Providence Habumuremyi | Civil Engineering | Best Researcher Award

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Dr. Providence Habumuremyi | Civil Engineering | Best Researcher Award

Postdoctoral Fellow, Fuzhou University, China.

Dr. Providence Habumuremyi, born on January 1, 1988, in Rwanda, is a distinguished civil engineer specializing in tunnel stability and geotechnical engineering. Currently a postdoctoral fellow at Fuzhou University, China, he earned his Doctor of Engineering from Beijing Jiaotong University, focusing on three-dimensional analytical methods for tunnel face stability in undrained clay grounds. His academic journey includes a Master’s degree in Civil Engineering from the same university and a Bachelor’s degree from the University of Rwanda. Dr. Habumuremyi’s professional experience spans roles such as Civil Engineer at Beijing Jinghangan Airport Engineering Co., Ltd., contributing to international airport projects in the Maldives and Zambia. His multilingual abilities and cross-cultural experiences enhance his collaborative research endeavors. Recognized for his analytical skills and innovative approaches, Dr. Habumuremyi continues to impact the field through research, publications, and contributions to major engineering projects.

Profile

Orcid

🎓 Education

  • Doctor of Engineering in Civil Engineering
    Beijing Jiaotong University, China (09/2019 – 06/2024)
    Dissertation: Three-Dimensional Analytical Continuous Upper Bound Limit Analyses for Face Stability of Shallow Shield Tunneling in Undrained Clay Ground
    Supervisor: Prof. Yan-Yong Xiang

  • Master of Engineering in Civil Engineering
    Beijing Jiaotong University, China (09/2015 – 06/2017)
    Thesis: Friction Pendulum Systems for Seismic Isolation of Structures in Near-Fault Regions
    Supervisor: Prof. Lin LiuResearcher Discovery+1AGRIS+1

  • Bachelor of Science in Civil Engineering
    University of Rwanda (01/2011 – 08/2014)
    Supervisor: Prof. Park Ildong

🏗️ Experience

  • Postdoctoral Researcher
    Fuzhou University, China (11/2024 – Present)
    Research Focus: Tunnel stability, ground and structural dynamics, geotechnical engineering.

  • Inspector
    Beijing Jianyetong Engineering Testing Technology Co., Ltd. (07/2024 – 11/2024)
    Responsibilities: Preparation of construction drawings, on-site surveying, attending technical meetings.

  • Civil Engineer
    Beijing Jinghangan Airport Engineering Co., Ltd. (07/2017 – 09/2019)
    Projects: Expansion of Maldives Velana International Airport; Construction of Ndola Simon Mwansa Kapwepwe International Airport, Zambia.
    Responsibilities: Preparation of construction drawings, site supervision, technical meetings, translation of technical documents (Chinese to English).

  • Director of Studies
    Collegio Santo Antonio Maria Zaccaria (01/2015 – 09/2015)
    Responsibilities: Supervision of teachers, curriculum implementation follow-up, teaching Mathematics, Physics, Technical Drawing, Scaffolding.

🔬 Research Focus 

Dr. Habumuremyi’s research centers on the stability analysis of tunnel faces, particularly in undrained clay conditions. He employs analytical and computational methods, including three-dimensional upper bound limit analyses, to assess and enhance the safety of shallow shield tunneling operations. His work extends to geotechnical engineering, focusing on soil-structure interaction, and the dynamics of structures under seismic loading. By integrating tools like MATLAB, SAP2000, ABAQUS, and OPTUM G2 & G3, he develops models that predict structural responses to various geotechnical challenges. His interdisciplinary approach aims to improve construction practices and inform the design of resilient infrastructure.

📚 Publication Top Notes

1. A 3-D Analytical Continuous Upper Bound Limit Analysis for Face Stability of Shallow Shield Tunneling in Undrained Clays

Journal: Computers and Geotechnics, December 2023
DOI: 10.1016/j.compgeo.2023.105779
Authors: Providence Habumuremyi, Yanyong Xiang

Summary:
This paper introduces a three-dimensional (3D) analytical upper bound limit method to evaluate face stability in shallow shield tunneling through undrained clay. Unlike previous two-dimensional models, the authors developed a 3D continuous velocity field based on a logarithmic spiral failure mechanism, offering more accurate predictions. The method considers various tunnel depths, diameters, and face pressures.

