Strength prediction model and methods for improving recycled aggregate concrete
By Khaleel H. Younis, K. Pilakoutas. 2013
223 citations recorded by the source corpus.
Subfields: Building and Construction, Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/f271a7865b749b8006c0a7be317bc88a0abe0415
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Building and Construction
- Hoang Nguyen
- Guichen Li
- Jin Duan
- Deyu Qian
- Yuantian Sun
- Panagiotis G. Asteris
- Xuan-Nam Bui
- Hossein Moayedi
In Civil and Structural Engineering
- Yuantian Sun
- Guichen Li
- Deyu Qian
- Junfei Zhang
- Hoang Nguyen
- Jin Duan
- Panagiotis G. Asteris
- Xuan-Nam Bui
Citations in the corpus
- A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, methodology): “MLR and nonlinear regression analysis have also been applied for predicting CS of RAC, in a study of Younis, Pilakoutas [28].”
- Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model (2019, methodology): “-e artificial neural network (ANN) methods were applied to assess the strength of RAC [23–25].”
- The influence of the use of recycled aggregates on the compressive strength of concrete: a review (2015, unknown): “…as artificial neural networks (Dantas, Leite, & Nagahama, 2013; Duan, Kou, & Poon, 2013; Kim, Lee, et al., 2013; Topçu & Sarıdemir, 2008), fuzzy logic (Topçu & Sarıdemir, 2008) and non-linear and multi-linear regressing analysis (Shin & Kim, 2013; Tam, Wang, & Tam, 2008; Younis & Pilakoutas, 2013).”