Junting Li | Structural health monitoring | Best Researcher Award

Published on by Global Academic Awards

Dr. Junting Li | Structural health monitoring | Best Researcher Award

lecturer at Shaanxi University of Technology, China

Dr. Junting Li is a lecturer with expertise in civil engineering, focusing on reliability-based life-cycle assessment for bridges, finite element analysis, and structural health monitoring. He holds a Ph.D. from Xi’an University of Architecture & Technology and has worked extensively on magnetic memory techniques for evaluating bridge damage. His research projects are supported by the National Natural Science Foundation of China. Dr. Li’s technical skills include ANSYS, ABAQUS, MATLAB, and Python. 📚🔧 His publications explore fatigue crack growth, corrosion defects, and structural evaluations using innovative non-destructive methods. 📊🛠️

Publication Profile

Scopus

Research Interests 🔍🏗️

Dr. Junting Li’s research focuses on the reliability-based life-cycle assessment of bridges, utilizing finite element analysis (FEA) to predict and evaluate structural integrity. His work in structural health monitoring aims to enhance the longevity and safety of infrastructure, incorporating advanced techniques like Metal Magnetic Memory (MMM) for non-destructive testing of hidden damages. Additionally, Dr. Li investigates crack propagation analysis, contributing to the development of more accurate assessment methods for bridge steel. His innovative approach combines modern computational methods with practical applications to improve the assessment and maintenance of critical civil structures. 🛠️📊

Education 🎓📚

Dr. Junting Li is currently pursuing a Ph.D. in Civil Engineering at Xi’an University of Architecture & Technology, China, with a focus on evaluating fatigue damage and life of bridge steel using Metal Magnetic Memory. His research is supervised by Prof. Sanqing Su. Previously, he completed a Master’s in Civil Engineering at Shenzhen University, where his thesis explored factors influencing shrinkage and cracking of high-strength concrete, under Prof. Weilun Wang. Dr. Li began his academic journey with a Bachelor’s in Civil Engineering from Chang’an University, Xi’an, China, solidifying his expertise in structural engineering. 🏗️🎓

Professional Experience 🛠️🏢

Since September 2024, Dr. Junting Li has been serving as a lecturer at the School of Civil Engineering and Architecture at Shaanxi University of Technology, Hanzhong, China. In this role, he is involved in teaching and conducting research in civil engineering, with a focus on the reliability-based life-cycle assessment of bridges and structural health monitoring. Dr. Li’s expertise in Metal Magnetic Memory and finite element analysis further enhances his contributions to the academic environment. His position allows him to apply his knowledge to bridge the gap between research and practical engineering solutions. 🏫🔧

Research Focus 🧠🔬

Dr. Junting Li’s research primarily centers on bridge health monitoring and structural integrity assessment using innovative techniques like Metal Magnetic Memory (MMM). His work involves evaluating fatigue damage, crack propagation, and life-cycle analysis of materials, especially steel bridges. He employs methods such as finite element analysis, stress ratio impact studies, and residual load-bearing capacity assessments to enhance the durability of civil engineering structures. Dr. Li also focuses on defect detection in ferromagnetic materials and corrosion analysis through advanced MMM techniques, aiming to improve the safety and longevity of infrastructure. 🏗️🔧💡

Publication Top Notes

  • Junting Li, Sanqing Su, Wei Wang, Xinwei Liu, Fuliang Zuo. “Fast reconstruction method for defect profiles of ferromagnetic materials based on metal magnetic memory technique,” Measurement, 2023, 215: 112885. (Cited by: 10+ 📈)

  • Sanqing Su, Junting Li, Wei Wang, Xinwei Liu, Fuliang Zuo, Ruize Deng. “Quantitative study of the defects of ferromagnetic materials using the magnetic charge model and particle swarm optimization algorithm,” Journal of Magnetism and Magnetic Materials, 2022, 564: 170076. (Cited by: 15+ 📈)

  • Sanqing Su, Junting Li, Wei Wang, Xinwei Liu, Fuliang Zuo, Ruize Deng. “Study of fatigue crack growth behavior and reliability update analysis of steel bridges,” China Civil Engineering Journal, 2024, 57(7). (Cited by: 5+ 📈)

  • Sanqing Su, Junting Li, Wei Wang, Xinwei Liu, Fuliang Zuo, Ruize Deng. “Metal magnetic memory characterization of fatigue crack propagation of Q345qD bridge steel under the influence of stress ratio,” Journal of Magnetism and Magnetic Materials, 2024: 171888. (Cited by: 8+ 📈)

  • Weilun Wang, Junting Li, Wenjun Peng. “Effects of water-to-cement ratio, curing method and fiber on the autogenous shrinkage of early-age concrete,” Journal of Ceramic Processing Research, 2019, 20: 77-85. (Cited by: 20+ 📈)

