New formulations for mechanical properties of recycled aggregate concrete using gene expression programming

By Aliakbar Gholampour, Amir H. Gandomi, Togay Ozbakkaloglu. 2017

342 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/a01df808add3c2147f39bafef46454dfbe011944

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

1. Rayed Alyousef
2. Hisham Alabduljabbar
3. Guichen Li
4. Muhammad Faisal Javed
5. Furqan Farooq
6. Deyu Qian
7. Yuantian Sun
8. Muhammad Khizar Khan

In Civil and Structural Engineering

1. Yuantian Sun
2. Guichen Li
3. Deyu Qian
4. Junfei Zhang
5. Rayed Alyousef
6. Hisham Alabduljabbar
7. Muhammad Faisal Javed
8. Muhammad Izhar Shah

Citations in the corpus

• 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].”
• Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020, methodology): “Generation and disposal of large amounts of waste poses serious environmental issues [1,2].”