Aydin Shishegaran


Aydin Shishegaran

I have extensive work experience as a structural engineer and a structural designer in 17 big civil projects for over ten years in Tehran. In Germany, I have worked since 2021 as a structural engineer and designed 60 projects until August 2022, when I updated this text.
Not only have I published 29 technical papers and reviewed 212 scientific papers in the structural engineering and building materials fields in high-quality journals, but I have also worked as a research assistant in two institutes in Iran, which this point shows that I have the ability to work as engineer and researcher, and maybe in the future as a professor at a university.
Other my achievements can be mentioned as follows:
-I have introduced and published two new machine learning methods and a new multi-criteria decision analysis method in my publications.
-I have created and published 5 patents.
-Award from Iran’s National Elites Foundation for one of my patents.
-Award from the Ministry of Science, Research and Technology (Iran) for scholarship visiting.

LinkedIn Profile

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IU International University of Applied Sciences logo
  • Part-time · 1 yr 9 mos
    Hybrid
Heinze Akademie logo
  • Heinze Akademie · Part-time
    Feb 2024 – Present · 8 mos
    Hamburg, Germany · On-site
  • Peine Ingenieurbüro GmbH · Full-time
    Feb 2023 – Present · 1 yr 8 mos
    Peine, Lower Saxony, Germany · On-site

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Bauhaus-Universität Weimar logo
  • Ph.D., Civil and Structural engineering
    Jan 2021 – Dec 2024
Iran University of Science and Technology logo
  • Ph.D., Civil engineering and Environmental engineering
    Sep 2016 – Mar 2022

 

Semnan University logo
  • Bachelor’s degree, Civil engineering
    Sep 2005 – Mar 2011

At CAEAssistant.com, we collaborate with a distinguished group of researchers who bring a wealth of academic and industry experience to our platform. These experts are not only leading voices in their respective fields but also active contributors to cutting-edge research, with numerous ISI-indexed publications and industry-relevant projects under their belts. Their deep expertise in areas such as finite element analysis, composite materials, and advanced simulation techniques ensures that the courses they create are both academically rigorous and practically valuable. By learning from these accomplished professionals, our students gain access to the latest knowledge and insights, empowering them to excel in their careers and research endeavors.

Computational Predictions for Predicting the Performance of Structure

 340.0

This package focuses on developing and applying predictive models for the structural analysis of steel and concrete components subjected to fire and subsequent earthquake loading. To accurately simulate the complex behavior of these structures, finite element analysis (FEA) using ABAQUS is employed. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept. However, due to the computational demands of FEA, various machine learning techniques, including regression models, Gene Expression Programming (GEP), Adaptive Network-Based Fuzzy Inference Systems (ANFIS), and ensemble methods, are explored as surrogate models. These models are trained on large datasets of FEA results to predict structural responses efficiently. The performance of these models is evaluated using statistical metrics such as RMSE, NMSE, and coefficient of determination.

Damage Prediction in Reinforced Concrete Tunnels under Internal Water Pressure

 370.0

This tutorial package equips you with the knowledge and tools to simulate the behavior of reinforced concrete tunnels (RCTs) subjected to internal water pressure. It combines the power of finite element (FE) modeling with artificial intelligence (AI) for efficient and accurate analysis. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept.

By leveraging Artificial Intelligence (AI) techniques such as regression, GEP, ML, DL, hybrid, and ensemble models,  we significantly reduce computational costs and time while achieving high accuracy in predicting structural responses and optimizing designs.

Computational Modeling of Steel Plate Shear Wall (SPSW) Behavior

 320.0

This course equips engineers with the tools to design and analyze Steel Plate Shear Wall (SPSW) and Reinforced Concrete Shear Walls (RCSW) subjected to explosive loads. Traditional Finite Element (FE) simulation is time-consuming and requires numerous samples for accurate results. This package offers a more efficient approach using Artificial Intelligence (AI) models trained on FEA data. You'll learn to develop FE models of SPSW and RCSW in ABAQUS software, considering material properties, interactions, and boundary conditions. The Taguchi method optimizes the number of samples needed for FE analysis, and this method is used with SPSS after explanation its concept.

We then delve into AI modeling using MATLAB. Explore various methods like regression, Machine Learning (ML), Deep Learning (DL), and ensemble models to predict the behavior of SPSW and RCSW under blast loads. Statistical analysis helps compare model accuracy. By combining FE analysis with AI models, you'll gain a powerful tool for designing blast-resistant structures while saving time and resources.

Earthquake Damping in 8-Story Structure using Bypass Viscous Damper | Seismic Damping in Masonry Cladding

 230.0

In this package, the dynamic behavior of a developed bypass viscous damper is thoroughly evaluated as an advanced solution for earthquake damping. This innovative seismic damping device features a flexible, high-pressure hose that serves as an external orifice, functioning as a thermal compensator to reduce viscous heating during dynamic events. By adjusting the hose’s dimensions, the damper’s performance can be fine-tuned to provide optimal damping properties. Comprehensive simulations using CFD models in ABAQUS and structural analysis in SAP2000 validate the damper’s effectiveness. The package also offers a simplified design procedure for integrating these dampers into structures, demonstrated through an 8-story hospital case study, where the dampers significantly reduce structural demands and enhance the performance of nonstructural elements during seismic events.