Internship | Integrating Machine Learning And Computational Physics To Assess Crack Pattern Similarity In Masonry



Education type

university (wo)


Internship and graduation project

Hours a week

Parttime - 20


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What will you be doing?

Masonry structures constitute the majority of building stock worldwide, and their vulnerability to subsidence-induced cracking is a general reason for concern. To assess the structural integrity of masonry buildings subjected to ground settlements, it is important to identify the most likely cause of damage, which often manifests through a specific crack pattern. While currently the task is exclusively performed by experts, and therefore is lengthy, expensive and often inconsistent, recent advances in neural network techniques have shown a great potential for the development of automated procedures able to classify damage patterns.

In this project, in collaboration with TNO, you will focus on crack pattern similarity assessment, that is an important step towards the longer term goal of automated damage assessment. You will develop an automated procedure for the creation of finite element models of cracked masonry structures. The model outputs will be used to fit deep neural networks for the quantification of similarities between crack patterns. The modelling approach will need to account for the most common damage causes and their relative crack pattern. The approach performance will be evaluated through the assessment of the fitted neural network capability of detecting similar crack patterns from field observations.

The final aim of this thesis is to develop a novel method to fit deep neural networks to outputs of finite element model of masonry structures. This will represent a step towards the creation of a completely automated process for the assessment of masonry structures. The topic is relevant and challenging, and if successful you will have the opportunity to publish a journal paper.

What do we require of you?

A background in finite element modelling and demonstrated coding experience (e.g. Python, Matlab) is required. Knowledge on and experience with deep neural networks is an asset but not required.

What can you expect of your work situation?

TNO is an independent research organization whose expertise and research make an important contribution to the competitiveness of companies and organizations, to the economy and to the quality of society as a whole. Innovation with purpose is what TNO stands for. With 3000 people we develop knowledge not for its own sake but for practical application. To create new products that make life more pleasant and valuable and help companies innovate. To find creative answers to the questions posed by society. We work for a variety of customers: governments, the SME sector, large companies, service providers and non-governmental organizations. Working together on new knowledge, better products and clear recommendations for policy and processes. In everything we do, impact is the key. Our product and process innovations and recommendations are only worth something if our customers can use them to boost their competitiveness.

The thesis will be jointly supervised by TNO and TU Delft (Giorgia Giardina) researchers. You will be working at the Department of Structural Reliability of TNO and at the Department of Geoscience & Engineering of TU Delft with a group of enthusiastic researchers/consultants who are open for discussion and are willing to help out. As an intern you have the possibility to work together with the experts of TNO and get a taste of how it is like to work in an applied research institute.


What can TNO offer you?

You want to work on the precursor of your career; an internship gives you an opportunity to take a good look at your prospective future employer. TNO goes a step further. It’s not just looking that interests us; you and your knowledge are essential to our innovation. That’s why we attach a great deal of value to your personal and professional development. You will, of course, be properly supervised during your internship and be given the scope for you to get the best out of yourself. Naturally, we provide suitable internship compensation.

Has this vacancy sparked your interest?

Then please feel free to apply on this vacancy! For further questions don’t hesitate to contact us.

Due to Covid-19 and the consequent uncertainties and restrictions, students who are not residing in the Netherlands may currently not be able to start an internship or graduation project at TNO.

Contact: Arthur Slobbe
Phone number: +31 (0)88-86 63185

Note that applications via email and third party applications are not taken into consideration.


Apply now



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