Here follows a collection of introductory topics to explain our concerns about some traditional practices

Technical standards detail risk control processes. These processes and standards require periodic review.

There are specific applications areas where standard suggestions may become misleading
Users interpretations of the standards are to their best knowledge or situation, but this is not always correct
There are competing standards producing different numbers, hence, some of them match up with reality better than others

Define the problem using its physics
Identify a choice of applicable standards
Use critical thinking on top of standard tools
Check with the relevant science
Do sensitivity studies – vary parameters and observe results
List assumptions and limitations
Do these match the risks ?
Refer the Code of Practice ‘Managing risks of plant in the workplace’. In Appendix C it lists common standards for industrial plant design.

To be realistic, we have to build models of equipment load and strength spectra
Realistic in turn means probabilistic, since Load and Strength in operation are both random variables
Ignoring the probability terms compromises the analysis confidence, and even discredits advanced methodologies and tools. Bear in mind, this problem sits with the users, not in the methods.
'Out-of-the-box' thinking is about modelling the 'reality' sufficiently enough to adequately answer your question!

Imagine, we are trying to extract out a single ball from this funnel without as much as looking at it at all
Our chance to take the red ball will be 33% in 1st attempt, 50% in the 2nd and 100% in the last
PoF increases along with the number of attempts, or with time, if chances are taken continuously
Say, the red ball is now one in a million chance. How do you feel about finding it in the 1st attempt? How about the 1001st?
When we operate plant equipment, the balls are dropping out from the bottom of the funnel at a certain rate, continuously.
Moreover, each piece of equipment has its own number of red balls in a funnel, and may even (by design) have a number of different funnels, so it can be continuously replenished by operational damage mechanisms (corrosion, fatigue, major force etc.).
When you consider this, do you still feel confident about using qualitative risk matrices for integrity risk assessments?

In the above topic of ‘probability’ we have identified that real load spectra are random. These spectra in fact result from numerous random loads superimposed over time. An example of a stress spectrum is shown in the picture.
In addition to random loads, material quality and strength also vary in a random manner. One cast part may be stronger than another. One casting batch may be weaker than another.

Microscopic material imperfections are always there to limit equipment resource, e.g. micro-cracks, pores, inclusions, and even grain boundaries grow into cracks with time. Corrosion also starts from the weakest points, such as from coating or oxide layer faults.

Applying safety factors in design is necessary when viewing random load and resistance parameters.
But there is much more value to be gained when safety factors are linked to PoF and life expectancy targets.
Finite Element Analysis (FEA) is an indispensable part for cutting (some) corners of stress assessment.
At QUANTY our philosophy foresees using FEA, together with probabilistic considerations, as a sufficient and robust solution.
Whole Pro FE Analysis. Ask us how to!