Concrete compressive strength using artificial neural networks

By Panagiotis G. Asteris, Vaseilios G. Mokos. Neural computing & applications (Print), 2019

410 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/5844c3b9c4db0655259b9693b771becf71aef55f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. Hoang Nguyen
2. Binh Thai Pham
3. Jin Duan
4. Panagiotis G. Asteris
5. Xuan-Nam Bui
6. Hossein Moayedi
7. Hai-Bang Ly
8. Van Quan Tran

In Mechanical Engineering

1. Indra Prakash
2. Binh Thai Pham
3. Hoang Nguyen
4. Panagiotis G. Asteris
5. Khabat Khosravi
6. D. Tien Bui

In Mechanics of Materials

1. Xuan-Nam Bui
2. Hossein Moayedi
3. Jin Duan
4. Panagiotis G. Asteris
5. Hoang Nguyen

Citations in the corpus

• A novel artificial intelligence technique to predict compressive strength of recycled aggregate concrete using ICA-XGBoost model (2020, background): “However, this trend has led to a series of negative effects [1, 2].”
• Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil (2021, methodology): “ANN has been known as a popular and powerful machine learning technique (computational model) [74, 75], based on structures and functions of biological neural networks: the nervous system of the human brain [20, 76–78].”