@ARTICLE{Riahi2022-4,
author={M Slimani, T Khatir, S Tiachacht, S Khatir, B Benaissa , M. K. Riahi  and M Abdel Wahab},
title={An efficient damage quantification based on Comprehensive learning Jaya algorithm in steel and composite structures},
journal={Journal Structural Control and Health Monitoring},
year={2022},
volume={x},
number={under review},
doi={https://onlinelibrary.wiley.com/page/journal/15452263/homepage/productinformation.html},
art_number={},
url={https://onlinelibrary.wiley.com/page/journal/15452263/homepage/productinformation.html},
affiliation={Department of Applied Mathematics, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Emirates Nuclear Technology Center, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Department of Nuclear Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Department of Mechanical Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates},
abstract={This paper presents a structural health monitoring method based on the "normalized Modified Cornwell Indicator" (nMCI). An improved damage indicator in laminated composite structures, 2D truss structure, and 3D frame structure, for damage position and magnitude. The performance of the suggested approach is examined using innovative zero-order search algorithms, Namely E-Jaya, and the Comprehensive Learning Jaya Algorithm. Firstly, in cases of damage localization compared to the classical damage identification indicator, then in the study of damage quantification truss bridge and experimental modal analysis of Guangzhou TV Tower (China). The presented techniques are tested for single and multiple damages, including the convergence study and CPU time for better selection of good techniques. The robustness of the method is also examined against measurement uncertainty modeled as different levels of noise.},
author_keywords={},document_type={Article}
}

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An improved damage indicator in laminated composite structures, 2D truss structure, and 3D frame structure, for damage position and magnitude. The performance of the suggested approach is examined using innovative zero-order search algorithms, Namely E-Jaya, and the Comprehensive Learning Jaya Algorithm. Firstly, in cases of damage localization compared to the classical damage identification indicator, then in the study of damage quantification truss bridge and experimental modal analysis of Guangzhou TV Tower (China). The presented techniques are tested for single and multiple damages, including the convergence study and CPU time for better selection of good techniques. The robustness of the method is also examined against measurement uncertainty modeled as different levels of noise.","author_keywords":"","document_type":"Article","bibtex":"@ARTICLE{Riahi2022-4,\nauthor={M Slimani, T Khatir, S Tiachacht, S Khatir, B Benaissa , M. K. Riahi and M Abdel Wahab},\ntitle={An efficient damage quantification based on Comprehensive learning Jaya algorithm in steel and composite structures},\njournal={Journal Structural Control and Health Monitoring},\nyear={2022},\nvolume={x},\nnumber={under review},\ndoi={https://onlinelibrary.wiley.com/page/journal/15452263/homepage/productinformation.html},\nart_number={},\nurl={https://onlinelibrary.wiley.com/page/journal/15452263/homepage/productinformation.html},\naffiliation={Department of Applied Mathematics, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Emirates Nuclear Technology Center, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Department of Nuclear Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates; Department of Mechanical Engineering, Khalifa University, PO Box 127788, Abu Dhabi, United Arab Emirates},\nabstract={This paper presents a structural health monitoring method based on the \"normalized Modified Cornwell Indicator\" (nMCI). An improved damage indicator in laminated composite structures, 2D truss structure, and 3D frame structure, for damage position and magnitude. The performance of the suggested approach is examined using innovative zero-order search algorithms, Namely E-Jaya, and the Comprehensive Learning Jaya Algorithm. Firstly, in cases of damage localization compared to the classical damage identification indicator, then in the study of damage quantification truss bridge and experimental modal analysis of Guangzhou TV Tower (China). The presented techniques are tested for single and multiple damages, including the convergence study and CPU time for better selection of good techniques. 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