Luleå University of Technology

University

www.ltu.se/en/staff/a/amir-garmabakiLuleå, Sweden

1 profile visit

About

WWW.LTU.SE Brief information I am an Associate Professor at the Division of Operation and Maintenance Engineering at Luleå University of Technology (LTU), where I lead various research activities, including Climate adaptation, Climate change mitigation and Circular Economy (CE), for transport infrastructure network. My academic contributions are centered on optimization theory, statistical modelling techniques, machine learning (ML), RAMS engineering, climate adaptation tools, circularity in transport system, and digital transformation.

My last 8 years' research profile is dedicated more toward solving challenges related to transport systems utilizing various tools including statistical and machine learning, optimization toolkits, etc. For instance, one of my most impactful contributions has been in the AdaptUrbanRail project, where I led the development of ML tools to distinguish between climatic and non climatic railway asset failure based on various related parameters. Climate Change Nature Journal selected our paper as a pioneering research breakthrough, showcasing the innovative use of AI/ML in climate adaptation for critical infrastructure. The developed methodology can be customized to various critical infrastructure assets and support infrastructure managers to improve network resiliency.

Data technologies, data ecosystems and cross-linking

Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.(AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use pathsSimulation models and predictive analytics to assess the scalability of circular processes across industries

Other enabling technologies

Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled componentsIndustry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chainsLife cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport

Representatives

Amir Garmabaki

Associate Prof.

Luleå University of Technology

Marketplace (1)

Project cooperation
Circular and Sustainable Transport Infrastructure
Circularity, Life cycle assessment, machine learning, Digital Twin, circular value creation , predictive analytics, diagnostic systems,
- Early idea
- Already defined
- Expertise offered
- Consortium seeks Partners
- Data technologies | Approaches to support SME fully exploit the value of existing CVC-related data
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
- Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)
Author
Amir Garmabaki
Associate Prof. at Luleå University of Technology
Luleå, Sweden