Risk Assessment Should Not Add Complexity
Complexity
In any modeling effort, complexity should exist only because the underlying real-world phenomenon is complex. The RA should not add complexity.
Ironically, a scoring type risk assessment, intended to simplify the modeling of real-world phenomena, actually adds complexity. By converting real-world phenomena into ‘points’ via an assignment protocol, an artificial layer of complexity has been introduced. This is unnecessary.
A robust risk assessment, covering complex scientific elements such as corrosion mechanisms and stress-strain relationships, may require a level of complexity in order to fully represent the associated risk issues. In this case, the complexity reflects the complexity of the science and is appropriate for certain kinds of risk assessment. In contrast, a risk assessment that requires the assignment of scores to various conditions—for example, soil corrosivity, CP effectiveness, etc—and then the assignment of weightings to each, and then the combination of the scores using non-intuitive algorithms, is adding complexity that probably adds no value to the analyses. As a matter of fact, such artificial complexity probably detracts from the accuracy and usability of the risk assessment.
Intelligent Simplication
Intelligent Simplification
The challenge when constructing a risk assessment model is to fully understand the mechanisms at work and then to identify the optimum number of detailed variables for the model’s intended use. This follows the reductionist approach previously discussed—breaking the problem down into pieces for later reassembly into meaningful risk estimates.
We must understand and embrace the complexity in order to achieve the optimum amount of simplification—this is the process of ‘intelligent simplification.’ The best approach is to begin with the robust solution, including all details and all nuances that make up the real-world phenomena. Only then can a shortcut be contemplated. That way, what is sacrificed by the simplification is clear.
Furthermore, the robust solution will be immediately appropriate for many practitioners and eventually appropriate for many more (ie, a desired future level of detail in the risk assessment). Modeling complex phenomena such as AC induced corrosion, vapor cloud explosion potential, fracture mechanics, loads-stresses, and many others, requires numerous inputs and interactions among inputs. Understanding what those inputs are and how they should be used to best model scenarios is the first step. With that understanding, simplifications without excessive loss of accuracy may be possible.
When simplifications are not appropriate, the robust solution should be employed, but perhaps in such a way that it does not interfere with the assessment’s efficiency.
Many processes, originating from sometimes complex scientific principles, are “behind the scenes” in a good risk assessment system. These must be well documented and available, but need not interfere with the casual users of the methodology (everyone does not need to understand the engine in order to benefit from use of the vehicle). An example is the concept of effective wall thickness as a simplification of the complexities involved in estimating stress-carrying capacity of various loads on differing components where defects may be present.
Deciding not to include a detailed variable directly in the risk assessment does not necessarily mean it is ignored. The detail may already be part of an evaluation being conducted elsewhere. For instance, the corrosion department may have a very sophisticated analyses of AC induced corrosion potential. Rather than replicate this analysis in the risk assessment, perhaps only the results need to be migrated into the
risk assessment.
Among all possible variables, choices are required that yield a balance between a comprehensive model and an unwieldy model—inclusion of every possible detail versus loss of important information. Users should be allowed to determine their own optimum level of complexity. Some will choose to capture much detailed information because they already have it available; others will want to get started with a high-level framework. However, by using the same overall risk assessment framework, results
can still be compared: from very detailed approaches to overview approaches.
Figure 1.2 illustrates the use of a ‘short circuit’ pending availability of full soil corrosivity information. A 16 mpy soil corrosivity value is used pending information regarding soil moisture, pH, and contaminant levels which will lead to more accurate soil corrosivity values. Having the details shown, but not populated, in the risk assessment model has advantages. It documents that further analyses is possible, if not warranted, and that the entered value is thought to capture the sub-variables that are not yet known.
Having flexibility in the level of rigor of a risk assessment is a large advantage. While detailed, technically rigorous analyses will always strengthen the assessment, it will not always be warranted. By this we mean, the cost/benefit of the rigor does not always justify the effort. In some instances, this will be a guess—a perceived low-value analysis may actually turn out to be a critical consideration and its absence is lamented.
For instance, discounting the potential for H2 permeation through a steel component’s wall seems reasonable until the rare phenomenon contributes to a failure and prompts regret that it wasn’t previously a consideration.
See also the discussion of Chapter 3 Verification, Calibration, and Validation.