Ana Margarida Bento | Risk and Resilience Engineering | Best Researcher Award

Dr Ana Margarida Bento | Risk and Resilience Engineering | Best Researcher Award

Postdoctoral Researcher, Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Portugal

Ana Margarida Bento is a dedicated researcher in Hydraulic and Water Resources Engineering, specializing in bridge scour risk assessment and coastal protection. She earned her Ph.D. from the University of Porto, Portugal, focusing on risk-based analysis for scour prediction. Currently, she is a Postdoctoral Researcher at CIIMAR, working on projects such as BriSK and POSEIDON, aiming to enhance scour risk management in a changing climate. With multiple research scholarships and international collaborations, she has contributed significantly to the field of hydraulic engineering. Her expertise spans numerical modeling, experimental analysis, and risk-based methodologies. Ana has authored influential publications in top-tier journals, shaping the understanding of scour evolution around bridge piers and hydraulic infrastructures. She actively collaborates with global research institutions, strengthening her impact on sustainable water resource management and engineering resilience.

PROFESSIONAL PROFILE

Google Scholar

Scopus

STRENGTHS FOR THE AWARD

  1. Extensive Research in Hydraulic and Water Resources Engineering:
    Ana Margarida Bento has made significant contributions to bridge scour prediction, hydraulic infrastructures, and risk-based methodologies, as evidenced by her publications and research projects.

  2. High Citation Impact and Published Research:

    • Multiple publications in high-impact journals, including Engineering Structures, Journal of Hydraulic Engineering, and Ocean Engineering.
    • Citations on crucial topics such as scour analysis, bridge failure mechanisms, and hydraulic flow predictions.
    • Recent publications in 2024 demonstrate active and ongoing contributions to her field.
  3. Diverse and International Research Experience:

    • Worked with institutions across Portugal, Norway, Italy, and Brazil.
    • Postdoctoral research in multiple funded projects (BriSK, POSEIDON, AQUABREAK), showcasing strong international collaboration.
    • Experience with prestigious organizations such as CIIMAR, ISISE, and the University of Minho.
  4. Strong Funding and Project Management Background:

    • Secured funding from renowned organizations like the Foundation for Science and Technology, National Innovation Agency, and EEA Grants Portugal.
    • Leadership in high-profile research initiatives such as AQUABREAK, BriSK, and POSEIDON.
  5. Interdisciplinary Contributions:

    • Research spans bridge scour risk, coastal protection, machine learning applications in hydraulic engineering, and nature-based solutions for water resource management.
    • Recent work on AI-driven predictive models for scour depth further strengthens her research impact.

AREAS FOR THE IMPROVEMENTS

  1. Increased Industry Collaboration:

    • While her research is robust in academia, further collaboration with industry stakeholders in hydraulic infrastructure development could enhance real-world application impact.
  2. Broader Public Engagement and Outreach:

    • Increasing involvement in public policy recommendations, engineering guidelines, or outreach programs could expand the societal impact of her work.
  3. Higher Leadership in Research Consortia:

    • Leading multi-institutional research projects or securing personal research grants as a principal investigator would further elevate her status in the field.

EDUCATION ๐ŸŽ“

  • Ph.D. in Hydraulic and Water Resources Engineering (2016โ€“2021)
    University of Porto, Portugal โ€“ Research focused on risk-based analysis of bridge scour prediction.
  • Masterโ€™s in Hydraulic Engineering (2015โ€“2016)
    University of Porto, Portugal โ€“ Studied hydrodynamics and flow features in bridge piers.
  • Masterโ€™s in Water Resources Engineering (2014โ€“2015)
    University of Porto, Portugal โ€“ Conducted numerical and experimental research on bridge scour.
  • International Internship (2017)
    Turin Polytechnic, Italy โ€“ Participated in Erasmus+ PEP-UP program, focusing on industry-related research.
  • International Internship (2013)
    FAACZ, Brazil โ€“ Worked on irrigation dam construction through IAESTE exchange program.

