The prediction of the remaining fatigue life of existing rail and road bridges is a relevant task to safely prolong their use. You will develop structural health monitoring methods and probabilistic structural prediction models to estimate the fatigue life. Novel in your approach is that you will do this for an entire structure instead of a single detail.

Job Description

Ample structural health monitoring systems exist that measure relevant data for fatigue deterioration, such as strain, acceleration, crack activity, or crack size. The same applies to structural prediction models. Which system and/or model is optimal depends on the structure. You will develop a framework that allows to estimate the structural reliability and the remaining fatigue life based on various possible combinations of systems and models, combining details and data of different type. Based on probabilistic theory, the framework should be able to select the most suited monitoring and model strategy for a specific bridge:

• Using Bayesian posterior analysis, you will develop an algorithm that updates the reliability and estimates the remaining life span based on model prediction (prior) and measurement data (likelihood), thereby considering spatial correlations between variables. This method gives a joint evaluation of all degradation-sensitive details in an entire structure. This method also includes the probability of a malfunctioning monitoring system (false calls or fallout).
• You will develop a Bayesian prediction algorithm that estimates the potential benefit or added value in terms of updated reliability of each (additional) sensor applied and its location, before any measurement is applied. You will make use of spatial correlations between the uncertain variables to make this possible.

The framework that you build hence enables the optimised selection of sensor types and locations based on a prediction of the added value of each sensor on the structure. Your task requires a translation of loads on the structure to the response of the structure in term of strains or stresses, and a prediction of the deterioration rate. You will build a digital twin for this purpose.

This PhD position is within a wider project carried out by a consortium of different Dutch universities, aiming to develop a digital twinning methodology for macro steel structures. You will closely collaborate with the other researchers from the consortium, who will focus on other aspects of the digital twin.. You will also collaborate with other PhD students at Eindhoven University of Technology that work on fatigue and similar subjects.

You are experienced in the following four fields:

1. Structural reliability (probabilistic, Bayesian) analysis.
2. Finite element modelling (Ansys, Abaqus, or equivalent).
3. Programming (Matlab, Python, C++, Fortran, or equivalent).
4. Fatigue.

Further, you hold or have obtained:

• A master's degree (or an equivalent university degree) in Civil Engineering or Mechanical Engineering.
• A research-oriented attitude.
• Proof that you fluently speak and write English (C1 level).

You have a cooperative attitude. At the same time, you take responsibility for your own work.

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2.541,- max. € 3.247,-).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.