Fahid Aslam
Civil and Structural Engineering · 2020–2022
A researcher in the Science corpus: 8 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Civil and Structural Engineering — 402 citations into it
- Building and Construction — 197 citations into it
- Artificial Intelligence — 11 citations into it
- Statistics and Probability — 8 citations into it
Whom Fahid Aslam trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Papers from Fahid Aslam's perspective
Ordered by how strongly the researchers Fahid Aslam trusts endorse each paper. Same corpus, a different order for every researcher.
- Gene Expression Programming: A New Adaptive Algorithm for Solving Problems (Complex Systems)
- A Representation for the Adaptive Generation of Simple Sequential Programs
- THE EFFECT OF COMPOSITION AND TEMPERATURE ON THE PROPERTIES OF FLY ASH- AND KAOLINITE -BASED GEOPOLYMERS
- Random Forests
- New formulations for mechanical properties of recycled aggregate concrete using gene expression programming
- Modelling uniaxial compressive strength of lightweight self-compacting concrete using random forest regression (Construction and Building Materials)
- Use of selected waste materials in concrete mixes. (Waste Management)
- Modeling of strength of high-performance concrete using artificial neural networks
- Influence of aggregate content on the behavior of fly ash based geopolymer concrete
- Use of waste glass as sand in mortar: Part I – Fresh, mechanical and durability properties
Their papers
- Machine learning modeling integrating experimental analysis for predicting the properties of sugarcane bagasse ash concrete (2022) — 71 citations
- 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
- A scientometric review of waste material utilization in concrete for sustainable construction (2021) — 156 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