Mehran Akhavan | Tall building | Best Researcher Award

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Dr. Mehran Akhavan | Tall building | Best Researcher Award

Islamic Azad University | Iran

Dr. Mehran Akhavan Salmassi is a dedicated structural engineering researcher whose work focuses on advancing the safety, resilience, and performance of modern infrastructure systems. Holding a Ph.D. in Structural Engineering from the Islamic Azad University, Semnan Branch, he has developed a strong academic and research profile grounded in innovation, analytical rigor, and global engagement. His core areas of expertise include tall building engineering, structural control, and the development of high-performance structural systems capable of withstanding dynamic loads and extreme environmental conditions. Over the years, Dr. Salmassi has contributed significantly to the field through 18 peer-reviewed publications, reflecting both theoretical advancements and practical engineering solutions. His research also includes three recognized novelties, demonstrating his commitment to pioneering new methods and expanding current engineering knowledge. Dr. Salmassi actively contributes to the scientific community as a reviewer for three international journals, supporting the peer-review process and ensuring high-quality scholarly dissemination. His reputation as a knowledgeable and engaging scholar has earned him invitations as a speaker and distinguished speaker in more than 20 international conferences, where he has presented research findings, shared technical insights, and collaborated with experts across the globe. Through his growing academic footprint, he continues to foster meaningful collaborations that bridge research, industry practice, and societal needs. His work emphasizes the importance of innovation in structural engineering, particularly in the design of safe and sustainable tall buildings for rapidly developing urban environments. Driven by a passion for scientific excellence and public safety, Dr. Salmassi aims to contribute solutions that not only advance engineering practice but also inspire emerging researchers in the field.

Profile: Google Scholar
Publications

Akhavan Salmassi, M., Kheyroddin, A., & Hemmati, A. (2020). Seismic behavior of end walls in RC tall buildings with torsional irregularity. Magazine of Civil Engineering, 97(07).

Salmassi, M. A., Kheyroddin, A., & Hemmati, A. (2024). Enhancement of the performance of two tall buildings with end shear walls using nonlinear time history analysis: A case study. Iranian Journal of Science and Technology, Transactions of Civil Engineering.

Akhavan Salmassi, M., Kheyroddin, A., & Hemmati, A. (2023). Evaluation of reinforced concrete tall buildings with end shear walls subjected to sequences far from the fault. Scientia Iranica.

Kheyroddin, A., & Akhavan Salmassi, M. (2018). Effect of end walls in tall buildings with square plan and flexural concrete frames under earthquake. In 7th National and 3rd International Conference on Modern Materials and Structural Systems.

Akhavan Salmassi, M., Kheyroddin, A., & Hemmati, A. (2021). Seismic behavior of tall buildings with end shear walls and opening. Journal of Seismology and Earthquake Engineering, 23(2), 55–70.

Akhavan Salmassi, M., Gerami, M., & Heidari Tafreshi, A. (2019). Evaluation of flexible steel frame structures with post-tensioned cables to sequences far from fault. Journal of Structural and Construction Engineering, 6(Special Issue 3), 221–234.

Alireza Javid | Concrete and FRP-strengthened structures | Research Excellence Award

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Mr. Alireza Javid | Concrete and FRP-strengthened structures | Research Excellence Award

Graduate Researcher in Structural Engineering | Sharif University of technology | Iran

Alireza Javid is a civil and structural engineering researcher whose work centers on sustainable construction materials, structural health monitoring, and the integration of advanced machine learning techniques into structural assessment and design. He holds an M.Sc. in Structural Engineering from Sharif University of Technology, where his research investigated the effects of high temperatures on cement bonding and pozzolanic concrete. His scholarly contributions reflect a strong interdisciplinary foundation, bridging experimental mechanics, data-driven modeling, and computational optimization. Javid has authored multiple peer-reviewed journal articles in high-impact international outlets, with published work addressing machine learning–based predictions of concrete compressive strength, bond behavior in FRP–timber systems under thermal cycling, high-temperature concrete overlay interactions, and the mechanical characterization of industrial by-product concrete. His publications collectively exceed 40 citations, demonstrating growing recognition within the structural materials and AI-in-construction communities. His research also extends to ultrasonic pulse velocity prediction, temperature-dependent performance of fiber-reinforced concrete, and microstructural deterioration of FRP composites in aggressive environments. Several manuscripts under review explore impact resistance of stabilized rammed earth, acid-rain durability of composite materials, and environmental effects on geopolymer concretes. In addition, he is preparing works on crack simulation, nano-engineered materials, and deep-learning-based crack classification, highlighting his expanding focus on intelligent infrastructure systems. As a research assistant at Sharif University of Technology, Javid has developed high-accuracy predictive models using CatBoost, gradient boosting, and novel optimization algorithms, achieving R² values up to 0.99 across various structural datasets. His work consistently emphasizes societal needs such as sustainability, material efficiency, and resilience under extreme conditions.

Profile: Google Scholar
Publications

Javid, A., & Toufigh, V. (2024). Utilizing ensemble machine learning and gray wolf optimization to predict the compressive strength of silica fume mixtures. Structural Concrete, 25(5), 4048–4074.

Javid, A., Javid, E., & Toufigh, V. (2025). High-temperature bond strength evaluation of concrete overlays with industrial by-products: Experimental and analytical approaches using machine learning. Engineering Applications of Artificial Intelligence, 153, 110954.

