A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Comparison of artificial intelligence methods for predicting compressive strength of concrete
Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.
Comparison of artificial intelligence methods for predicting compressive strength of concrete
Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.
Comparison of artificial intelligence methods for predicting compressive strength of concrete
Mehmet Timur Cihan (author)
2021
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Predicting the Compressive Strength of Rubberized Concrete Using Artificial Intelligence Methods
| DOAJ | 2021
| British Library Online Contents | 2013
| Springer Verlag | 2024
Determination of Compressive Strength of Concrete via Artificial Intelligence
| BASE | 2020