J.M. Oliveira dos Santos PhD (Joao)

Born in 1988, João Santos is an Assistant Professor at the department of Construction Management and Engineering at the Faculty of Engineering Technology at the University of Twente (UT) since 2018.

In 2016, João Santos completed the doctoral program in Transportation Systems provided by the Faculty of Science and Technology of the University of Coimbra (FCTUC), the Technical University of Lisbon (UTL), and the Faculty of Engineering of University of OPorto (FEUP), Portugal. During his graduate studies in Transportation Systems at the FCTUC, he spent a year and a half as visiting scholar at the Centre for Sustainable Transportation Infrastructure (CSTI), at the Virginia Tech Transportation Institute (VTTI), USA.

Prior to his position at the UT, he was a recipient of a Marie Skłodowska-Curie Post-Doctoral Fellowship working on an EU Framework 7 funded project SUPR&R ITN (www.superitn.eu), at IFSTTAR in Nantes.

He conducts research at the interface of civil engineering, operation research and environmental technology. His work develops assessment methodologies, frameworks and operation search methods, such as optimization algorithms, to handle from a data-driven and optimized-based perspective the commonly existing trade-off relationships underlying to the application of sustainability, resilience and circularity principles, when planning, designing, maintaining and operating infrastructures, transportation systems and cities.

Engineering & Materials Science

# Asphalt
# Life Cycle
# Pavements
# Recycling

Earth & Environmental Sciences

# Asphalt
# Life Cycle
# Pavement

Business & Economics

# Pavement

Faculty of Engineering Technology (ET), Market Dynamics (MD)

João Santos' research work emphasizes the development and application of advanced computational models, optimization algorithms, statistical machine learning methods, systems analysis tools and industrial ecology methods to tackle multiple and varied real-world problems and global challenges faced by today’s infrastructures, transportation systems and cities in order to promote a more sustainable future.

He is also interested in (1) investigating the impacts of climate change on pavement and urban transport systems and (2) combining machine learning and statistical models to create predictive models intended to improve the knowledge of physical and mechanical properties, characteristics and processes influencing the infrastructure and transportation systems condition and the urban environment, based on the combination of data collected in the field, existing databases and novel sensing platforms. He is particularly thrilled by the prospect of integrating modern systems models with real-world data sources in research that requires carefully applied state-of-the-art operation research and industrial ecology methods along with new theoretical insights.

The common thread that ties his research together is the concept of enhancing the frameworks, platforms and tools that decision-makers use to design and manage their systems. Whether this entails advancing the state of knowledge about (1) the impact of infrastructure, transportation systems and cities on the natural environment and (2) operation research methods or solving applied research questions regarding the enhancement of their management, the outcome is a more informed decision process that decision-makers can use to help promoting a transition towards a more sustainable future. This requires implementing new models and data to solve complex problems related to social, environmental, and economic aspects of engineered systems and make environmentally benign, cost-effective, and socially acceptable policies, all of which drive his long-term research interests.

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