What is he best known for?
The fields of study he is best known for:
- Composite material
- Structural engineering
- Geotechnical engineering
Mahdi Shariati mainly investigates Structural engineering, Shear, Cable gland, Composite beams and Artificial neural network.
His Structural engineering research includes themes of Ductility, Compressive strength and Slag.
In Compressive strength, Mahdi Shariati works on issues like Geotechnical engineering, which are connected to Ultimate tensile strength, Reinforcement and Compression.
The subject of his Shear research is within the realm of Composite material.
In his research on the topic of Composite beams, Computational intelligence and I-beam is strongly related with Shear capacity.
In his study, Extreme learning machine and Soft computing is strongly linked to Genetic programming, which falls under the umbrella field of Artificial neural network.
His most cited work include:
- Potential of adaptive neuro fuzzy inference system for evaluating the factors affecting steel-concrete composite beam's shear strength (87 citations)
- Prediction of shear capacity of channel shear connectors using the ANFIS model (86 citations)
- RETRACTED ARTICLE: Analysis of influential factors forpredicting the shear strength of a V-shaped angle shear connector in composite beamsusing an adaptive neuro-fuzzy technique (74 citations)
What are the main themes of his work throughout his whole career to date?
Mahdi Shariati spends much of his time researching Structural engineering, Composite material, Cable gland, Shear and Finite element method.
His study in Structural engineering is interdisciplinary in nature, drawing from both Ductility and Composite number.
Many of his research projects under Composite material are closely connected to Monotonic function with Monotonic function, tying the diverse disciplines of science together.
His work on Shear capacity as part of general Shear research is frequently linked to Parametric statistics, bridging the gap between disciplines.
He has included themes like Dynamic loading, Plasticity and Welding in his Finite element method study.
His Shear research focuses on Shear strength and how it relates to Artificial neural network, Soft computing, Genetic programming and Extreme learning machine.
He most often published in these fields:
- Structural engineering (58.42%)
- Composite material (34.65%)
- Cable gland (28.71%)
What were the highlights of his more recent work (between 2019-2021)?
- Composite material (34.65%)
- Compressive strength (18.81%)
- Structural engineering (58.42%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Composite material, Compressive strength, Structural engineering, Artificial neural network and Silica fume.
His work on Flexural strength and Composite beams as part of general Composite material study is frequently connected to Push out test and Monotonic function, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His research in Compressive strength intersects with topics in Portland cement, Radial basis function, Fly ash, Absorption of water and Cold forming.
Mahdi Shariati studies Structural engineering, focusing on Buckling in particular.
His work on Extreme learning machine is typically connected to Coefficient of determination as part of general Artificial neural network study, connecting several disciplines of science.
His Ductility study incorporates themes from Composite number and Beam.
Between 2019 and 2021, his most popular works were:
- Developed comparative analysis of metaheuristic optimization algorithms for optimal active control of structures (36 citations)
- Prediction of concrete strength in presence of furnace slag and fly ash using Hybrid ANN-GA (Artificial Neural Network-Genetic Algorithm) (35 citations)
- A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques (34 citations)
In his most recent research, the most cited papers focused on:
- Composite material
- Structural engineering
- Geotechnical engineering
His primary areas of study are Artificial neural network, Extreme learning machine, Compressive strength, Mathematical optimization and Cement.
His Artificial neural network study frequently links to adjacent areas such as Shear strength.
His Extreme learning machine research incorporates themes from Metaheuristic, Kernel, Radial basis function, Support vector machine and Shear.
His Compressive strength research integrates issues from Portland cement, Slag, Backpropagation, Fly ash and Structural engineering.
His Cement research includes themes of Flexural strength and Permeability.
His Pozzolan study is related to the wider topic of Composite material.
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