Reza Sojoudizadeh | Structural Engineering | Best Researcher Award

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Assist. Prof. Dr. Reza Sojoudizadeh | Structural Engineering | Best Researcher Award

Assistant Professor at Islamic Azad University, Iran.

Dr. Reza Sojoudi Zadeh is an Associate Professor in the Department of Civil Engineering at Mah.C., Islamic Azad University, Mahabad, Iran. He specializes in structural engineering, with a focus on optimization techniques, seismic performance assessment, and concrete technology. With years of academic and research experience, he has significantly contributed to the advancement of structural engineering through teaching, research, and scholarly publications.

Professional Profile:

Scopus

Orcid

Google Scholar

Education Background

Dr. Sojoudi Zadeh obtained his Ph.D. in Civil Engineering (Structural Engineering) from Urmia University, Iran, in 2018, where he explored the seismic performance-based life cycle cost optimization of steel moment frames using soft computing techniques. He earned his MSc in Civil Engineering (Structural Engineering) from Tabriz University in 2002, with a thesis on dynamic soil-structure interaction using SAP2000 software. He also completed his BSc in Civil Engineering at Tabriz University in 2000.

Professional Development

Dr. Sojoudi Zadeh has been actively involved in academia, teaching a range of undergraduate and graduate courses, including Statics, Design of Reinforced Concrete Structures, Concrete Technology, Structural Optimization, and Finite Element Analysis. In addition to his teaching responsibilities, he has supervised numerous research projects, contributed to the development of advanced structural engineering methodologies, and collaborated on various industry and academic initiatives aimed at enhancing structural performance and optimization.]

Research Focus

His research focuses on structural optimization, seismic performance analysis, tall buildings, and concrete technology. His work integrates computational techniques and experimental approaches to improve the resilience and cost-effectiveness of modern structures. He is particularly interested in developing innovative methodologies for optimizing structural designs and enhancing the durability of concrete-based structures.

Author Metrics:

Dr. Sojoudi Zadeh has an extensive publication record in peer-reviewed journals and conferences, with a strong citation impact. His Google Scholar profile (Link) reflects his contributions to structural engineering research. His ORCID ID is 0000-0001-8923-7343, ensuring recognition of his scholarly work and research contributions.

Awards and Honors:

Throughout his academic career, Dr. Sojoudi Zadeh has received several accolades for his research and contributions to structural engineering. His work on seismic optimization and concrete technology has been acknowledged at various academic and industry conferences, solidifying his reputation as a leading researcher in his field. His dedication to education and research continues to impact the structural engineering community.

Publication Top Notes

1. Modified Sine-Cosine Algorithm for Sizing Optimization of Truss Structures with Discrete Design Variables

Authors: S. Gholizadeh, R. Sojoudizadeh
Journal: Iran University of Science and Technology
Volume: 9 (2), Pages: 195-212
Year: 2019
Citations: 33
Abstract: This study presents a modified version of the Sine-Cosine Algorithm (SCA) tailored for the discrete sizing optimization of truss structures. The algorithm incorporates adaptive mechanisms to enhance convergence speed and solution accuracy. The methodology is tested on benchmark truss structures, demonstrating significant improvements in weight reduction and structural performance.

2. Shape and Size Optimization of Truss Structure by Means of Improved Artificial Rabbits Optimization Algorithm

Authors: S.L. SeyedOskouei, R. Sojoudizadeh, R. Milanchian, H. Azizian
Journal: Engineering Optimization
Volume: 56 (12), Pages: 2329-2358
Year: 2024
Citations: 8
Abstract: This paper introduces an improved Artificial Rabbits Optimization Algorithm (I-ARO) to solve structural optimization problems in truss structures. The proposed method integrates novel search strategies to enhance exploration and exploitation capabilities. Numerical simulations on different truss models confirm the effectiveness of the approach in minimizing weight while maintaining structural integrity.

3. Elite Particles Method in Discrete Metaheuristic Optimization of Structures

Authors: R. Sojoudizadeh, S. Gholizadeh
Journal: Journal of Civil and Environmental Engineering
Volume: 52 (108), Pages: 39-48
Year: 2022
Citations: 2
Abstract: This study introduces the Elite Particles Method (EPM) as a novel metaheuristic optimization technique for solving structural optimization problems. EPM improves upon traditional discrete optimization algorithms by incorporating elite-driven search mechanisms. The results demonstrate enhanced performance in optimizing truss and frame structures compared to existing methods.

