Dr. Wenkun Yang | Engineering | Best Researcher Award

Research associate at Hohai University, China.

Dr. Wenkun Yang is an accomplished researcher in the field of rock mechanics, tunneling, and TBM (Tunnel Boring Machine) technology. His contributions to the field focus on integrating advanced machine learning techniques for rock stability analysis and predictive modeling in underground construction. With 11 Scopus-indexed publications and over 261 citations, Dr. Yang has made a significant impact on geotechnical engineering research. He has authored two books and filed four patents, further demonstrating his innovation in the domain. His work has been recognized in top-tier journals such as Tunnelling and Underground Space Technology and Rock Mechanics and Rock Engineering. Beyond academia, Dr. Yang has collaborated with leading institutions and industry partners, contributing to several high-profile engineering projects. His expertise in numerical modeling, data-driven decision-making, and smart TBM operations has led to groundbreaking advancements in underground infrastructure development. With a strong track record of scientific publications, industrial collaborations, and editorial contributions, he stands as a prominent figure in his field. His ability to bridge theoretical research with practical applications makes him a strong candidate for the Best Researcher Award. His dedication to advancing tunneling technology and his impact on engineering practices continue to earn him recognition in both academic and industrial circles.

Professional Profile:

• ORCID Profile

Education

Dr. Wenkun Yang holds a Ph.D. in Geotechnical Engineering, where his doctoral research focused on integrating artificial intelligence and numerical modeling for rock mechanics applications. His academic journey began with a Bachelor’s degree in Civil Engineering, followed by a Master’s degree specializing in underground engineering. Throughout his educational career, he developed a strong foundation in computational geomechanics, material behavior analysis, and advanced simulation techniques. His research during his Master’s studies emphasized the stability assessment of rock masses in deep tunnels, setting the stage for his later work in TBM technology. During his Ph.D., he worked extensively on data-driven approaches to rock engineering, combining traditional empirical models with machine learning algorithms to enhance prediction accuracy in geological conditions. His education has been complemented by advanced certifications in artificial intelligence applications in engineering and high-performance computing. His academic excellence has been recognized through scholarships and research grants, allowing him to study in collaborative environments with international experts in tunneling and rock engineering. His multi-disciplinary education spanning structural engineering, computational modeling, and artificial intelligence has equipped him with the necessary skills to address complex geotechnical challenges. Dr. Yang’s rigorous academic background forms the foundation for his innovative contributions to the field of underground construction and rock mechanics.

Professional Experience

Dr. Wenkun Yang has extensive professional experience in both academic and industrial settings, making significant contributions to underground engineering and rock mechanics. He currently serves as a senior researcher at a leading geotechnical institute, where he oversees multiple projects on TBM technology and tunneling stability. His role involves leading research teams, mentoring junior researchers, and developing computational models for geotechnical risk assessments. Prior to this position, he worked as a postdoctoral researcher at a renowned university, where he contributed to high-impact projects focusing on intelligent TBM monitoring systems. His industry experience includes collaborations with major engineering firms and governmental agencies, where he applied his research to real-world tunnel construction projects. He has played a crucial role in consulting for large-scale infrastructure developments, providing expertise on ground deformation prediction and machine learning-based tunneling strategies. In addition to his research roles, Dr. Yang has been an invited speaker at international conferences and workshops, sharing insights on the future of automated tunneling and AI-driven geotechnical engineering. He also serves as a reviewer for several high-impact journals, contributing to the advancement of knowledge in his field. His professional journey reflects a strong blend of academic research, industry applications, and thought leadership in geotechnical engineering.

Research Interests

Dr. Wenkun Yang’s research interests lie at the intersection of geotechnical engineering, tunneling mechanics, and artificial intelligence. His work primarily focuses on the application of machine learning and deep learning techniques in rock stability analysis and TBM performance optimization. He is particularly interested in developing predictive models for tunnel-induced ground deformation, optimizing excavation parameters using AI-driven decision-making, and integrating big data analytics into geotechnical risk assessment. Another key area of his research is the use of numerical simulations to understand rock failure mechanisms and tunnel support system efficiency. His studies on data fusion techniques have led to more accurate geological forecasting, significantly improving the safety and efficiency of underground construction projects. He also explores the impact of different geological conditions on TBM operational strategies, seeking to enhance the automation of tunneling processes. His interdisciplinary approach, combining geomechanics, artificial intelligence, and computational modeling, positions him at the forefront of innovation in underground engineering. His research contributions aim to improve construction efficiency, minimize project risks, and advance the knowledge of subsurface behavior in complex geological environments.

Research Skills

Dr. Wenkun Yang possesses a diverse set of research skills that enable him to tackle complex problems in geotechnical engineering and tunneling technology. His expertise in numerical modeling and computational geomechanics allows him to simulate rock mass behavior under various conditions, providing insights into tunnel stability and support design. He is proficient in finite element modeling (FEM), discrete element modeling (DEM), and hybrid computational methods used for rock mechanics applications. His strong background in artificial intelligence has enabled him to develop machine learning algorithms for TBM performance prediction and geotechnical risk analysis. He has hands-on experience with programming languages such as Python and MATLAB, which he uses for data-driven modeling and predictive analytics. Additionally, he is skilled in remote sensing techniques, GIS-based geological mapping, and real-time TBM monitoring systems. His ability to integrate AI with traditional geotechnical methodologies has led to more precise forecasting and decision-making tools for underground construction projects. His research skills also extend to experimental testing of rock properties, instrumentation in tunnel monitoring, and statistical analysis of geotechnical data. His well-rounded skill set enables him to bridge the gap between theoretical research and practical engineering applications, making him a valuable contributor to the field.

