Predicting the compressive strength of green concretes using Harris hawks optimization-based data-driven methods

2022 ◽  
Vol 318 ◽  
pp. 125944
Author(s):  
Emadaldin Mohammadi Golafshani ◽  
Mehrdad Arashpour ◽  
Ali Behnood
2018 ◽  
Vol 19 ◽  
pp. 51-61 ◽  
Author(s):  
L. Salari-Sharif ◽  
S.W. Godfrey ◽  
M. Tootkaboni ◽  
L. Valdevit

Author(s):  
Funso Falade ◽  
Taim Iqbal

Compressive strength of concrete, renowned as one of the most substantial mechanical properties of concrete and key factors for the quality assurance of concrete. In the present study, two different data-driven models, i.e., Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR) were used to predict the 28 days compressive strength of recycled aggregate concrete (RAC). 16 different input parameters, including both dimensional and non-dimensional parameters, were used for predicting the 28 days compressive strength of concrete. The present study established that estimation of 28 days compressive strength of recycled aggregate concrete was performed better by ANFIS in comparison to MLR. Besides, the performance of data-driven models with and without the non-dimensional parameters is explored. It was observed that the data-driven models show better accuracy when the non-dimensional parameters were used as additional input parameters. Furthermore, the effect of each non-dimensional parameter on the performance of each data-driven model is investigated and 28 days compressive strength of concrete is examined.


2015 ◽  
Vol 5 (2) ◽  
pp. 16-23 ◽  
Author(s):  
Faezehossadat Khademi ◽  
◽  
Mahmoud Akbari ◽  
Sayed Mohammadmehdi Jamal ◽  
◽  
...  

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