Learning Outcomes
More and more data is becoming available in the area of civil engineering that engineers need to make sense of and integrate into their design work. Examples of such data sources are ranging from sensor based measurements of infrastructure and buildings, to measurements of the environment (weather, water flows), to openly available geographic data. Lately civil engineers have started to analyse sentiment data users have left on social media platforms such as Facebook or Twitter about their experience while using civil engineered products, while generative AI tools, such as ChatGPT have changed the landscape of information gathering and generation. At the end of this class, students will know about the basics of data engineering analysis - the art of asking the right questions for drawing insights from any of these data- sets to support sustainable civil engineering tasks. Students will also understand the basics of state-of-the-art machine learning methods that are used to build the above summarized AI tools.
After finalizing the module students will be able to apply the most common data mining and deep machine learning methods to data sets from the wider civil engineering field. Students will also have a good knowledge of how to assess the performance and quality of models and how to evaluate their applicability for prediction and sustainable decision making. Students will also develop first thoughts on the ethical ramifications of analyzing data with respect to for example, accounting for minorities that might not be well represented in a data set, but also with respect to potential biases that are introduced by the analysis methods. Above and beyond the Bachelor module that we offer, at the end of this Masters module, students will also be able to design new data-driven value propositions to improve civil engineering decision making or design work with respect to improving the ability of civil engineered products to support social and environmental needs.
