Southeast University, China
Author Profile
Early Academic Pursuits:
Wael Abdelmonem Abdelmonem Altabey commenced his academic journey with a Bachelor's degree in Mechanical Engineering from Alexandria University, Egypt. Subsequently, he pursued higher education, obtaining a Master's degree and Ph.D. in Mechanical Engineering from the same institution. His academic pursuits were marked by outstanding achievements, including a Ph.D. CGPA of 4.000 and numerous research projects focused on structural health monitoring and materials behavior.
Professional Endeavors:
Dr. Altabey's professional career encompasses various roles in academia and research institutes. He has served as an Associate Professor at the International Institute for Urban Systems Engineering (IIUSE) at Southeast University in China and as a Research Associate Professor at the National and Local Joint Engineering Research Center for Basalt Fiber Production and Application Technology. His research endeavors have spanned structural health monitoring, artificial intelligence applications in engineering, and materials science.
Contributions and Research Focus:
Dr. Altabey's research focuses on developing artificial intelligence-based schemes for structural health monitoring and damage detection in composite structures. His work has significant implications for improving the safety and reliability of infrastructure systems. Additionally, he has made substantial contributions to the study of materials behavior, including the fatigue behavior of composite materials under various loading conditions.
Accolades and Recognition:
Throughout his career, Dr. Altabey has received numerous accolades and honors, including being listed in the World's Top 2% Scientists by Stanford University. His research has been widely cited, and he has received funding for several research projects from prestigious organizations such as the National Natural Science Foundation of China and Alexandria University Research Support Initiative.
Impact and Influence:
Dr. Altabey's research has had a significant impact on the field of engineering, particularly in the areas of structural health monitoring and materials science. His innovative work has contributed to advancing knowledge and addressing critical challenges in infrastructure safety and sustainability. Moreover, his leadership roles in academic institutions and research centers underscore his influence in shaping the future of engineering education and research.
Legacy and Future Contributions:
Dr. Altabey's legacy in academia and research is characterized by his dedication to excellence, innovation, and collaboration. His ongoing research projects and collaborations with international institutions indicate his commitment to addressing complex engineering problems and contributing to the advancement of knowledge in his field. As he continues to make strides in his career, Dr. Altabey's impact on engineering research and practice is expected to endure, leaving a lasting legacy for future generations.
A total of 1837 citations for his publications, demonstrating the impact and recognition of his research within the academic community.
- Citations 1837
- h-index 26
- i10-index 69
Notable Publications
Efficiency of grey wolf optimization algorithm for damage detection of skeletal structures via expanded mode shapes
Optimum wavelet selection for nonparametric analysis toward structural health monitoring for processing big data from sensor network: A comparative study
Selecting optimum levels of wavelet multi‐resolution analysis for time‐varying signals in structural health monitoring
Mode shape‐based damage identification for a reinforced concrete beam using wavelet coefficient differences and multiresolution analysis
Comparative analysis of wavelet transform for time-frequency analysis and transient localization in structural health monitoring
Prediction of natural frequency of basalt fiber reinforced polymer (FRP) laminated variable thickness plates with intermediate elastic support using artificial neural networks …
A deep learning based approach for response prediction of beam-like structures
Deep learning-based crack identification for steel pipelines by extracting features from 3d shadow modeling
A comparison of three different methods for the identification of hysterically degrading structures using BWBN model