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Ali Lashgari
Ali Lashgari
Marie Curie Postdoctoral Fellow,
Department of the Built Environment,
Aalborg University,
Denmark
Visiting researcher,
Norwegian Geotechnical Institute (NGI), Oslo,
Norway
Former Visiting researcher,
Department of Civil and Environmental Engineering,
Hong Kong University of Science and Technology,
Hong Kong
Emails: alil@build.aau.dk & alashgari@ust.hk
About Me
About Me
Biography:
Biography:
I am a Marie Skłodowska-Curie Postdoctoral Fellow at Aalborg University and Visiting Researcher at the Norwegian Geotechnical Institute (NGI), working at the forefront of geotechnical engineering, geohazards, and offshore energy infrastructure resilience. My research bridges geotechnical mechanics and performance-based engineering to improve the reliability of infrastructure exposed to geohazards, including seismic and climate-induced hazards. I have several years of university-level teaching experience as a visiting lecturer. Moreover, I have contributed to several research projects and collaborate closely with both academia and industry to translate advanced geotechnical modelling into practical risk-informed solutions.
I was a Visiting Researcher at the Hong Kong University of Science and Technology (HKUST), expanding my work on centrifuge modeling of offshore foundations and geotechnical reliability in complex loading environments. I am a member of the Geotechnical Extreme Events Reconnaissance Association (GEER) and the Deep Foundations Institute (DFI). Alongside my academic career, I co-founded Intelligent Environmental Risks Analyzers Ltd., a science-driven company focused on predictive modelling of natural hazards and risk-informed decision tools.
Research interest:
Research interest:
My research lies at the intersection of:
Geotechnical uncertainty and reliability-based design
Seismic and climate-induced geohazards
Performance-based assessment of geosystems and offshore energy infrastructure
Probabilistic modelling and fragility analysis
Data-driven and physics-informed approaches in geotechnics
I develop advanced and modified sliding block methodologies (including CSSR-based approaches) for predicting seismic slope displacements and ground failure. My work integrates large-scale landslide datasets with probabilistic and physics-based frameworks to derive predictive models and fragility functions applicable across diverse tectonic and climatic settings.
More recently, my research focuses on offshore wind foundations, monopile performance, submarine slope instability, and climate change-induced landslide hazards with particular emphasis on uncertainty quantification and risk-informed design frameworks for the energy sector.
By combining mechanics-based modelling, centrifuge testing, and reliability analysis, I aim to advance next-generation geotechnical design methodologies that are robust, transparent, and aligned with sustainability and infrastructure resilience goals.
Professional Affiliation:
Professional Affiliation:
My current activity
My current activity
This project has received funding from the European Union under the Marie Skłodowska-Curie Postdoctoral Fellowships grant agreement No 101106129.
PRO-SLIDE
PRO-SLIDE
The offshore energy infrastructure (OEI)-soil interaction has been studied by numerical analyses in the literature. However, these studies are rarely focused on the performance of OEIs during the seismic submarine landslide (SSL). Moreover, the pseudo-static approach employed for stability assessment of OEI is generally conservative for deep water conditions. The dynamic approach needs a wide range of input parameters and spends a lot of time and money on analyses. Accordingly, predictive models are needed to fill the gap between these approaches for a simple prediction of OEI deformations during SSL. The overall objective of PRO-SLIDE is to develop a reliable and low computational cost solution for simplified resilience assessment of OEIs damage due to SSLs. Answering this concern is not trivial, but it can guide future analyses and contribute to gaining insights from a calibration effort using numerical models that will be undertaken using a benchmark centrifuge model that can successfully capture key mechanisms and features as well as trends. The findings will be based on a database produced via advanced numerical analyses of the OEI-SSL mechanism using the DEM-FEM, a catalog of near-fault ground motions, machine learning technique, and probabilistic/fragility analyses.
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