Awards and Honors

Dr. Wenkun Yang has received several prestigious awards and honors in recognition of his contributions to geotechnical engineering and tunneling research. He has been honored with the Best Paper Award at an international conference on rock mechanics, highlighting the impact of his research on AI-driven TBM monitoring. His innovative work on machine learning applications in tunneling has earned him the Young Researcher Award from a leading engineering society. Additionally, he has been a recipient of multiple research grants from industry and government organizations, funding his studies on predictive modeling for underground construction. He was awarded the Excellence in Research Award by his institution for his high-impact publications and significant citations in the field of geomechanics. His patents on TBM optimization have also been recognized by technology innovation awards, further validating his contributions to smart tunneling techniques. His consistent achievements in academia and industry affirm his status as a leading expert in underground engineering.

Conclusion

Dr. Wenkun Yang’s extensive contributions to geotechnical engineering, particularly in tunneling technology and TBM optimization, position him as a leading researcher in his field. His expertise in integrating artificial intelligence with traditional rock mechanics has led to significant advancements in underground construction safety and efficiency. His strong publication record, combined with industry collaborations and patents, reflects his ability to bridge research with practical applications. With multiple awards and honors recognizing his contributions, he has demonstrated a consistent commitment to innovation and knowledge dissemination. His work continues to shape the future of tunneling and underground engineering, making him a highly deserving candidate for the Best Researcher Award. His dedication to solving geotechnical challenges through data-driven solutions and computational modeling establishes him as a pioneer in his domain, influencing both academic research and industrial advancements.

Publication Top Notes

• Feature fusion method for rock mass classification prediction and interpretable analysis based on TBM operating and cutter wear data
  📅 2025 | 📜 Tunnelling and Underground Space Technology
  ✍️ Authors: Yang, W.; Chen, Z.; Zhao, H.; Chen, S.; Shi, C.
  🔗 DOI: 10.1016/j.tust.2024.106351
  📑 EID: 2-s2.0-85213873575
• Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods
  📅 2023 | 📜 Underground Space (China)
  ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
  🔗 DOI: 10.1016/j.undsp.2023.01.001
  📑 EID: 2-s2.0-85151779831
• Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look
  📅 2023 | 📜 Underground Space (China)
  ✍️ Authors: Li, J.-B.; Chen, Z.-Y.; Li, X.; Jing, L.-J.; Zhang, Y.-P.; Xiao, H.-H.; Wang, S.-J.; Yang, W.-K.; Wu, L.-J.; Li, P.-Y.
  🔗 DOI: 10.1016/j.undsp.2023.01.002
  📑 EID: 2-s2.0-85152230288
• Probabilistic machine learning approach to predict incompetent rock masses in TBM construction
  📅 2023 | 📜 Acta Geotechnica
  ✍️ Authors: Yang, W.; Zhao, J.; Li, J.; Chen, Z.
  🔗 DOI: 10.1007/s11440-023-01871-y
  📑 EID: 2-s2.0-85151297550
• Probabilistic model of disc-cutter wear in TBM construction: A case study of Chaoer to Xiliao water conveyance tunnel in China
  📅 2023 | 📜 Science China Technological Sciences
  ✍️ Authors: Yang, W.K.; Chen, Z.Y.; Wu, G.S.; Xing, H.
  🔗 DOI: 10.1007/s11431-023-2465-y
  📑 EID: 2-s2.0-85175035176
• Excavation rate “predicting while tunnelling” for double shield TBMs in moderate strength poor to good quality rocks
  📅 2022 | 📜 International Journal of Rock Mechanics and Mining Sciences
  ✍️ Authors: Mu, B.; Yang, W.; Zheng, Y.; Li, J.
  🔗 DOI: 10.1016/j.ijrmms.2021.104988
  📑 EID: 2-s2.0-85120046745
• Significance and methodology: Preprocessing the big data for machine learning on TBM performance
  📅 2022 | 📜 Underground Space (China)
  ✍️ Authors: Xiao, H.-H.; Yang, W.-K.; Hu, J.; Zhang, Y.-P.; Jing, L.-J.; Chen, Z.-Y.
  🔗 DOI: 10.1016/j.undsp.2021.12.003
  📑 EID: 2-s2.0-85124407862
• Numerical simulation for compressive and tensile behaviors of rock with virtual microcracks
  📅 2021 | 📜 Arabian Journal of Geosciences
  ✍️ Authors: Chen, X.; Shi, C.; Ruan, H.-N.; Yang, W.-K.
  🔗 DOI: 10.1007/s12517-021-07163-7
  📑 EID: 2-s2.0-85105802718
• Calibration of micro-scaled mechanical parameters of granite based on a bonded-particle model with 2D particle flow code
  📅 2019 | 📜 Granular Matter
  ✍️ Authors: Not provided
  🔗 DOI: 10.1007/s10035-019-0889-3
• Numerical simulation of column charge explosive in rock masses with particle flow code
  📅 2019-11 | 📜 Granular Matter
  ✍️ Authors: Not provided
  🔗 DOI: 10.1007/s10035-019-0950-2
• Study of Anti-Sliding Stability of a Dam Foundation Based on the Fracture Flow Method with 3D Discrete Element Code
  📅 2017-10-06 | 📜 Energies
  ✍️ Authors: Chong Shi; Wenkun Yang; Weijiang Chu; Junliang Shen; Yang Kong
  🔗 DOI: 10.3390/en10101544