Furqan Farooq

Civil and Structural Engineering · 2020–2021

A researcher in the Science corpus: 6 papers; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Civil and Structural Engineering — 293 citations into it
• Building and Construction — 123 citations into it
• Artificial Intelligence — 8 citations into it

Whom Furqan Farooq trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Amir H. Gandomi
2. P. Sarker
3. D. Hardjito
4. P. Chindaprasirt
5. T. Bakharev
6. A. Fernández-Jiménez
7. B. V. Rangan
8. P. Nath
9. Rafat Siddique
10. Vanchai Sata

Papers from Furqan Farooq's perspective

Ordered by how strongly the researchers Furqan Farooq trusts endorse each paper. Same corpus, a different order for every researcher.

1. THE EFFECT OF COMPOSITION AND TEMPERATURE ON THE PROPERTIES OF FLY ASH- AND KAOLINITE -BASED GEOPOLYMERS
2. Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (Complex Systems)
3. A Representation for the Adaptive Generation of Simple Sequential Programs
4. Random Forests
5. Influence of aggregate content on the behavior of fly ash based geopolymer concrete
6. Use of selected waste materials in concrete mixes. (Waste Management)
7. New formulations for mechanical properties of recycled aggregate concrete using gene expression programming
8. Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression (Construction and Building Materials)
9. Modeling of strength of high-performance concrete using artificial neural networks
10. Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network

Their papers

• Compressive Strength Prediction via Gene Expression Programming (GEP) and Artificial Neural Network (ANN) for Concrete Containing RCA (2021) — 182 citations
• Predicting the compressive strength of concrete with fly ash admixture using machine learning algorithms (2021) — 342 citations
• Compressive Strength of Fly-Ash-Based Geopolymer Concrete by Gene Expression Programming and Random Forest (2021) — 201 citations
• Predictive modeling for sustainable high-performance concrete from industrial wastes: A comparison and optimization of models using ensemble learners (2021) — 384 citations
• Geopolymer concrete as sustainable material: A state of the art review (2021) — 296 citations
• Applications of Gene Expression Programming and Regression Techniques for Estimating Compressive Strength of Bagasse Ash based Concrete (2020) — 150 citations