Briyan Castillo | Mathematical Modeling | Young Scientist Award

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Young Scientist Award

Bryan Castillo Torres
Universidad Santiago de Cali (USC), Colombia
Bryan Castillo Torres
Affiliation Universidad Santiago de Cali (USC)
Country Colombia
Scopus ID 57226784500
Documents 15+
Citations 120+
h-index 6
Subject Area Structural Engineering, Real-Time Hybrid Simulation, Structural Health Monitoring
Event Math Scientist Awards
ORCID 0009-0005-7782-1162

Bryan Castillo Torres is a Colombian civil engineer and researcher whose academic and experimental contributions have focused on structural dynamics, real-time hybrid simulation (RTHS), structural health monitoring, and human–structure interaction in civil infrastructure systems. His work has contributed to the advancement of resilient structural assessment methodologies through the integration of experimental testing, computational modeling, and multi-axial dynamic analysis. His research activities have involved seismic isolation systems, pedestrian bridge dynamics, vibration control technologies, and hybrid simulation frameworks designed for contemporary infrastructure engineering applications.

Abstract

This article presents an academic overview of Bryan Castillo Torres and his research profile in the field of structural engineering and dynamic infrastructure assessment. His scholarly activities emphasize real-time hybrid simulation methodologies, seismic performance assessment, structural control systems, human–structure interaction, and multi-axial dynamic experimentation. Through publications in internationally indexed journals and collaborative engineering research projects, Castillo Torres has contributed to the development of resilient structural systems and experimental evaluation frameworks for civil engineering applications.[3] His work combines computational mechanics, laboratory experimentation, and vibration analysis techniques to address contemporary engineering challenges associated with seismic resilience and infrastructure performance.[4]

Keywords

Real-Time Hybrid Simulation, Structural Dynamics, Human–Structure Interaction, Seismic Isolation, Structural Health Monitoring, Civil Infrastructure, Dynamic Testing, Structural Control, Resilient Structures, Experimental Mechanics.

Introduction

Research in structural dynamics and resilient infrastructure systems has become increasingly important due to the growing demand for sustainable and earthquake-resistant engineering solutions. Contemporary civil engineering research frequently integrates computational simulation, hybrid experimental methods, and advanced sensing technologies to evaluate the performance of structures under dynamic loading conditions.

Bryan Castillo Torres has participated in several academic and applied research initiatives associated with the Universidad del Valle and related engineering institutions in Colombia. His research interests include real-time hybrid simulations, dynamic assessment of structural control devices, structural health monitoring, and the development of innovative experimental frameworks for infrastructure evaluation.[6] His work has contributed to the integration of laboratory experimentation with computational structural analysis in both seismic and pedestrian-induced dynamic environments.

Research Profile

Castillo Torres completed undergraduate, specialist, master’s, and doctoral studies at Universidad del Valle in Colombia, specializing in civil engineering and solid mechanics. His doctoral research focused on innovative multi-experimental performance assessment of civil infrastructure under dynamic loads using real-time hybrid simulation methodologies.

Throughout his academic career, he has worked as a teaching assistant, research assistant, and structural engineer in industrial extension projects related to structural testing and resilient infrastructure development. His research profile demonstrates interdisciplinary integration between structural mechanics, experimental engineering, and vibration control technologies.

  • Dynamic testing using Real-Time Hybrid Simulation systems.
  • Structural health monitoring and multiaxial sensor evaluation.
  • Human–structure interaction assessment in pedestrian bridges.
  • Experimental seismic isolation and vibration control systems.
  • Development of resilient infrastructure testing frameworks.

Research Contributions

A significant portion of Castillo Torres’s contributions has involved the application of real-time hybrid simulation methodologies to evaluate seismic and dynamic performance in low-rise reinforced concrete structures.[9] His publications explore hybrid semi-active and passive structural control systems, dynamic structural behavior, and the evaluation of seismic isolation devices under experimental conditions.

