressive strength of recycled aggregate concrete (RAC) prediction, this investigation develops a prediction architecture based on machine learning algorithms. The artificial neural networks algorithm and artificial neural networks with a cascade-correlation algorithm using one hidden layer or two hidden layers are proposed to predict the compressive strength of the recycled aggregate concrete. In this research, 112 datasets of recycled aggregate concrete are gathered from the literature with 6 inputs. Moreover, this investigation has predicted the age effect of recycled aggregate age on the compressive strength of recycled aggregate concrete. The reliability of ANN architecture is evaluated by some criteria such as correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). The best ANN architecture could be considered as a new tool for an estimation of the RAC compressive strength.
On the Training Algorithms for Artificial Neural Network in Predicting Compressive Strength of Recycled Aggregate Concrete
Due to the different difficulties of compressive strength of recycled aggregate concrete (RAC) prediction, this investigation develops a prediction architecture based on machine learning algorithms. The artificial neural networks algorithm and artificial neural networks with a cascade-correlation algorithm using one hidden layer or two hidden layers are proposed to predict the compressive strength of the recycled aggregate concrete. In this research, 112 datasets of recycled aggregate concrete are gathered from the literature with 6 inputs. Moreover, this investigation has predicted the age effect of recycled aggregate age on the compressive strength of recycled aggregate concrete. The reliability of ANN architecture is evaluated by some criteria such as correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE). The best ANN architecture could be considered as a new tool for an estimation of the RAC compressive strength.
On the Training Algorithms for Artificial Neural Network in Predicting Compressive Strength of Recycled Aggregate Concrete
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) / Van Thi Mai, Hai (author) / Van Tran, Quan (author) / Nguyen, Thuy-Anh (author)
CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure ; Chapter: 189 ; 1867-1874
2021-10-28
8 pages
Article/Chapter (Book)
Electronic Resource
English
Recycled aggregate concrete (RAC) , Compressive strength , Artificial neural networks (ANN) , Cascade Engineering , Geoengineering, Foundations, Hydraulics , Sustainable Architecture/Green Buildings , Sustainable Development , Structural Materials , Cyber-physical systems, IoT , Professional Computing
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
| Online Contents | 2013
Prediction of compressive strength of recycled aggregate concrete using artificial neural networks
| British Library Online Contents | 2013