What is he best known for?
The fields of study he is best known for:
- Artificial neural network
- Statistics
- Machine learning
Danial Jahed Armaghani mostly deals with Artificial neural network, Mean squared error, Coefficient of determination, Particle swarm optimization and Structural engineering.
His Artificial neural network study incorporates themes from Intelligent decision support system, Algorithm, Genetic algorithm and Sensitivity.
Danial Jahed Armaghani has included themes like Empirical modelling, Data mining, Linear regression, Rock mass rating and Performance prediction in his Mean squared error study.
His biological study spans a wide range of topics, including Ranking, Compressive strength, Geotechnical engineering and Adaptive neuro fuzzy inference system.
The Schmidt hammer research Danial Jahed Armaghani does as part of his general Compressive strength study is frequently linked to other disciplines of science, such as Maxima and minima, therefore creating a link between diverse domains of science.
Danial Jahed Armaghani has researched Particle swarm optimization in several fields, including Bearing capacity and Artificial intelligence.
His most cited work include:
- Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks (156 citations)
- Prediction of seismic slope stability through combination of particle swarm optimization and neural network (150 citations)
- Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition (149 citations)
What are the main themes of his work throughout his whole career to date?
Artificial neural network, Mean squared error, Coefficient of determination, Geotechnical engineering and Structural engineering are his primary areas of study.
His Artificial neural network research includes themes of Compressive strength, Algorithm, Particle swarm optimization and Data mining.
His research in Mean squared error tackles topics such as Rock mass rating which are related to areas like Core recovery parameters.
His Coefficient of determination research integrates issues from Linear regression, Genetic algorithm, Artificial intelligence, Performance prediction and Schmidt hammer.
His biological study spans a wide range of topics, including Finite element method and Deformation.
His Structural engineering study incorporates themes from Cohesion and Soft computing.
He most often published in these fields:
- Artificial neural network (44.29%)
- Mean squared error (36.19%)
- Coefficient of determination (35.71%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial neural network (44.29%)
- Mean squared error (36.19%)
- Data mining (14.29%)
In recent papers he was focusing on the following fields of study:
His primary areas of investigation include Artificial neural network, Mean squared error, Data mining, Support vector machine and Compressive strength.
His research integrates issues of Particle swarm optimization, Firefly algorithm, Genetic algorithm, Structural engineering and Adaptive neuro fuzzy inference system in his study of Artificial neural network.
His work on Bearing capacity is typically connected to Test data as part of general Structural engineering study, connecting several disciplines of science.
His Mean squared error research is multidisciplinary, relying on both Algorithm, Coefficient of determination and Rock mass rating.
His Coefficient of determination study combines topics in areas such as Ranking, Training phase and Tunnel boring machine.
His Compressive strength study combines topics from a wide range of disciplines, such as Ultimate tensile strength, Fly ash and Mortar.
Between 2020 and 2021, his most popular works were:
- Developing GEP tree-based, neuro-swarm, and whale optimization models for evaluation of bearing capacity of concrete-filled steel tube columns (66 citations)
- Deep neural network and whale optimization algorithm to assess flyrock induced by blasting (44 citations)
- A new development of ANFIS–GMDH optimized by PSO to predict pile bearing capacity based on experimental datasets (33 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Machine learning
- Geotechnical engineering
Artificial neural network, Mean squared error, Particle swarm optimization, Compressive strength and Algorithm are his primary areas of study.
The Artificial neural network study combines topics in areas such as Optimization algorithm, Structural engineering, Adaptive neuro fuzzy inference system and Data mining.
The various areas that Danial Jahed Armaghani examines in his Structural engineering study include Soft computing and Gene expression programming.
His study explores the link between Mean squared error and topics such as Support vector machine that cross with problems in Coefficient of determination.
His Particle swarm optimization research is multidisciplinary, incorporating elements of Bearing capacity and Deformation.
The study incorporates disciplines such as Empirical modelling and Silica fume in addition to Algorithm.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