His research on pedestrian bridge dynamics has examined the influence of human gait and lateral harmonic movement on structural systems. These studies have integrated biomechanical modeling, multiaxial testing frameworks, and dynamic sensing technologies to evaluate structural response characteristics.[10]

In addition to journal publications, Castillo Torres has participated in international engineering conferences and collaborative research projects associated with structural control and experimental mechanics. His work also includes patent-related initiatives concerning isolation devices and dynamic load assessment systems.

Publications

  1. “Comprehensive assessment of the seismic performance of an innovative hybrid semi-active and passive state control system for a low-degree-of-freedom structure using Real-Time Hybrid Simulation.” Structural Control and Health Monitoring (2024). DOI:
    https://doi.org/10.1155/2024/9945556
  2. “Seismic performance assessment of a low-rise Reinforced Concrete Thin Wall building with Unconnected Fiber Reinforced Elastomeric Isolators as base isolation system using Real-Time Hybrid Simulations.” Journal of Building Engineering (2024). DOI:
    https://doi.org/10.1016/j.jobe.2024.109303
  3. “Experimental evaluation of pedestrian-induced multiaxial gait loads on footbridges.” Sensors (2024). DOI:
    https://doi.org/10.3390/s24082517
  4. “Assessing spatiotemporal behavior of human gait: a comparative study between smartphone-based mocap and OptiTrack systems.” Experimental Techniques (2024). DOI:
    https://doi.org/10.1007/s40799-024-00716-x

Research Impact

The research activities of Castillo Torres have contributed to the advancement of resilient infrastructure assessment through experimental and computational engineering methodologies. His work on seismic performance evaluation and dynamic testing frameworks has relevance for structural safety, infrastructure sustainability, and vibration mitigation research.

Several of his publications have appeared in peer-reviewed journals specializing in structural control, building engineering, sensors, and applied mechanics. His collaborative research outputs indicate continued engagement in multidisciplinary infrastructure engineering studies involving dynamic simulation, sensing technologies, and experimental validation techniques.

Award Suitability

Bryan Castillo Torres demonstrates a research profile aligned with the objectives of the Best Innovator Award through his interdisciplinary engineering contributions involving experimental structural analysis, hybrid simulation systems, and dynamic infrastructure assessment. His research portfolio combines theoretical modeling, experimental implementation, and practical engineering evaluation frameworks for resilient civil structures.

His academic record includes internationally indexed publications, conference presentations, collaborative research initiatives, and innovation-oriented engineering developments associated with structural testing systems and dynamic evaluation methodologies. Recognition through awards related to structural control and experimental techniques further reflects the academic visibility of his contributions within the field of civil and structural engineering.

Conclusion

Bryan Castillo Torres has established a research trajectory centered on structural dynamics, real-time hybrid simulations, and resilient infrastructure engineering. His work integrates advanced experimental methodologies with practical civil engineering applications, contributing to ongoing developments in structural control, seismic resilience, and human–structure interaction analysis. Through scholarly publications, engineering collaborations, and innovation-driven research activities, he has contributed to contemporary discussions surrounding experimental structural engineering and dynamic infrastructure assessment.

 

References

  1. Castillo, B., Ceron, D., Vides, S., Marulanda, J., & Thomson, P. (2024). Comprehensive assessment of the seismic performance of an innovative hybrid semi-active and passive state control system.
    https://doi.org/10.1155/2024/9945556
  2. Castillo, B., et al. (2024). Seismic performance assessment using Real-Time Hybrid Simulations. Journal of Building Engineering.
    https://doi.org/10.1016/j.jobe.2024.109303
  3. Castillo, B., Artunduaga, E., Marulanda, J., Thomson, P., & Ortiz, A. (2024). Multi-experimental seismic analysis using hybrid simulations.
    https://doi.org/10.1177/13694332241281525
  4. Castillo, B., Marulanda, J., & Thomson, P. (2024). Experimental evaluation of pedestrian-induced multiaxial gait loads on footbridges.
    https://doi.org/10.3390/s24082517

Fuat Türk | Optimization | Best Researcher Award

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Assist. Prof. Dr. Fuat Türk | Optimization | Best Researcher Award