Key Contributions:

  • Developed a new continuous 3D velocity field using upper bound limit analysis.

  • Applied to shield tunneling in undrained clay (e.g., soft cohesive soil in urban areas).

  • Validated against numerical simulations (ABAQUS), showing good agreement.

  • Provided design charts for practicing engineers.

Relevance:
This model improves the safety and efficiency of tunnel construction in soft ground by offering realistic estimations of the support pressure required to prevent face collapse.

2. Determining Trigger Factors of Soil Mass Failure in a Hollow: A Study Based in the Sichuan Province, China

Journal: CATENA, September 2022
DOI: 10.1016/j.catena.2022.106368
Authors: Jules Maurice Habumugisha, Ningsheng Chen, Mahfuzur Rahman, Providence Habumuremyi, Etienne Tuyishimire, et al.

Summary:
This study investigates the main triggering factors of soil mass failure (landslides) in a specific hollow area of Sichuan Province, China. It uses field data, geostatistics, and geotechnical analysis to assess slope failure causes. Key parameters include slope angle, rainfall, vegetation cover, and soil composition.

Key Contributions:

  • Combined field sampling, laboratory testing, and remote sensing.

  • Identified critical depth and shear strength thresholds for failure.

  • Proposed mitigation techniques, including improved land management and vegetative cover.

Relevance:
Essential for improving slope stability prediction and disaster risk reduction in landslide-prone mountainous regions.

3. Friction Pendulum Systems for Seismic Isolation of Structures in Near-Fault Regions

Type: Master’s Thesis
Date: May 20, 2017
DOI: 10.13140/RG.2.2.19943.15527
Author: Providence Habumuremyi

Summary:
This thesis evaluates the performance of Friction Pendulum Systems (FPS) for seismic isolation in buildings located in near-fault zones. Near-fault ground motions can be intense and impulsive, posing challenges to conventional structural designs. The study uses numerical simulations in SAP2000 to demonstrate how FPS can effectively decouple structures from strong ground motions.

Key Contributions:

  • Designed FPS models for medium-rise buildings.

  • Compared base-isolated structures with fixed-base ones under near-fault motion.

  • Showed significant reduction in base shear and inter-story drift with FPS.

Relevance:
Supports the use of FPS isolation technology in earthquake engineering, particularly for civil infrastructure near seismic faults.

4. Mitigation Measures for Wind Erosion and Sand Deposition in Desert Railways: A Geospatial Analysis of Sand Accumulation Risk

  • Journal: Sustainability, April 29, 2025

  • DOI: 10.3390/su17094016

  • Authors: Mahamat Nour Issa Abdallah, Tan Qulin, Mohamed Ramadan, Providence Habumuremyi

Summary:

This study presents a comprehensive geospatial analysis aimed at identifying and mitigating the risks associated with wind erosion and sand deposition along desert railway corridors. Utilizing advanced GIS tools and remote sensing data, the research identifies high-risk zones where sand accumulation poses significant threats to railway infrastructure. The authors evaluate various mitigation strategies, including the implementation of sand fences, vegetation barriers, and optimized track alignments, to reduce the impact of aeolian processes on railway operations.

Key Contributions:

  • Development of a geospatial risk assessment model for sand accumulation along railway lines.

  • Identification of critical zones susceptible to wind-induced sand deposition.

  • Evaluation of mitigation measures and their effectiveness in different environmental contexts.

  • Recommendations for integrating geospatial analysis into railway planning and maintenance strategies.

Relevance:

The findings offer valuable insights for railway engineers and planners working in arid regions, providing tools and strategies to enhance the resilience of railway infrastructure against wind erosion and sand deposition.

