PhD position in data and process analysis to enable the digitalized integrated planning of interventions on road sections comprised of assets of multiple types in Zürich

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100%, Zurich, fixed-term

The Chair of Infrastructure Management held by Professor Dr. Bryan T. Adey in the Institute of Construction and Infrastructure Management of the Department of Civil, Environmental and Geomatic Engineering has an opening for a PhD student focused on data and process analysis to enable the digitalized integrated planning of interventions on road sections comprised of assets of multiple types.

Project background

The future of national roads is one where both human driven and automated vehicles travel seamlessly from their origins to their destinations. Potential disruptions to unhindered travel will be foreseen with the extensive use of surveillance technologies, and either prevented through the implementation of detailed action plans or their consequences eliminated with the seamless rerouting of vehicles along alternate routes to avoid traffic jams with as little as possible lost travel time. Interventions on the infrastructure, whether it be the filling of pavement cracks, or the rehabilitation of bridges, will be grouped together in ways that minimize traffic disruption, while maintaining costs as low as possible.

A new ETH project “MINERVA”, co-funded by the Swiss Federal Road Authority, will develop tools to help enable the realization of the latter part of this future. It will include the development of a new process to enable the integrated planning of maintenance interventions on road sections that are comprised of assets of different types. The process will increase the efficiency and effectiveness of the process through the optimal exploitation of digital tools. This process will harness the power of mathematical models built using state‐of‐the‐art concepts, including

  1. optimisation models including but not limited to mixed integer programming, heuristics, etc. to determine the optimal spatial and temporal work clusters to be performed in the future on assets considering their specific characteristics,
  2. Monte Carlo simulations and failure trees to predict the asset level interventions required in the future, capturing the uncertainty associated with the prediction of the future condition, the likelihood of damage due to potentially disruptive events such as natural hazards, and the behaviour of the asset as a function of its components,
  3. dynamic Bayesian networks to predict the future condition of assets and corresponding levels of uncertainty, and 
  4. multi-variate kriging for roads and structures to ensure accurate estimates of current condition of assets when there are incomplete data sets.

The process will use appropriate data, and include state-of-the-art data analysis and management.

In summary, MINERVA will enable a digital revolution in the integrated planning of maintenance interventions on road sections comprised of assets of multiple types.

Job description

The goals of this PhD are

  • to develop the new digitally supported integrated intervention planning process, taking into consideration the realisation of network development interventions, capacity and operation management considerations, and
  • to develop accurate data driven estimates of the current and future state of signals, bridges, supporting structures, bridges, catenaries, catenary supporting structures and signal boxes, where a possible method for the current state is multivariate Kriging, and a possible method for the future state is dynamic Bayesian networks.

The successful candidate for this position will be expected to work closely with another PhD candidate who will focus on the development of the appropriate algorithms to determine the optimal intervention strategies for each asset type being investigated, and the optimal intervention program. The work will require regular interaction with the staff of the Federal Road Authority of Switzerland and an accompanying group of experts.

Your profile

The successful candidate for this PhD position will have a Master’s degree in data and/or process analysis as well as a keen interesting in the management of road infrastructure, or a Master’s degree in civil/road engineering and a keen interest in data and/or process analysis. It is beneficial if the successful candidate has a good grasp of probability theory, R, python, relational/RDF databases and GIS. The ideal candidate is of mother tongue German with a good knowledge of English. Candidates with other weightings on these two languages will, however, be considered.

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ETH Zurich is a family-friendly employer with excellent working conditions. You can look forward to an exciting working environment, cultural diversity and attractive offers and benefits.

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