4. Sizing Optimization of Truss Structures with Discrete Design Variables Using Combined PSO Algorithm with Special Particles Method

Authors: A. Gheibi, R. Sojoudizadeh, H. Azizian, M. Gheibi
Journal: Journal of Optimization in Industrial Engineering
Volume: 16 (2), Pages: 295-302
Year: 2024
Citations: 1
Abstract: This paper presents a hybrid optimization approach that integrates Particle Swarm Optimization (PSO) with the Special Particles Method (SPM) for discrete sizing optimization of truss structures. The hybrid method effectively balances exploration and exploitation, leading to more efficient structural designs with reduced computational costs.

5. Seismic Optimization of Steel Mega‐Braced Frame With Improved Prairie Dog Metaheuristic Optimization Algorithm

Authors: T. PayamiFar, R. Sojoudizadeh, H. Azizian, L. Rahimi
Journal: The Structural Design of Tall and Special Buildings
Volume: 34 (3), Article ID: e2207
Year: 2025
Abstract: This research develops an improved version of the Prairie Dog Optimization Algorithm (PDMA) to optimize the seismic performance of steel mega‐braced frames. The study focuses on minimizing structural responses under seismic loads by optimizing brace configurations. Simulation results indicate that the proposed algorithm outperforms conventional optimization techniques in terms of both efficiency and robustness.

Conclusion

Based on his research excellence, innovative methodologies, and contributions to structural engineering, Dr. Reza Sojoudizadeh is a highly suitable candidate for the Best Researcher Award. His work has significantly advanced optimization techniques in structural engineering, and he has demonstrated a consistent record of high-quality publications and impact.

To further strengthen his profile, he could focus on interdisciplinary research, international collaborations, and public engagement. However, his current research achievements, academic experience, and algorithmic innovations already make him an outstanding contender for the award.

Roberto Rocchetta | Network Resilience | Best Researcher Award

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Dr. Roberto Rocchetta | Network Resilience | Best Researcher Award

Dr Eng at SUPSI – Department of Environment, Construction, and Design (DACD), Switzerland📖

Dr. Roberto Rocchetta is a post-doctoral researcher at the Technical University of Eindhoven (TU/e), collaborating with Signify, BMW, Philips, and the Department of Mathematics and Computer Science. His multidisciplinary expertise spans uncertainty quantification, reliability engineering, machine learning, and energy systems. Dr. Rocchetta has made significant contributions to power grid resilience, optimization, and vulnerability analysis, with over 10 peer-reviewed journal articles and 15 conference papers. He has worked at NASA Langley and National Institute of Aerospace (NIA) and has held academic positions at several prestigious institutions.

Profile

Scopus Profile

Orcid Profile

Google Scholar Profile

Education Background🎓

  • Ph.D. in Reliability Engineering, Uncertainty Quantification, and Computer Science, University of Liverpool, UK (2015-2019)
  • Master of Research in Decision-Making under Risk and Uncertainty, University of Liverpool, UK (2014-2015)
  • Master’s and Bachelor’s in Energy Engineering, University of Bologna, Italy (2008-2014)

Professional Experience🌱

  • Postdoc, TU/Eindhoven & Signify, Department of Mathematics and Computer Science (2021-Present): Focus on LEDs reliability, survival analysis, and experimental design.
  • Postdoc, TU/Eindhoven & Philips, Department of Mathematics and Computer Science (2019-2021): Focus on AI and machine learning for MRI maintenance optimization.
  • Research Scholar, NIA and NASA Langley, USA (2017-2018): Focus on data-driven reliability and robustness optimization.
  • Visiting Ph.D. Candidate, ETH Zurich and Milan Polytechnic (2017-2018): Focus on energy systems and risk analysis.
  • Intern, ARAMIS Start-Up, Milan (2017): Focus on reinforcement learning for maintenance optimization.
Research Interests🔬

Dr. Rocchetta’s research interests lie in uncertainty quantification, resilience and reliability analysis of complex systems, stochastic optimization, machine learning, and data-driven decision-making. His work has a strong focus on power grids, energy systems, and network modeling, with an emphasis on optimizing and quantifying uncertainties in critical infrastructures.