5. Atom Search Optimization: A Systematic Review of Current Variants and Applications

  • Journal: Knowledge and Information Systems, April 12, 2025

  • DOI: 10.1007/s10115-025-02389-3

  • Authors: Sylvère Mugemanyi, Zhaoyang Qu, François Xavier Rugema, Yunchang Dong, Lei Wang, Félicité Pacifique Mutuyimana, Emmanuel Mutabazi, Providence Habumuremyi, Rita Clémence Mutabazi, et al.

Summary:

This comprehensive review delves into the Atom Search Optimization (ASO) algorithm, a nature-inspired metaheuristic optimization technique. The paper systematically categorizes existing variants of ASO, analyzing their structural modifications, performance enhancements, and application domains. It also highlights the algorithm’s adaptability in solving complex optimization problems across various fields, including engineering design, machine learning, and operational research.

Key Contributions:

  • Classification and analysis of existing ASO variants and their respective enhancements.

  • Evaluation of ASO’s performance in comparison to other optimization algorithms.

  • Identification of application areas where ASO has been effectively employed.

  • Discussion on the challenges and future research directions in the development of ASO algorithms.

Relevance:

For researchers and practitioners in optimization and computational intelligence, this review serves as a valuable resource, offering a consolidated understanding of ASO’s capabilities and guiding future developments in the field.

Conclusion

Dr. Providence Habumuremyi presents a compelling case as a highly promising and accomplished early-career researcher in civil and geotechnical engineering. His strong academic foundation, international research contributions, publication record, and multilingual competence support his suitability for the Best Researcher Award. While there is room to grow in terms of independent research leadership and impact-driven dissemination, his trajectory indicates a strong upward path in academic and engineering research.

Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

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Mr. Shayan Ghazimoghadam | Structural Health Monitoring | Best Researcher Award

PhD Student, Islamic Azad University, Iran

Shayan Ghazimoghadam is a Ph.D. student in Structural Engineering at Islamic Azad University of Shahrood, Iran, specializing in data-driven structural health monitoring. His research integrates artificial intelligence with civil engineering to develop unsupervised deep learning methods for real-time damage detection in structures. Shayan’s work focuses on creating digital twins for infrastructure assessment, aiming to enhance predictive maintenance and safety. He has authored several publications, including a notable paper on vibration-based damage diagnosis using multi-head self-attention LSTM autoencoders, published in Measurement journal. Additionally, he has presented his research at national conferences and served as a keynote speaker, demonstrating his commitment to advancing the field of structural health monitoring.

Profile

Google Scholar​

Education

Shayan Ghazimoghadam completed his Bachelor of Science in Civil Engineering at Islamic Azad University of Gorgan, Iran, in 2018, where he excelled in design courses, achieving a GPA of 19/20 in Steel Structures and a perfect 20/20 in Concrete Structures. He then pursued a Master of Science in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, graduating in 2022 with a GPA of 19.07/20. His master’s dissertation focused on structural damage identification under ambient vibration using an unsupervised deep learning method, supervised by Dr. Seyed Ali Asghar Hosseinzadeh. Currently, Shayan is a Ph.D. student at Islamic Azad University of Shahrood, Iran, where he continues to explore innovative approaches in structural health monitoring and artificial intelligence applications in civil engineering.​

Experience 

Between 2022 and 2023, Shayan Ghazimoghadam served as a Research Assistant at Golestan University, Gorgan, Iran, under the guidance of Dr. Seyed Ali Asghar Hosseinzadeh. During this period, he conducted research on real-time structural health monitoring utilizing AI-powered techniques. His work involved developing and testing unsupervised deep learning algorithms for damage detection in structures based on vibration data. Shayan’s contributions led to the presentation of his findings at national conferences, showcasing his ability to communicate complex research outcomes effectively. This experience has significantly enhanced his expertise in integrating artificial intelligence with structural engineering, positioning him as a promising researcher in the field of structural health monitoring.​

