Novel Approach to Strength Modeling of Concrete under Triaxial Compression

By Amir H. Gandomi, S. Babanajad, Amir Hossein Alavi, Y. Farnam. 2012

145 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/10c0258fc1a320494c4ebb8ed916b7d2c01b480a

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. Muhammad Faisal Javed
2. Fahid Aslam
3. Rayed Alyousef
4. Furqan Farooq
5. David A. Roke
6. Muhammad Khizar Khan
7. Shazim Ali memon
8. Amir H. Gandomi

In Civil and Structural Engineering

1. Muhammad Faisal Javed
2. Fahid Aslam
3. Furqan Farooq
4. Rayed Alyousef
5. David A. Roke
6. Shazim Ali memon
7. Muhammad Khizar Khan
8. Amir H. Gandomi

Citations in the corpus

• Assessment of artificial neural network and genetic programming as predictive tools (2015, methodology): “GP algorithm parameter selection will affect the model generalization capability of GP; therefore, in this study, these parameters were selected based on previously suggested values in the literature [9,24].”
• Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “Genetic programming (GP), one of the strong soft computing techniques, is worthy as it develops a model without considering the previously established relationships [48, 49].”