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Wael A. Altabey
Alexandria University, Alexandria, Egypt
Prof. Wael A. Altabey is a full professor at department of
Mechanical Engineering, Faculty of Engineering, Alexandria
University, Alexandria, Egypt. Before that he was an associate
professor between Dec. 2018 to Dec. 2024 at school of Engineering
Mechanics, Southeast University, Nanjing, China, and research
associate professor at National and Local Joint Engineering Research
Center for Basalt Fiber Production and Application Technology,
Southeast University, Nanjing, Jiangsu, China, after completing a
postdoctoral research fellowship for two years (2016-2018).
Since 2016 his researches have focused on the utilization of
Artificial Intelligence (AI) based schemes for structural health
monitoring (SHM) and Non-Destructive Testing (NDT) for damage
classification, detection, diagnosis, prediction, dynamic response analysis, digital twins, and Reliability
evaluation in composite, and steel Structures (such as aircraft, wind turbines, pipelines, plates, bridges
and industrial machines)
He has listed in Stanford/Elsevier List of World's Top 2% Scientists for five years starting in
2020, until now. He participated in several research activities, which achieved from Natural Science
Foundation of China (NSFC), Alexandria University Research Support Initiative (Alex-RSI) and private
sectors in China and Egypt. He recorded a performance results in March 2026 as
3,889 Citations at
G.Scholar (h-index 35), and 3,351 Citations at Scopus (h-index 32).
He has authored over 250 refereed papers, including over
150 journal articles; has published
15
scientific books, and 50 book chapters in archival volumes; and has been the guest editor of
15 journal
volumes and proceedings. He has been a member of the scientific committee of numerous conferences
and workshops in the field of artificial intelligence, mechanical, materials, and civil engineering.
Speech title "A Smart and
Sustainable Infrastructure Framework
for Establishing a Resilient
Environment: Strategic Overview" Abstract—Over the past two decades, rapid economic growth and the increasing interconnectivity
driven by globalization have significantly expanded the scale and complexity of infrastructure
systems. As these systems grow, they require more robust integration between their subsystems to
effectively respond to natural and human-induced disasters, regardless of geographic location. This
increased complexity has made infrastructure systems more susceptible to various risks, with
heightened vulnerability to unforeseen events. A resilient infrastructure system is characterized by
its ability to withstand and recover from disruptions whether from technological failures, natural
disasters, or other external shocks. These disruptions, whether long- lasting or temporary, can
change system performance in ways that either enhance, maintain, or degrade it. Typically, extreme
events and disasters tend to intensify the risks faced by systems, reduce their robustness, and have
detrimental effects on both the infrastructure and the communities that depend on it. This paper
introduces a framework for evaluating the infrastructure resilience systems and essential resources,
blending both quantitative and qualitative approaches. The infrastructure resilience is a
multidisciplinary field, encompassing a wide range of studies that aim to improve system robustness.
Properly designed resilient systems are capable of maximizing their performance by effectively
managing limited resources and optimizing energy flow within constrained timeframes. As the scale
of these systems increases, the interdependencies between various components play a crucial role in
determining the most efficient network configurations and long-term performance evolution. By
establishing consistent criteria for evaluating infrastructure network resilience, this paper seeks to
provide a comprehensive methodology for assessing and enhancing the robustness of modern
infrastructure systems. In order to develop a new evaluation methodology for assuring a given
resilient system and satisfaction, a more comprehensive methodology for resilience evaluation is
needed and this review attempts to identify some of these requirements and mandates.
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