Lotfalipour, F., Javid, A., & Toufigh, V. (2025). Boosting algorithms for predicting the bond properties of timber and fiber reinforced polymer (FRP) under thermal cycling using single-lap shear tests. European Journal of Wood and Wood Products, 83(2), 83.

Mohsennia, E., Javid, A., & Toufigh, V. (2025). Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products. Case Studies in Construction Materials, e04801.

Javid, A., Kamali, H., & Toufigh, V. (2025). Compressive strength prediction of fiber-reinforced concrete under varied temperature conditions using machine learning. Construction and Building Materials, 504, 144648.

Wei Zhang | Structural Health Monitoring | Breakthrough Research Award

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Prof. Dr. Wei Zhang | Structural Health Monitoring | Breakthrough Research Award

Doctor of Engineering | Civil Aviation University of China | China

Prof. Dr. Wei Zhang is a distinguished Professor and Doctoral Supervisor at the Civil Aviation University of China, specializing in civil and aeronautical engineering with extensive contributions to aviation safety, intelligent systems, and mechanical dynamics. His prolific research record includes 68 publications in high-impact journals such as Robotics and Autonomous Systems, Measurement Science and Technology, and Nonlinear Dynamics, alongside 17 authorized patents that advance aircraft towing, taxiing, and vibration isolation technologies. With over 10 major national and industrial projects—including NSFC-Civil Aviation Joint Fund and COMAC collaborations—his work bridges theoretical mechanics with real-world aviation applications. His citation portfolio reflects strong global recognition, as evident from numerous indexed publications in SCI and Scopus databases. Wei Zhang’s expertise extends to AI-driven robotics and smart airport systems, and his leadership roles include Deputy Director positions in the National Engineering Research Center for Airport Ground Support Equipment and the Key Laboratory of Civil Aviation Smart Airport Theory and Systems. His research portfolio demonstrates a deep commitment to innovation in automated air cargo handling, semi-autonomous aircraft control, and energy-efficient aviation mechanisms, establishing him as a key contributor to China’s advancement in intelligent civil aviation engineering and global aeronautical safety research.

Profile: Scopus | Google Scholar | ORCID
Fearuted Publications:

Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems. (2025). Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering. 1 Citation.

Similarity modeling method for coupling vibration system with energy-regenerative suspension. (2025). Jixie Kexue Yu Jishu Mechanical Science and Technology for Aerospace Engineering.

A novel attitude-variable high acceleration motion planning method for the pallet-type airport baggage handling robot. (2025). Machines.

Robust adaptive cascade trajectory tracking control for an aircraft towing and taxiing system. (2025). Actuators.

Adaptive coordinated control for an under-actuated airplane–tractor system with parameter uncertainties. (2025). Engineering Science and Technology: An International Journal. 1 Citation.

Adrien Gallet – Structural Engineering – Best Researcher Award

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Dr. Adrien Gallet - Structural Engineering - Best Researcher Award

Computational Structural Engineering | Unipart Construction Technologies | United Kingdom

Adrien Gallet is a trilingual doctoral researcher in structural engineering with strong expertise in parametric modelling, Python programming, and structural design, currently pursuing a PhD at the University of Sheffield. Research focuses on machine-learned structural design models from the inverse problem perspective, producing multiple journal articles and data repository contributions. Professional experience spans academia and industry, including doctoral research and teaching roles at Sheffield, a design engineering placement at AKT II in London contributing to Google’s KGX1 office project, consulting work at BE Design Partnership on warehouse projects, and contracting engineering internship at Max Boegl. Research achievements involve the development of physics-informed neural network training pipelines, Grasshopper support scripts, and optimisation programs in Python and MATLAB, reflecting a strong integration of engineering and computational methods. Recognition includes prestigious awards such as the Outstanding Teaching Delivery Award, IStructE Young Researcher Conference Poster Award, Royal Academy of Engineering Scholarship, and multiple academic prizes from the University of Sheffield, demonstrating consistent academic excellence and leadership potential. Extracurricular activities highlight involvement in orienteering, long-distance running, and fencing, alongside leadership in founding the USIS Trading Division, encouraging financial market exposure for students. Technical proficiency covers advanced software tools like Rhino/Grasshopper, Karamba3D, Robot, Peregrine, and AutoCAD, combined with coding expertise in Python and MATLAB. Fluent in English, German, and French, Adrien demonstrates strong international and collaborative potential. A balance between research, teaching, engineering practice, and extracurricular engagement reflects adaptability, innovation, and leadership in both academic and professional settings, positioning Adrien as a highly capable researcher whose work advances the integration of computational intelligence with structural engineering, while maintaining strong interdisciplinary and practical contributions to the field.

Profile: Scopus | ORCID
Publications

  • Zhuang, B., Gallet, A., & Smyl, D. (2025). Inverse structural design with generative and probabilistic autoencoders and diffusion models. Engineering Applications of Artificial Intelligence.

  • Smyl, D., Zhuang, B., Rigby, S., Bruun, E., Jones, B., Kastner, P., Tien, I., & Gallet, A. (2025). OpenPyStruct: Open-source toolkit for machine learning-driven structural optimization. Engineering Structures.

  • Gallet, A., Liew, A., Hajirasouliha, I., & Smyl, D. (2024). Influence zones of continuous beam systems. Structures.

  • Gallet, A., Smyl, D. (2024). IZ kmax: Influence zone results and design datasets. Dataset.

  • Gallet, A., Liew, A., Hajirasouliha, I., & Smyl, D. (2024). Machine learning for structural design models of continuous beam systems via influence zones. Inverse Problems.