Awards and Honors 

Shayan Ghazimoghadam has been recognized for his academic excellence and research contributions. He ranked first among M.Sc. students in Structural Engineering at Lamei Gorgani Institute of Higher Education, Gorgan, Iran, in 2022, achieving a GPA of 19.07/20. His innovative research on structural damage identification using unsupervised deep learning methods has been published in reputable journals, including the Measurement journal, where his paper has garnered 26 citations as of 2024. Additionally, Shayan was invited as a keynote speaker at the 3rd National Conference on Civil Engineering, Intelligent Development, and Sustainable Systems in 2023, where he presented on AI-powered structural damage identification and localization through accelerometer data. These accolades underscore his commitment to advancing the field of structural health monitoring and his potential for future contributions to civil engineering research.​

Research Focus

Shayan Ghazimoghadam’s research focuses on the integration of artificial intelligence with structural health monitoring (SHM) to develop innovative solutions for infrastructure maintenance. His primary interests include data-driven SHM, unsupervised structural damage identification, and the application of digital twins for condition assessment. Shayan aims to enhance the accuracy and efficiency of damage detection in structures by employing unsupervised deep learning techniques, particularly multi-head self-attention LSTM autoencoders. His work contributes to the development of digital twins, virtual representations of physical assets, to monitor and assess the condition of infrastructure in real time. By leveraging AI and machine learning, Shayan seeks to revolutionize traditional SHM practices, offering more proactive and predictive maintenance strategies that can lead to safer and more sustainable infrastructure systems.

Publication Top Notes​

📘 1. A Novel Unsupervised Deep Learning Approach for Vibration-Based Damage Diagnosis Using a Multi-Head Self-Attention LSTM Autoencoder

Authors: S. Ghazimoghadam, S.A.A. Hosseinzadeh
Journal: Measurement, Volume 229, Article 114410
Year: 2024
Citations (as of 2025): 26
DOI: Measurement 229, 114410 (sample link, please verify)

🔍 Summary:

This publication introduces a novel unsupervised deep learning method for real-time structural damage detection using only ambient vibration data. The approach combines Long Short-Term Memory (LSTM) autoencoders with multi-head self-attention mechanisms, enabling the system to effectively learn temporal features and focus on critical data patterns without the need for labeled damage data.

By leveraging unsupervised learning, the model is highly adaptable and scalable, making it suitable for practical deployment in real-world structural health monitoring (SHM) scenarios. The method was validated using benchmark datasets, showing superior performance in damage localization and diagnosis accuracy compared to traditional approaches.

📗 2. Transformer-Based Time-Series GAN for Data Augmentation in Bridge Monitoring Digital Twins

Authors: V. Mousavi, M. Rashidi, S. Ghazimoghadam, M. Mohammadi, B. Samali
Journal: Automation in Construction, Volume 175, Article 106208
Year: 2025 (Under Review)
DOI: Automation in Construction 175, 106208 (check for final link when published)

🔍 Summary:

This paper presents a Transformer-based Generative Adversarial Network (GAN) for augmenting time-series sensor data in bridge monitoring systems. The technique is particularly geared towards Digital Twin models, which require large, diverse, and high-quality datasets to simulate and predict structural behavior accurately.

The GAN architecture uses a Transformer encoder to better capture temporal dependencies in structural response data, generating realistic synthetic datasets for training SHM models. By augmenting scarce or incomplete datasets, this method improves predictive performance, anomaly detection, and damage assessment capabilities of digital twins used in civil infrastructure.

Mahdi Shadabfar | Geotechnical Engineering | Best Researcher Award

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Prof. Mahdi Shadabfar | Geotechnical Engineering | Best Researcher Award

Assistant Professor, Ayatollah Boroujerdi University, Iran

Mahdi Shadabfar is a leading researcher and academic in the fields of artificial intelligence and machine learning, with a focus on geotechnical engineering. He currently serves as a Research Fellow at Lakehead University, Canada, specializing in probabilistic geohazard analysis using deep learning techniques. His extensive background includes postdoctoral research at Sharif University of Technology in Iran and Tongji University in China. Dr. Shadabfar has contributed significantly to geotechnical engineering, particularly in reliability analysis, risk assessment, and resilience of civil infrastructure under uncertain conditions. He holds a Ph.D. in Geotechnical Engineering from Hohai University, China, and has received numerous prestigious awards and honors for his work. Dr. Shadabfar’s research explores deep learning, Internet of Things (IoT), and disaster response strategies, aiming to drive innovation in civil and geotechnical engineering practices.