  • Zixuan Li, Zhen Chen, Junting Li, Zhiwen Xu, Weilun Wang. “Research on the Early-Age Cracking of Concrete Added with Magnesium Oxide under a Temperature Stress Test Machine,” Materials, 2023, 17(1): 194. (Cited by: 5+ 📈)

  • Xinwei Liu, Sanqing Su, Wei Wang, Junting Li, Fuling Zuo. “Evaluation of residual load-bearing capacity for corroded steel strands via MMM technique,” Journal of Constructional Steel Research, 2024, 219: 108777. (Cited by: 3+ 📈)

  • Xinwei Liu, Sanqing Su, Wei Wang, Junting Li, Fuliang Zuo, Ruize Deng. “Quantitative method for evaluating corrosion defects and residual bearing capacity of bridge structural steel via MMM technique,” Journal of Magnetism and Magnetic Materials, 2024, 590: 171639. (Cited by: 4+ 📈)

  • Xinwei Liu, Sanqing Su, Wei Wang, Junting Li, Fuliang Zuo. “Quantitative Evaluation of Corrosion Defects on Structural Steel Plates via Metal Magnetic Memory Method,” Research in Nondestructive Evaluation, 2023, 34(5-6): 169-185. (Cited by: 6+ 📈)

  • Sanqing Su, Fuliang Zuo, Wei Wang, Xinwei Liu, Junting Li, Ruize Deng. “Invisible damage identification and danger warning for steel box girders using the metal magnetic memory method,” Structures, 2023, 54: 704-715. (Cited by: 7+ 📈)

Mohtasham Khanahmadi | Structural Health Monitoring | Best Researcher Award

Published on by Global Academic Awards

Mr. Mohtasham Khanahmadi | Structural Health Monitoring | Best Researcher Award

Mr. Mohtasham Khanahmadi, Semnan University, Iran

Mohtasham Khanahmadi is a skilled Civil Engineering Researcher from Iran, specializing in structural health monitoring, damage detection, and signal processing. He holds a Master’s in Structural Engineering from Semnan University and a Bachelor’s from Velayat University. With over five years of research experience, his work focuses on non-destructive evaluation and modal analysis of thin-walled and composite structures. He has published several papers on vibration-based damage localization and wavelet-based feature extraction. Mohtasham is fluent in English and Azerbaijani. 🌍🔧📚📊

Publication Profile

Orcid

Google Scholar

Educational Background

Mohtasham Khanahmadi completed his Bachelor of Science in Civil Engineering from Velayat University, Iranshahr, Iran, from January 21, 2011 to January 27, 2015. During his undergraduate studies, he gained foundational knowledge in civil engineering principles. He then pursued his Master of Science in Civil (Structural) Engineering at Semnan University, Semnan, Iran, from September 23, 2015, to November 26, 2018. His graduate studies further honed his expertise in structural engineering, laying the groundwork for his research in damage detection, structural health monitoring, and signal processing. 🌍🏫🔧

 

Research Focus

Mohtasham Khanahmadi’s research is primarily focused on structural health monitoring, damage detection, and localization techniques for civil engineering structures. His work explores advanced signal processing methodologies for damage detection in thin-walled and composite structures like beams, columns, and sandwich panels. He utilizes vibration-based approaches and wavelet transforms to identify and assess structural damage, including interfacial debonding in concrete-filled steel tubular (CFST) columns. His studies aim to enhance the integrity and performance of structures, contributing significantly to nondestructive evaluation and modal analysis. 🔍🛠️📐

 

Publication Top Notes

  • Vibration-based damage localization in 3D sandwich panels using an irregularity detection index (IDI) based on signal processing 📊, Cited by: 10, Year: 2024
  • Signal processing methodology for detection and localization of damages in columns under the effect of axial load 🛠️, Cited by: 10, Year: 2023
  • Interfacial debonding detection in concrete-filled steel tubular (CFST) columns with modal curvature-based irregularity detection indices 🏗️, Cited by: 7, Year: 2024
  • An effective vibration-based feature extraction method for single and multiple damage localization in thin-walled plates using one-dimensional wavelet transform: A numerical study 📐, Cited by: 3, Year: 2024
  • Vibration-based health monitoring and damage detection in beam-like structures with innovative approaches based on signal processing: A numerical and experimental study 🏢, Cited by: 2, Year: 2024
  • A mode shape sensitivity-based wavelet feature extraction method for interface debonding detection in concrete-filled steel tubes 🛠️,Year: 2025
  • A numerical study on vibration-based interface debonding detection of CFST columns using an effective wavelet-based feature extraction technique 🏗️,Year: 2024