Researcher| Gazi University | Turkey

Assist. Prof. Dr. Fuat Türk is a researcher in artificial intelligence, medical image analysis, and machine learning, currently affiliated with Gazi University, Turkey. His research focuses on developing intelligent diagnostic and segmentation models for healthcare applications, including kidney and renal tumor detection, lung opacity analysis, heart disease prediction, and cancer diagnosis. He has contributed to advancing hybrid deep-learning architectures, CNN-based image fusion models, machine learning–driven feature selection, and optimization techniques for clinical decision support systems. Dr. Türk’s work integrates computational modeling with real-world medical datasets, aiming to improve diagnostic accuracy, automate radiological workflows, and support early disease detection. His published works demonstrate a strong interdisciplinary approach bridging mathematics, computer vision, and biomedical engineering, and his studies have been cited widely in the fields of medical imaging and machine learning.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  1. Türk, F., Lüy, M., & Barışçı, N. (2020). Kidney and renal tumor segmentation using a hybrid V-Net-based model. Mathematics, 8(10), 1772. Citations: 98

  2. Türk, F. (2023). Analysis of intrusion detection systems in UNSW-NB15 and NSL-KDD datasets with machine learning algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, 12(2), 465–477. Citations: 59

  3. Türk, F., & Kökver, Y. (2023). Detection of lung opacity and treatment planning with three-channel fusion CNN model. Arabian Journal for Science and Engineering, 49, 2973–2985. Citations: 17

  4. Türk, F. (2024). Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection. Signal, Image and Video Processing. Citations: 15

  5. Akkur, O. E. E., & Türk, F. (2022). Breast cancer diagnosis using feature selection approaches and Bayesian optimization. Computer Systems Science and Engineering, 45(2), 1017–1031. Citations: 13

Vahid Goodarzimehr | Optimization | Best Researcher Award

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Assist. Prof. Dr. Vahid Goodarzimehr | Optimization | Best Researcher Award

Assistant professor | Shahid Chamran University of Ahvaz | Iran

Dr. Vahid Goodarzimehr is a pioneering researcher in computational mechanics, structural optimization, and metaheuristic algorithms, with his groundbreaking work inspired by Einstein’s theory of relativity. He introduced the Special Relativity Search (SRS) algorithm — a novel metaheuristic method that simulates relativistic physics to solve complex engineering and mathematical optimization problems. His research integrates artificial intelligence, optimization theory, and physics-based modeling to advance dynamic and structural design systems. With publications in top-tier Q1 journals such as Knowledge-Based Systems, Computer Methods in Applied Mechanics and Engineering, and Engineering Structures, Dr. Goodarzimehr has established himself as a leading innovator in physics-inspired computational optimization. His algorithms like SRS, MOSRS, and SABO have significantly influenced the next generation of engineering problem-solving tools.

Profiles: Scopus | Orcid | Google Scholar

Featured Publications

  1. Goodarzimehr, V., Shojaee, S., Hamzehei-Javaran, S., & Talatahari, S. (2022). Special relativity search: A novel metaheuristic method based on special relativity physics. Knowledge-Based Systems, 257, 109484. Citation count: 101.

  2. Omidinasab, F., & Goodarzimehr, V. (2020). A hybrid particle swarm optimization and genetic algorithm for truss structures with discrete variables. Journal of Applied and Computational Mechanics, 6(3), 593–604. Citation count: 64.

  3. Goodarzimehr, V., Topal, U., Das, A. K., & Vo-Duy, T. (2023). Bonobo optimizer algorithm for optimum design of truss structures with static constraints. Structures, 50, 400–417. Citation count: 37.

  4. Goodarzimehr, V., Talatahari, S., Shojaee, S., & Hamzehei-Javaran, S. (2023). Special relativity search for applied mechanics and engineering. Computer Methods in Applied Mechanics and Engineering, 403, 115734. Citation count: 35.

  5. Talatahari, S., Goodarzimehr, V., & Taghizadieh, N. (2020). Hybrid teaching-learning-based optimization and harmony search for optimum design of space trusses. Journal of Optimization in Industrial Engineering, 13(1), 177–194. Citation count: 32.