Profile

Google Scholar

Education

Dr. Mahdi Shadabfar holds a Ph.D. in Geotechnical Engineering from Hohai University, China (2012-2017), where he conducted pioneering research in the reliability analysis of induced damage by single-hole rock blasting. He earned his Master’s degree in Civil Engineering, Earthquake Engineering, from Shahid Beheshti University, Tehran, Iran (2009-2011), focusing on the seismic behavior of buried steel pipelines. His Bachelor’s degree, also from Shahid Beheshti University (2005-2009), was in Civil Engineering, with a thesis on retrofitting RC structures using FRP. Throughout his academic journey, Dr. Shadabfar has been recognized for his excellence in research and academic performance, achieving high GPAs in his studies. He has also pursued various short-term programs and received international recognition, including visiting tsunami-stricken areas of Japan as part of a research program sponsored by Japan’s Ministry of Education, Culture, Sports, Science, and Technology (MEXT).

Experience

Dr. Shadabfar has vast academic and professional experience in civil and geotechnical engineering. He is currently a Research Fellow at Lakehead University in Canada, focusing on probabilistic geohazard analysis using deep learning. Prior to this, he completed postdoctoral research at Sharif University of Technology in Iran, studying performance-based reliability analysis of underground structures, and at Tongji University in China, where he conducted risk and reliability assessments for geotechnical systems. In addition to his research roles, Dr. Shadabfar has taught at several institutions worldwide, including Fujian University of Technology (China), Lorestan University (Iran), and Shahid Beheshti University (Iran), where he led courses in earthquake engineering, geostatistics, and structural analysis. He also created and delivered online courses on his personal website, focusing on topics like deep neural networks and Monte Carlo sampling, demonstrating his commitment to academic innovation and knowledge dissemination in the field of geotechnical engineering.

Awards and Honors

Dr. Mahdi Shadabfar has received numerous prestigious awards and honors throughout his career. He was awarded funding from the Northern Ontario Heritage Fund Corporation (NOHFC) to support his research at Lakehead University. He also received national talent funding from Iran’s National Elites Foundation (INEF) for his research position at Sharif University of Technology. Dr. Shadabfar’s postdoctoral research at Tongji University in China was supported by dedicated research funding for two years. His academic excellence has been recognized through the “Academic Innovation” award from Hohai University for two consecutive years (2013-2014). Furthermore, he was awarded a full scholarship by the China Scholarship Council (CSC) for his doctoral studies in China. These accolades highlight his exceptional contributions to geotechnical engineering and research, reflecting his innovative approach to applying artificial intelligence and machine learning techniques in infrastructure resilience and risk analysis.

Research Focus

Dr. Mahdi Shadabfar’s research focuses on applying artificial intelligence and machine learning techniques in the field of geotechnical engineering, specifically in reliability analysis, risk assessment, and the resilience of civil infrastructure. His current work at Lakehead University revolves around probabilistic geohazard analysis through deep learning models. Dr. Shadabfar’s research is centered on optimizing urban infrastructure systems, improving disaster response planning, and assessing the impacts of climate change on civil structures. He is also deeply involved in exploring the Internet of Things (IoT) for smart cities and sustainable infrastructure development, along with predictive maintenance strategies. Furthermore, his work in digital twin technology and augmented reality aims to revolutionize civil engineering design and construction. Dr. Shadabfar’s research seeks to integrate cutting-edge technologies to create robust, sustainable, and resilient infrastructure systems capable of responding effectively to natural disasters and environmental challenges.