Author Metrics

Dr. Roberto Rocchetta is an accomplished researcher with an extensive publication record. He is the first author of more than 10 peer-reviewed journal articles and 15 conference papers, with his works collectively receiving over 450 citations on Google Scholar. His research has achieved significant recognition within the scientific community, leading to an h-index of 8 on Scopus. These metrics reflect his impactful contributions to the fields of uncertainty quantification, reliability engineering, and complex systems analysis. His research continues to influence both academic and practical advancements, demonstrating the broad applicability and importance of his work.

Key Contributions

  • Developed computational frameworks for power grid reliability, vulnerability, and resilience analysis.
  • Contributed to hybrid decision-making methods combining model-based and data-driven approaches.
  • Active reviewer for top journals and technical committees in reliability and risk management fields.

Honours and Awards

  • Humboldt Research Fellowship Award (pending)
  • Best Poster Award, ISIPTA Conference 2021
  • First Prize, Math. Competitive Game 2017 (Monte Carlo approach)
  • Best Paper Award, TU/e Postdoc Best Paper Ceremony

Skills and Software Proficiency

  • Data Analysis & Simulation: MATLAB, Python, Julia, R
  • Energy Systems & Multi-Physics: COMSOL, MatPower
  • Writing & Visualization: LaTeX, JabRef, Mendeley, Office Suite
  • Database Management: Vertica, SQL
Publications Top Notes 📄

1. A Reinforcement Learning Framework for Optimal Operation and Maintenance of Power Grids

  • Authors: R. Rocchetta, L. Bellani, M. Compare, E. Zio, E. Patelli
  • Journal: Applied Energy
  • Volume: 241
  • Pages: 291-301
  • Year: 2019
  • Citation Count: 240
  • Focus: The paper introduces a reinforcement learning framework for the optimal operation and maintenance of power grids, addressing challenges in decision-making under uncertainty.

2. On-line Bayesian Model Updating for Structural Health Monitoring

  • Authors: R. Rocchetta, M. Broggi, Q. Huchet, E. Patelli
  • Journal: Mechanical Systems and Signal Processing
  • Volume: 103
  • Pages: 174-195
  • Year: 2018
  • Citation Count: 130
  • Focus: This paper discusses the use of Bayesian model updating techniques for improving the reliability of structural health monitoring systems, emphasizing real-time performance adjustments.

3. Risk Assessment and Risk-Cost Optimization of Distributed Power Generation Systems Considering Extreme Weather Conditions

  • Authors: R. Rocchetta, Y. Li, E. Zio
  • Journal: Reliability Engineering & System Safety
  • Volume: 136
  • Pages: 47-61
  • Year: 2015
  • Citation Count: 123
  • Focus: The research focuses on risk assessment methodologies and the optimization of risk-cost balances for distributed power generation systems, particularly under extreme weather conditions.

4. A Power-Flow Emulator Approach for Resilience Assessment of Repairable Power Grids Subject to Weather-Induced Failures and Data Deficiency

  • Authors: R. Rocchetta, E. Zio, E. Patelli
  • Journal: Applied Energy
  • Volume: 210
  • Pages: 339-350
  • Year: 2018
  • Citation Count: 96
  • Focus: This paper introduces a power-flow emulator approach designed to assess the resilience of power grids, accounting for failures caused by weather events and the challenges of insufficient data.

5. Assessment of Power Grid Vulnerabilities Accounting for Stochastic Loads and Model Imprecision

  • Authors: R. Rocchetta, E. Patelli
  • Journal: International Journal of Electrical Power & Energy Systems
  • Volume: 98
  • Pages: 219-232
  • Year: 2018
  • Citation Count: 68
  • Focus: This research assesses the vulnerabilities of power grids by incorporating stochastic load behavior and the imprecision in modeling, contributing to a more robust understanding of system reliability.

Conclusion

Dr. Roberto Rocchetta is an outstanding researcher whose work significantly contributes to the fields of uncertainty quantification, resilience analysis, and optimization of power grids. His impactful publications, strong interdisciplinary expertise, and real-world relevance of his research make him a strong contender for the Best Researcher Award. His ability to collaborate across academic and industrial boundaries, along with his recognition in the scientific community, further strengthens his candidacy. By expanding his collaborations and industry engagement, Dr. Rocchetta has the potential to elevate his impact even further, making substantial contributions to the global energy sector.