 

 

 

Liangliang Sun | Optimization | Best Researcher Award

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Prof. Liangliang Sun | Optimization | Best Researcher Award

Dean at Northeastern University, China

Prof. Liangliang Sun 🌟 is a distinguished scholar in control theory and intelligent scheduling systems, currently serving at Northeastern University, China 🏫. With a robust academic background including a Ph.D. from both Northeastern University and the University of Connecticut 🎓, he has cultivated deep expertise in steelmaking-continuous casting optimization, energy systems, and industrial automation ⚙️. His prolific research spans over 18 high-impact publications 📚, multiple national-level projects 🎯, and several patented innovations 🔬. Prof. Sun has earned accolades such as the Xingliao Talent Plan 🌟 and has been a two-time champion 🏆 in China’s prestigious Challenge Cup competitions. His contributions extend into teaching, editorial responsibilities 🖋️, and international collaborations 🌐. Known for integrating theory with real-world industrial applications 🔄, he bridges gaps between research and practice seamlessly. As a dynamic thought leader 🚀 in control engineering and smart manufacturing, Prof. Sun continues to inspire innovation and academic excellence across global platforms 🌍.

Professional Profile 

Scopus Profile

🎓 Education

Prof. Liangliang Sun’s academic journey is marked by scholarly excellence and international immersion 🌍. He earned his Bachelor’s and Master’s degrees in Automation and Control Engineering from Northeastern University, China 🇨🇳, laying the groundwork for a solid technical foundation. He later pursued a prestigious dual Ph.D. from Northeastern University and the University of Connecticut 🇺🇸, enriching his expertise through cross-cultural academic synergy. His doctoral research focused on intelligent optimization and industrial system control, setting the stage for impactful innovation. This global academic trajectory reflects a deep commitment to advancing modern engineering frontiers 🔬. With early exposure to cutting-edge research environments, Prof. Sun developed a unique ability to integrate Eastern precision with Western analytical frameworks, a blend that defines his distinctive academic persona 📘.

🏢 Professional Experience

Prof. Sun has cultivated an impressive professional track record in academic and applied engineering settings 🔧. Currently a full professor at Northeastern University’s School of Information Science and Engineering 🏫, he teaches and mentors in systems engineering and automation. His career includes a postdoctoral tenure at the University of Connecticut 🇺🇸, where he deepened his research in smart manufacturing. He has led numerous national research projects as Principal Investigator, collaborating with top-tier steel and energy enterprises 🏭. Beyond academia, he offers expert consultations on intelligent scheduling, production line efficiency, and energy optimization 💡. Prof. Sun also holds leadership roles in editorial boards and technical committees, actively shaping global discourse in control systems and industrial AI 📊. His trajectory exemplifies research-driven engineering leadership ⚙️.

🔍 Research Interest

Prof. Sun’s research orbits around intelligent optimization, industrial control, and smart scheduling algorithms 🚀. He explores dynamic production scheduling in steelmaking, aiming to enhance process integration, reduce emissions, and boost system efficiency 🔄. His work leverages artificial intelligence, deep learning, and energy modeling to address complex industrial challenges. With a passion for merging theory with real-world applicability 🛠️, his investigations span multi-objective optimization, cyber-physical systems, and data-driven control frameworks. He has also ventured into hydrogen-rich energy systems and low-carbon manufacturing pathways 🌱. His scientific vision aligns seamlessly with the evolving demands of Industry 4.0 and sustainable engineering 🌐. Through over 18 peer-reviewed papers, Prof. Sun has contributed significantly to reshaping production intelligence and process automation across diverse industrial landscapes 📚.

🏅 Awards and Honors

Prof. Liangliang Sun has garnered prestigious awards that reflect his academic leadership and innovation excellence 🏆. He’s a dual Champion of the National Challenge Cup, a rare feat highlighting his creativity and technical mastery early on 🧠. He is a selected recipient of the Xingliao Talent Plan, the “Hundred and Ten Thousand Talents Project”, and the Young Top Talent in Liaoning Province 🌟. Recognized nationally and provincially, he exemplifies the profile of a high-impact scholar pushing disciplinary boundaries 🚧. His awarded projects from the National Natural Science Foundation of China (NSFC) underline the national trust in his research potential 🧪. These honors demonstrate his sustained excellence in advancing automation, energy intelligence, and industrial digitization. Each accolade fortifies his status as a distinguished researcher 💼.

