Welcome to section 2. In this section, we’ll start by continuing to set the scene for the reliability modelling to come later. In the first three lectures, we’ll discuss common modes of failure, how uncertainty creeps into the design process and consider the slightly more philosophical question; how safe is safe enough?

Although calculating the likelihood of failure is our main focus in this course - once a number is established, we need to take some action. Do we modify the design or accept this risk?!

While we won’t dig into the downstream actions that flow from a reliability analysis, it’s worth highlighting the challenging questions this simple number raises!

In the second half of this section, we get back onto more familiar ground and start to focus on the technical aspects of the challenge ahead. We’ll review the semi-probabilistic approach to design, and in doing so, we’ll also review the fundamental building blocks of probabilistic analysis - the probability density function and cumulative distribution function.

We’ll wrap up this section by jumping into our Python coding environment and reviewing the tools at our disposal that will make working with probability distributions so much more efficient.

When this section is complete, we’ll have covered all of the groundwork necessary to start building our first reliability analysis algorithm in the next section.