Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

By Hadi Mashhadban, S. S. Kutanaei, M. A. Sayarinejad. 2016

190 citations recorded by the source corpus.

Subfields: Building and Construction, Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/32011afe45306927903ec1f13fc5ba0860418769

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. Guichen Li
3. Muhammad Faisal Javed
4. Furqan Farooq
5. Yuantian Sun
6. Muhammad Khizar Khan
7. Shazim Ali memon
8. Junfei Zhang

In Civil and Structural Engineering

1. Yuantian Sun
2. Guichen Li
3. Junfei Zhang
4. Rayed Alyousef
5. Muhammad Faisal Javed
6. Furqan Farooq
7. Muhammad Khizar Khan
8. Shazim Ali memon

Citations in the corpus

• Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021, methodology): “Recently, ANN was used to accurately predict the mechanical properties of rice husk ash concrete [33] and workability of selfcompacting concrete [42].”
• Optimized neural network using beetle antennae search for predicting the unconfined compressive strength of jet grouting coalcretes (2019, methodology): “Artificial neural network (ANN) was employed for modeling the nonrelationships between inputs (influencing variables) and outputs (eg, UCS, Young modulus) in building and construction materials.(38,39) In a neural network, the”