🧩 Conclusion

Prof. Liangliang Sun represents a remarkable blend of intellectual rigor, innovation, and practical relevance 🎯. With interdisciplinary strength across automation, AI, and energy systems, he bridges theoretical research with industrial transformation 🌐. His impactful publications, pioneering projects, and patented inventions underscore a career built on thoughtful innovation and global relevance 💡. He leads with purpose, engages in collaborative discovery, and mentors the next generation of scientific leaders 👨‍🏫. Prof. Sun’s contributions enhance the technological landscape and redefine the future of smart manufacturing and sustainable engineering 🔋. As a thought leader, he continues to shape high-impact solutions for complex industrial ecosystems, leaving a lasting imprint on global engineering science 🌟. His career is a testament to vision, resilience, and relentless pursuit of excellence 🔝.

Publications Top Notes

📘 Title: A learning-enhanced ant colony optimization algorithm for integrated planning and scheduling in hot rolling production lines under uncertainty
Authors: S. Jiang, L. He, L. Cao, L. Sun, G. Peng
Year: 2025
Citations: 1
Source: Swarm and Evolutionary Computation

📘 Title: A Self-adaptive two stage iterative greedy algorithm based job scales for energy-efficient distributed permutation flowshop scheduling problem
Authors: Y. Yu, Q. Zhong, L. Sun, X. Jing, Z. Wang
Year: 2025
Source: Swarm and Evolutionary Computation

📘 Title: Research on steelmaking-continuous casting cast batch planning based on an improved surrogate absolute-value Lagrangian relaxation framework
Authors: C. Li, L. Sun
Year: 2025
Source: International Journal of Automation and Control

📘 Title: An Online Learning-Based mACO Approach for Hot Rolling Scheduling Problems Involving Dynamic Order Arrivals
Authors: S. Jiang, Q. Liu, L. Cao, L. Sun
Year: 2025
Source: IEEE Transactions on Automation Science and Engineering

📘 Title: A robust optimization approach for steeling-continuous casting charge batch planning with uncertain slab weight
Authors: C. Li, L. Sun
Year: 2024
Citations: 1
Source: Journal of Process Control

📘 Title: Optimal control of three-dimensional unsteady partial differential equations with convection term in continuous casting
Authors: Y. Yu, Y. Wang, X. Pang, L. Sun
Year: 2024
Citations: 1
Source: Computers and Mathematics with Applications

📘 Title: Optimal Scheduling Works for Two Employees with Ordered Criteria
Authors: N.M. Matsveichuk, Y.N. Sotskov, L. Sun
Year: 2024
Source: WSEAS Transactions on Business and Economics

Zohaib Khan | Optimization | Best Researcher Award

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Dr. Zohaib Khan | Optimization | Best Researcher Award

Jiangsu University, China

Zohaib Khan is a dedicated researcher specializing in machine learning, object detection, and control science engineering, with a strong focus on precision agriculture and AI-driven automation. Currently pursuing a PhD at Jiangsu University, China, he has made significant contributions to deep learning-based agricultural robotics, publishing multiple first-author papers in high-impact SCI Q1, Q2, and EI journals. His work emphasizes real-time detection, optimization algorithms, and AI-driven sustainability solutions. With extensive mentoring experience (50+ Bachelor’s and 10 Master’s students), he has played a key role in academic development. Zohaib has received numerous national and international awards, including first prizes in elite research and innovation competitions. His technical expertise spans Python, MATLAB, LaTeX, and AI-driven modeling, complementing his ability to lead interdisciplinary research. With a passion for advancing AI applications in agriculture, he continues to drive innovation in sustainable and automated farming solutions.

