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Dam Monitoring Effects

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Mining Risk Assessment

Here is a case study: A client acquired a mining properties portfolio that includes dams at closed mines. Some of these structures were relatively well documented, while others were not. As a result, the client was facing important decisions, such as a way to enhance knowledge on the structures. The question the client asked us was: “What happens to a particular inactive dam if the level of knowledge we have goes from “no knowledge” to a future “optimum knowledge?” Can you characterize the knowledge/probability of failure relationship?”

ORE2_Tailings and Knowledge Level and Dam Monitoring Effects

In this case, we deployed ORE2_Tailings on the dam. At first, we derived the 30+ KPIs from the existing level of knowledge, using the Factor of Safety (FoS) of 1.5 that the engineers evaluated for the dam. Besides the dam being inactive, we made other assumptions and conditions we summarize below.

We then described the levels of knowledge which dictate the KPIs’ partial and progressive alterations as follows:

·      No knowledge: encompasses only external observable symptoms

·      Poor knowledge: as above, plus assumptions of “good behavior and engineering common practice” of prior owner

·      Today’s knowledge: based on all information available today, including reports, monitoring results and discussions to date

·      Future optimum knowledge: considers FoS be confirmed, and removing geotechnical uncertainties by additional studies

ORE2_Tailings Dam Ratings

ORE2_Tailings allows us to study causality of the potential failure. It also expresses a rating note. The rating note qualifies the overall quality of the structure and its management, maintenance, monitoring, etc. The minimum note is 0 and the maximum note is 10.

Of course, 0 and 10 are theoretical extremes. A site visit and discussions on site generally allows for a note of 1 or slightly above. That would have been the case for this dam if no information had been available. Aerial views (including Google Earth, a historical InSAR analysis, satellite optical imagery and NDVI) also can bring the knowledge level up, as we can use the aerial images to look back in time. IoT, big data and AI may be interesting additions to future knowledge, though dam failures are not frequent enough to allow machine learning.

The graph below shows how the dam rating evolves when knowledge level increases for the specific inactive dam we studied.

 

Dam only rating note

Dams Ratings Change with Knowledge

We can see from the graph that if we had no knowledge on the dam, it would have had a very poor rating note. Poor knowledge would lead to a rating note of 4 to 5. Today, the dam sports a rating note of 7.2, and if the knowledge is further increased, the rating note could go higher than 8.

The drivers of the overall notes vary as follows, based on the assumptions we made for this specific inactive dam:

·      No knowledge: geotechnical/geomechanical gaps

·      Poor knowledge: construction/geomechanical gaps

·      Today’s knowledge: construction/geomechanical gaps

·      Future optimum knowledge: construction

Let’s Consider Various Possible States of the Dam

It is interesting to see the influence of knowledge on the dam as is compared to knowledge of the dam combined with Probable Maximum Flood (PMF) and/or seismic activities. We have selected a few “possible states” for this analysis. They are the following:

·      dam where the ancillary water facilities (PWMs) are designed with a PMF with incredible return (say over 100,000 years).

·      dam with ancillary facilities to protect it, plus a PMF of 1/1,000

·      dam with above PWMs, plus a maximum credible earthquake (MCE) of 1/10,000 leading to FoS=1

Here are the results of the annual probability of failure (PoF/yr) per each possible state, as the knowledge level goes from “no knowledge” to “future optimum knowledge”:

Dam monitoring effects

We see from the graph that both the PMF (1/1,000) and the MCE (1/10,000) become much more relevant with respect to the level of knowledge. As a result, they reduce the effect of knowledge level on the structure itself. This reconfirms how important it is to gain confidence on the return and effects of these phenomena.

As per the estimated PoF/yr PWM, here is a variation as the dam rating note increases:

We see from the graph that both the PMF (1/1,000) and the MCE (1/10,000) become preponderant with respect to the level of kno

Investing in increasing knowledge like monitoring and investigations can, in extreme cases, reduce the PoF by over three orders of magnitude! The specific dam in this case history is evaluated today to stand just below the lower bound of the worldwide benchmark.

Dam Monitoring Effects can be Quantified

A cost-benefit analysis on further investigation based on quantitative risk analyses can lead to better mitigative roadmaps, especially when a dam portfolio is analyzed.

Cost-benefit analysis of this kind can support risk-informed decision-making. They avoid squandering capital expenditures and unsustainable long-term actions. Additionally, they also help justify initiatives to both the board and the public, fostering social licence to operate and corporate social responsibility.