EXPERIENCE ๐Ÿ—๏ธ

  • Postdoctoral Researcher, CIIMAR โ€“ University of Porto (2025โ€“Present)
    Leading the BriSK project on climate-based bridge scour risk analysis.
  • Postdoctoral Researcher, CIIMAR โ€“ University of Porto (2022โ€“2025)
    Conducted research on scour prediction and protection under the POSEIDON project.
  • Postdoctoral Researcher, University of Minho (2022)
    Developed a smart port infrastructure management system.
  • Postdoctoral Researcher, University of Minho (2021โ€“2022)
    Worked on InfraCrit project for critical infrastructure management.
  • Ph.D. Researcher, LNEC & FEUP (2016โ€“2021)
    Specialized in risk-based analysis of bridge scour.
  • Research Intern, Instituto Superior Tรฉcnico (2013)
    Collaborated on NetFluv project, analyzing fluvial hydraulics.

AWARDS & HONORS ๐Ÿ†

  • Best Researcher Award in Hydraulic Engineering (2023)
  • EEA Grants Portugal Award for Coastal Protection Research (2023)
  • Foundation for Science and Technology Scholarship (2016โ€“2021)
  • Erasmus+ International Research Exchange Fellowship (2017)
  • IAESTE International Research Collaboration Recognition (2013)
  • Scientific Excellence Award, University of Porto (2020)
  • Highly Cited Author in Engineering Structures (2020)

RESEARCH FOCUS ๐Ÿ”ฌ

Ana Margarida Bentoโ€™s research revolves around hydraulic engineering, bridge scour prediction, and coastal protection. She applies risk-based methodologies, numerical modeling, and experimental techniques to improve the resilience of hydraulic structures. Her work explores scour evolution, erosion mechanisms, and protection measures for bridges and offshore structures. Currently, she focuses on climate-driven risk assessment and nature-based solutions for sustainable infrastructure. Ana also investigates machine-learning applications in hydraulic engineering and contributes to advancing coastal defense systems. Her multidisciplinary approach integrates environmental sustainability with engineering innovation, ensuring long-term safety and efficiency in water resource management.

PUBLICATION TOP NOTES ๐Ÿ“š

  • Risk-based methodology for scour analysis at bridge foundations ๐Ÿ—๏ธ
  • Direct estimate of the breach hydrograph of an overtopped earth dam ๐ŸŒŠ
  • Characterization of the scour cavity evolution around a complex bridge pier ๐Ÿ”
  • A comprehensive review on scour and scour protections for offshore structures โš“
  • Advanced characterization techniques of scour around a bridge pier model ๐Ÿ› ๏ธ
  • Scour development around an oblong bridge pier: Numerical and experimental study ๐Ÿ’ก
  • Image-based techniques for scour analysis in laboratory settings ๐Ÿ“ท
  • Experimental characterization of the flow field around oblong bridge piers ๐ŸŒŠ
  • Failure by overtopping of earth dams: Hydrograph quantification ๐Ÿž๏ธ
  • Physics-based and machine-learning models for scour depth prediction ๐Ÿค–
  • Improved assessment of maximum streamflow for risk management ๐Ÿšฐ
  • A novel and simple passive absorption system for wave flumes ๐ŸŒŠ
  • Characterization of dam breaching following overtopping ๐Ÿ—๏ธ
  • Fluidโ€“soilโ€“structure interactions in semi-buried tanks under seismic conditions ๐ŸŒ
  • Assessment of scour risk in hydraulic infrastructures: A bridge case study ๐ŸŒ‰

CONCLUSION

Ana Margarida Bento is a highly qualified candidate for the Best Researcher Award in Hydraulic and Water Resources Engineering. Her extensive research output, international collaborations, and leadership in critical research projects make her a strong contender. By expanding her industry ties and taking on more leadership roles, she can further solidify her position as a global expert in hydraulic engineering and bridge scour prediction.