Professional Profile 

Google Scholar
Scopus Profile
ORCID Profile

Education

Zohaib Khan is currently pursuing a PhD in Control Science Engineering at Jiangsu University, China (2022–2026), specializing in machine learning and object detection. He previously earned an MSc in Electrical Engineering (2019–2022) from the same institution, focusing on power systems and renewable energy. His Bachelor’s degree in Electrical Power Engineering (2013–2017) from Swedish College of Engineering and Technology, Pakistan, laid the foundation for his technical expertise. His early academic years were marked by excellence, having completed Pre-Engineering at Fazaia Degree College (2011–2013) and his Secondary School Certificate (2009–2011) at Agricultural University Public School. Zohaib’s academic journey is distinguished by his strong analytical skills and passion for integrating AI and automation in engineering solutions. His education reflects a deep commitment to advanced research, innovation, and interdisciplinary problem-solving, positioning him as a future leader in AI-driven technologies and precision agriculture.

Professional Experience

Zohaib Khan has gained substantial experience in both academic research and engineering practice. As an intern at WAPDA, Pakistan, he developed hands-on expertise in power distribution and transmission lines, strengthening his understanding of grid operations and maintenance. Later, as an Electrical Engineer at LIMAK (JV) ZKB – CPEC Project (2017–2018), he contributed to electrical system design, installation, and maintenance, gaining valuable project management experience. His role involved troubleshooting, safety compliance, and interdisciplinary collaboration, enhancing his problem-solving capabilities. In academia, Zohaib has mentored over 50 Bachelor’s and 10 Master’s students, guiding them through research projects in machine learning, object detection, and automation. His strong writing, teaching, and IT skills have been instrumental in fostering innovation. His diverse experience, spanning applied research and engineering implementation, makes him a well-rounded professional capable of driving breakthroughs in AI-powered automation and precision agriculture.

Research Interest

Zohaib Khan’s research focuses on machine learning, deep learning, object detection, and AI-driven automation, with applications in precision agriculture and robotics. His studies revolve around real-time detection, optimization algorithms, and advanced control systems for agricultural sustainability and industrial automation. He has pioneered AI-driven precision farming techniques, developing deep learning-enhanced YOLOv7 and YOLOv8 algorithms for real-time crop health assessment and robotic spraying systems. Additionally, his work explores autonomous navigation in unstructured farmlands, energy-efficient control systems, and reinforcement learning for AI-based decision-making. His research extends to risk assessment in renewable energy systems, contributing to more efficient and resilient smart grids. Through interdisciplinary collaborations, Zohaib continues to push the boundaries of AI in sustainable agriculture, robotics, and industrial automation, aiming to develop intelligent, scalable, and high-impact solutions for modern technological challenges.

Awards and Honors

Zohaib Khan has received multiple prestigious awards recognizing his contributions to research, innovation, and academic excellence. He has won First Prizes in National Competitions, including the China University Business Elite Challenge (2024) and the Brand Planning Competition (2024). His research excellence was acknowledged with the Excellent Paper Award at the Sino-award (2021) and special recognition in Jiangsu Province Graduate Energy-saving and Low-Carbon Research Competition (2023). Additionally, he was honored as an Outstanding Student in the 17th “Yale School of Jiangsu University” program and received a Certificate of Excellence for Teaching Assistance. His leadership and public speaking skills earned him first place in an English debate at Jiangsu University. These accolades reflect his dedication to research, leadership in innovation, and commitment to advancing AI applications in engineering and agriculture, solidifying his reputation as a promising researcher in his field.

Conclusion

Zohaib Khan’s academic, professional, and research journey showcases his exceptional talent in AI-driven automation, machine learning, and precision agriculture. His extensive experience in research, mentoring, and engineering practice positions him as a leading scholar in intelligent agricultural robotics and sustainable AI applications. With a strong publication record in high-impact journals (SCI Q1, Q2, and EI) and multiple national and international awards, he has demonstrated his ability to drive innovation and solve real-world problems. His work in deep learning-based automation and AI-driven optimization techniques continues to push the boundaries of technology for sustainability and efficiency. As he progresses in his career, Zohaib remains committed to advancing cutting-edge research, fostering academic collaborations, and contributing transformative solutions in AI, robotics, and smart energy systems. His dedication and achievements make him a strong candidate for prestigious research awards and a key contributor to the future of AI in engineering and agriculture.

Publications Top Noted