Mohammad-Abazari

2D_topo_opt_truss_structure

As an academic project, I developed a 2D topology optimization of a truss structure in python, the algorithm took and performed many examples available in the web. The topology optimization was done under the nested formulation, It means that the process is static. Many examples of this problem have been done, however, this is able to analyze structures of any size, and shows a nice visualization with VTK library and Matplotlib. I hope this work could be useful for you.

2DOrthogonal_Polynomial_Decomposition

OPD implementation with application to a IR sequence of a simulated CFRP sample and two real CFRP and GFRP samples with teflon insertions. Recreates the results presented in Paper "Characterization of defects of pulsed thermography inspections by orthogonal polynomial decomposition"

3dasm_course

Data-Driven Design & Analysis of Structures & Materials (3dasm)

ABAQUS

Some models out of my own research projects are uploaded here. Contact me if anything didn't work.

ABAQUS-version-CDPM2

This repository contains the user-material (VUMAT) of the concrete damage-plasticity model 2 (CDPM2) for use in ABAQUS

Abaqus-VUMAT-elastic_damage

Abaqus VUMAT subroutine for the linear elastic materials with damage based on von mises stress for explicit analysis.

Abaqus-VUMAT-Gurson_GTN

Numerical impementation of the Gurson model with GTN modification.

Abaqus-VUMAT-Johnson-Cook

Abaqus-VUMAT-Johnson-Cook

ABAQUS_PDALAC

Development of the Failure Criteria for Composites using ABAQUS Subroutines (UMAT/VUMAT)

composite_cdm_ap_ply

Continuum damage mechanics framework for AP-PLY composites implemented as an Abaqus VUMAT subroutine.

Mohammad-Abazari/composite_cdm_ap_ply

Continuum damage mechanics framework for AP-PLY composites implemented as an Abaqus VUMAT subroutine.

Mohammad-Abazari/ABAQUS

Some models out of my own research projects are uploaded here. Contact me if anything didn't work.

Mohammad-Abazari/Algorithm

Differential evolution; Particle swarm optimization; Simulated annealing; Jaya

Mohammad-Abazari/Answer-to-History-Mohammad-Reza-Pahlavi-1979

"Answer to History" is the last book M.R. Pahlavi published just before his passing by STEIN AND DAY Publishers New York.

Mohammad-Abazari/CPFEM-VUMAT

Crystal plasticity finite element code, VUMAT file for Abaqus

Mohammad-Abazari/Design-and-development-of-hybrid-Optimization-enabled-Deep-Q-learning-model-for-Covid-19-detection-

The problem of respiratory sound classification has received good attention from the clinical scientists and medical researcher’s community in the last year to the diagnosis of COVID-19 disease. In this paper, the input audio samples are fed into the pre-processing module in which median filtering is done to remove the noise and artifacts from the audio samples. The feature extraction is carried out by considering features, like spectral contrast, Mel frequency cepstral coefficients (MFCC), Empirical Mode Decomposition (EMD) algorithm, spectral flux, Fast Fourier Transform (FFT), spectral roll-off, spectral centroid, Root mean square energy, zero-crossing rate, spectral bandwidth, spectral flatness, power spectral density, mobility complexity, fluctuation index and relative amplitude. Moreover, the deep Q network is applied for Covid-19 classification phase wherein the training of deep Q network is done using the proposed optimization algorithm, named Snake Jaya Honey Badger Optimization (SJHBO) algorithm. The proposed SJHBO algorithm is the hybridization of Jaya Honey Badger Optimization (JHBO) along with Snake optimization. Hence, the developed method achieved the better superior performance based on the accuracy, sensitivity and specificity .

Mohammad-Abazari/fatiguepy

Package to estimate life of random fatigue history with frequency domain methods

Mohammad-Abazari/Finite-Element-MATLAB

MATLAB Finite Element Codes

Mohammad-Abazari/Flywheel_Shape_Optim

MATLAB code for shape optimization of flywheel using JAYA algorithm.

Mohammad-Abazari/Haftka-Optimization-2005

EGM 6365-Structural optimization fall 2005 University of florida

Mohammad-Abazari/Jaya-Honey-Badger-Optimization-based-Deep-Neuro-Fuzzy-Network-structure-for-detection-of-Covid-19-

