PhD position in dynamic integrated planning of maintenance interventions on roads comprised of assets of multiple types in Zürich

The Chair of Infrastructure Management lead 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 the development of algorithms to enable to the integrated planning of roads comprised of assets of multiple types.

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.

The goal of this PhD is to propose the first information driven and mathematically supported methodology to enable the digitalized integrated planning of interventions on national road networks. The two main contributions will be, 1) the improvement of the ability of asset managers to predict the interventions that are required on their assets, e.g., bridges and road sections, and 2) the improvement the ability of corridor planners to decide the sequence of the interventions can be executed. The former will involve the use of the large and growing amount of information with respect to the condition of the assets, the effectiveness of possible interventions, the costs of these interventions and their associated effects on service, as well as their evolution over time. It will also consider current and future uncertainties, the risks associated with the assets with and without interventions, and the need to periodically deal with external constraints, such as imposed periods of time where interventions are not permitted. The latter will involve the development of a flexible operations research model, using mixed integer programming or heuristics that can deal with changing information and increasing detail over time, as well as hard and soft constraints. A particular challenge will be ensuring both fast and accurate estimates and dealing with information with large variations in accuracy and certainty.

The successful candidate for this position will be expected to work closely with another PhD candidate who will focus on the development of a new digitally supported integrated intervention planning process. The work will require regular interaction with the staff of the Federal Road Authority of Switzerland and an accompanying group of experts.

The successful candidate for this PhD position will have a Master’s degree in civil engineering, geo-spatial engineering, systems engineering or a related field, and will have experience using operations research methods. A good grasp of probability theory, risk assessment, R, python and GIS is beneficial. 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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