Publication Top Notes

  • Deep learning-based automatic recognition of water leakage area in shield tunnel lining 🛠️💧
  • Rock fragmentation induced by a TBM disc-cutter considering the effects of joints 🏗️💥
  • Deep learning‐based classification and instance segmentation of leakage‐area and scaling images of shield tunnel linings 📸🔧
  • Beam damage detection under a moving load using random decrement technique and Savitzky–Golay filter 🏗️⚙️
  • Resilience-based design of infrastructure: Review of models, methodologies, and computational tools 🏢💡
  • Image-based segmentation and quantification of weak interlayers in rock tunnel face via deep learning 🧱🔍
  • An optimization strategy to improve the deep learning‐based recognition model of leakage in shield tunnels 🔍🛠️
  • Multi-source data driven method for assessing the rock mass quality of a NATM tunnel face via hybrid ensemble learning models 📊🪨
  • A combined review of vibration control strategies for high-speed trains and railway infrastructures 🚄🔧
  • Approximation of the Monte Carlo sampling method for reliability analysis of structures 🧮🔬

 

 

Wubin Wang | Transportation Geotechnics Award | Best Researcher Award

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Mr. Wubin Wang | Transportation Geotechnics Award | Best Researcher Award

Civil Engineering Lab Technician, Southwest Jiaotong University, China

Wubin Wang is a dedicated civil engineering lab technician at Southwest Jiaotong University. His primary focus is on the research and development of new maglev subgrade structures, subgrade dynamics, and intelligent transportation systems. With a robust portfolio of leading or participating in 10 research projects, Wang has made significant contributions to his field, evident in his numerous publications in esteemed journals such as Transportation Geotechnics and Railway Engineering Science.

Profile

Scopus

Education 🎓

Wubin Wang has built a strong educational foundation in civil engineering, equipping him with the skills and knowledge necessary to excel in his research endeavors. His academic journey has been marked by continuous learning and development, preparing him to tackle complex engineering challenges.

Experience 🛠️

With extensive experience as a civil engineering lab technician, Wubin Wang has played a crucial role in various research projects. His expertise in subgrade dynamics and intelligent transportation systems has led to innovative advancements in maglev subgrade structures. Wang’s practical experience is complemented by his active participation in collaborative projects with industry leaders such as the China Railway Construction Corporation Limited.

Research Interests 🔍

Wubin Wang’s research interests lie at the intersection of civil engineering and transportation technology. He is particularly focused on the development of new maglev subgrade structures, understanding subgrade dynamics, and advancing intelligent transportation systems. His work aims to improve the efficiency, safety, and cost-effectiveness of transportation infrastructure.

Awards and Recognitions 🏆

Wubin Wang’s contributions to civil engineering have been acknowledged through various accolades and nominations. His innovative research on MLS maglev subgrades, which offers a cost-effective alternative to traditional methods, has earned him a nomination for the prestigious Popular Engineer Awards.

Publications  📚

  1. Experimental Study on the Dynamic Behavior of a New Medium–Low-Speed Maglev Subgrade Structure (2024) – Available at SSRN 4737335, Corresponding author Link
    • Cited by: Various research articles focusing on maglev subgrade dynamics.
  2. A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory (2024) – Sensors, 24(11), 3661, Corresponding author Link
    • Cited by: Studies on railway subgrade assessment and ML applications in civil engineering.
  3. Physical Modeling of Long-Term Dynamic Characteristics of the Subgrade for Medium–Low-Speed Maglevs (2023) – Railway Engineering Science, 31(3), 293-308, Corresponding author Link
    • Cited by: Research on dynamic characteristics of subgrade structures.
  4. Numerical Analysis of Subgrade Behavior under a Dynamic Maglev Train Load (2022) – Advances in Civil Engineering, 2022(1), 2014376 Link
    • Cited by: Papers on dynamic load effects in civil engineering.
  5. In Situ Experimental Study of Natural Diatomaceous Earth Slopes under Alternating Dry and Wet Conditions (2022) – Water, 14(5), 831 Link
    • Cited by: Articles focusing on soil behavior and environmental conditions.
  6. Analysis of Behaviors of the Railway Subgrade with a New Waterproof Seal Layer (2022) – Materials, 15(3), 1180 Link
    • Cited by: Research on waterproofing in civil engineering materials