The Covid-19 virus is fast spreading disease in globally, which threateness billions of human begins. In this paper, Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is introduced for Covid-19 prediction by audio signal. Here, Covid-19 prediction is done using DNFN, and it is trained by developed JHBO algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The Covid-19 prediction process is more indispensable to handle the spread and death occurred rate because of Covid-19. However, early and precise prediction of Covid-19 is more difficult, because of different sizes and resolutions of input image. An effective Covid-19 detection technique is introduced based on hybrid optimization driven deep learning model. The Deep Neuro Fuzzy network (DNFN) is used for detecting Covid-19, which classifies the feature vector as Covid-19 or non Covid-19. Moreover, the DNFN is trained by devised Jaya Honey Badger Optimization (JHBO) approach, which is introduced by combining Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. Covid-19 is respiratory disease, which is usually produced by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). However, it is more indispensable to detect the positive cases for reducing further spread of virus, and former treatment of affected patients. An effectual Covid-19 detection model using devised Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) is developed in this paper. Here, the audio signal is considered as input for detecting Covid-19. The gaussian filter is applied to input signal for removing the noises and then feature extraction is performed. The substantial features, like spectral roll-off, spectral bandwidth, Mel frequency cepstral coefficients (MFCC), spectral flatness, zero crossing rate, spectral centroid, mean square energy and spectral contract are extracted for further processing. Finally, DNFN is applied for detecting Covid-19 and the deep leaning model is trained by designed JHBO algorithm. Accordingly, the developed JHBO method is newly designed by incorporating Honey Badger optimization Algorithm (HBA) and Jaya algorithm. The performance of developed Covid-19 detection model is evaluated using three metrics, like testing accuracy, sensitivity and specificity. The developed JHBO-based DNFN is outperformed than other existing methods testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219. The recent investigation has started for evaluating the human respiratory sounds, like voice recorded, cough, and breathing from hospital confirmed Covid-19 tools, which differs from healthy persons sound. The cough-based detection of Covid-19 also considered with non-respiratory and respiratory sounds data related with all declared situations. This paper explicates the Covid-19 detection approach using designed Jaya Honey Badger Optimization-based Deep Neuro Fuzzy Network (JHBO-based DNFN) with audio sample. The series of steps followed for introduced Covid-19 diagnosis model are pre-processing, feature extraction, and classification. The input audio sample is acquired from a Coswara dataset and gaussian filter is applied. The gaussian filter effectively reduces the salt and pepper noise with minimal duration. Feature extraction process is most significant for precise detection of Covid-19, where spectral bandwidth, spectral roll off, Spectral flatness, Mel frequency cepstral coefficients (MFCC), spectral centroid, root mean square energy, spectral contract, and zero crossing rate are extracted. The Deep learning approach is effectual for disease detection and classification process in medical field. Here, DNFN is utilized for detecting the Covid-19 disease. Moreover, DNFN is trained by developed JHBO approach for obtaining better performance. The proposed JHBO algorithm is newly devised by combining Jaya algorithm and HBA. Here, Jaya algorithm is incorporated with HBA for obtaining improved performance with better convergence speed. The performance of DNFN is estimated with three performance metrics, namely specificity, testing accuracy and sensitivity. The proposed JHBO-based DNFN achieved improved performance testing accuracy, sensitivity and specificity of 0.9176, 0.9218 and 0.9219.

Mohammad-Abazari/large-scale-truss-optimization

Large scale truss optimization using NSGA-II

Mohammad-Abazari/MATH307

Applied Linear Algebra

Mohammad-Abazari/mathematical-python

Introduction to Mathematical Computing with Python and Jupyter

Mohammad-Abazari/MATLAB-SDOF-Solver

Solve SDOF with any loading function

Mohammad-Abazari/Mohammad-Abazari

Config files for my GitHub profile.

Mohammad-Abazari/pinn_wind_bearing

Python scripts for wind turbine main bearing fatigue life estimation with physics-informed neural networks

Mohammad-Abazari/pyMetaheuristic

A python library for: Adaptive Random Search, Ant Lion Optimizer, Arithmetic Optimization Algorithm, Artificial Bee Colony Optimization, Artificial Fish Swarm Algorithm, Bat Algorithm, Biogeography Based Optimization, Cross-Entropy Method, Crow Search Algorithm, Cuckoo Search, Differential Evolution, Dispersive Flies Optimization, Dragonfly Algorithm, Firefly Algorithm, Flow Direction Algorithm, Flower Pollination Algorithm, Genetic Algorithm, Grasshopper Optimization Algorithm, Gravitational Search Algorithm, Grey Wolf Optimizer, Harris Hawks Optimization, Improved Grey Wolf Optimizer, Improved Whale Optimization Algorithm, Jaya, Jellyfish Search Optimizer, Krill Herd Algorithm, Memetic Algorithm, Moth Flame Optimization, Multiverse Optimizer,Pathfinder Algorithm, Particle Swarm Optimization, Random Search, Salp Swarm Algorithm, Simulated Annealing, Sine Cosine Algorithm, Teaching Learning Based Optimization, Whale Optimization Algorithm.

Mohammad-Abazari/Python_Stable_3D_Truss_Analysis

slientruss3d : Python for stable truss analysis and optimization tool

Mohammad-Abazari/Report-flat-slab-column-connection

Mohammad-Abazari/Thick-cylinder-subjected-to-internal-pressure

Lame’s problem - Thick cylinder subjected to internal pressure

Mohammad-Abazari/truss-bridge-optimization

truss-bridge-optimization

Mohammad-Abazari/Truss-Optimization-1

Truss-Optimization-1+

Mohammad-Abazari/Truss-Optimization-using-genetic-Algorithm

Light Weighting of A Truss System using Genetic Algorithm.

Mohammad-Abazari/TrussDesigner

Models and optimizes trusses

Mohammad-Abazari/TrussLayoutOptimization

TrussLayoutOptimization

Mohammad-Abazari/trussOptimization-1

trussOptimization-1

Mohammad-Abazari/TTO

Truss topology optimization

Mohammad-Abazari/USDFLD-Tsai-Wu

USDFLD Tsai-Wu

Mohammad-Abazari/ViscoelasticFoam

Abaqus VUMAT and sample input files for modeling of viscoelastic foams