# ORE2 tailings results

Sep 29th, 2021

We use archival information to formulate the ORE2_Tailings™ quality note and causality analysis. This includes site visits (when possible and if agreed), photographic historic documentation. Of course information will evolve in time, as new data may become available. Thus, one can use ORE2 tailings results to decide which aspects of the life of a considered structure to study.

ORE2_Tailings™ results encompass various dam body’s probabilities. They correspond to the FoS yielded by available engineering analyses and the causality analysis. Here again a link between ORE2_Tailings™ and conventional failure modes analyses is possible. If available, the ESA, USA, pseudo static (with a return period) FoSs are transformed into an annualized probability of failure using a proprietary algorithm. Thus:

- static drained conditions,
- liquefaction/residual strength and finally
- rapid drawdown modes of failure

can be simulated. If the client has developed Event trees, Failure trees, Bowties, etc. we will compare the results and discuss potential significant differences.

The ancillary water management (weirs, spillways, decants, etc.) also contribute to the annualized probability of failure by their design and possible deficiencies. This is similar to what an overtopping, scouring, erosion, etc. failure mode analysis would consider.

**Water Management**

Water management is a complex issue. ORE2_Tailings™ deals with it in a systematic manner by looking at the various internal and external elements of the water management subsystem, including external water courses.

Each “built” element is characterized by a:

- design-criteria defined in the project files and
- qualitative state of the system (excellent, fair, poor) also based on known/perceived near-misses.

Their combination leads to the likelihood of seeing a “water management failure” generated by external and/or internal system’s element(s) failures.

The vulnerability of the structure to erosion, in case of overtopping resulting by any combination of the surface water management, leads to modifying the estimated likelihood. This is to cover point c) of the failure definition explained in Section 5.1.

**Combining the probabilities**

Thus, at end of the ORE2_Tailings™ evaluation two families of results are available:

- the probability pf
_{A} of failure of the dam body alone (without considering water management) which is a result of the:
- level of knowledge (uncertainties),
- quality of the structure (since inception to the date of the analysis) AND
- the result of the engineering analyses (stability, seismic, liquefaction, etc.)

- the probability of the water management system failure which we combine with pf
_{A} in order to evaluate the probability pf_{B} of the system “as is”.

The probability pf_{A} is a rather theoretical value. Indeed it would correspond to an ideal state where all water management issues have been mitigated to a probability level lower than the dam itself. Let’s make an example. If a dam body would have a pf_{A}=1.0E-05, one would need to mitigate the water management system to 1.0E-05 or lower for A to be “valid”. As water management generally obey to 1.0E-03 or higher criteria, it becomes self evident that the system “as is” will generally obey to pf_{B}>pf_{A}.

### 6.1 Benchmarking of probabilities of failure (Take away #4)

The probabilities of failure (pf_{A}, pf_{B}) will be displayed together with the world-wide benchmark for catastrophic failures for each dam/slope. Riskope have benchmarked dam hazards since 2013. Hazard benchmarking is based on the probability of failure, disregarding the consequences. Indeed this significantly differs from a full pledged risk prioritization that will be developed in the next posts.

We discussed this theme in detail in a blogpost https://www.riskope.com/2021/06/23/dam-portfolio-ore2_tailings-support-for-decision-makers/ . The title of that post is Dam portfolio ORE2_Tailings™ support for decision makers. There we clearly show that a rating based on probabilities only is not sufficient for rational decision-making.

__Take away #4__: The benchmarking exercise.

6.2 First rating of risks (Take away #5)

We deliver this rating for the sake of completeness. However, it is generally not valid for RIDM. That is because multiplying the probability of failure by the consequences leads to identical values if the:

- probability is very large and the consequences small, or
- vice versa.

That corresponds to neutral risk aversion, but we know this to be wrong for tailings.

This explains why common-practice colored matrices that attribute similar or identical colors to the cells (p_{max}, C_{min} and p_{min}, C_{max}) are misleading. Simultaneously one needs to apply a proper risk tolerance threshold.

__Take away #5____:__ The decreasing risks ranking.

## 7 – RISK TOLERANCE THRESHOLDS

In order to allow meaningful comparisons one needs to maintain constant failure definition, consequence metric and risk tolerance throughout the entire Mine/TSF/Dams portfolio. We can facilitate the client in developing their own risk tolerance threshold. This is normally the object of a separate mandate.

## 8 – RISKS PRIORITIZATION

In this section we generally combine the risks from Chapter 6 with the tolerance example we gave in Chapter 7 to deliver a risk informed prioritization.

__Take away #6____:__ risks ranking in terms of intolerable part.

__Take away #7:__ Risk aggregation and interdependencies for the portfolio.

Tagged with: decision, management, risk, tailings, Tailings Dams

Category: Uncategorized

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