Compreensão dos riscos geotécnicos – uma estrutura para incertezas

However, geomechanics properties are inherently variable and difficult to obtain, resulting in uncertainty. How then can we properly understand geotechnical risk? This session presents a means to characterize risk as a function of the relationship between uncertainty – provides divisions to the continuum of tools available to geoscience and geological engineering practitioners to address uncertainty in risk assessment – and proposes a framework that matches tools to risk character in order to improve risk assessment outcomes.

Overview

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(upbeat music)

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<v ->Hello and welcome to this presentation</v>

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on understanding geotechnical risk.

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My name is Chris Kelin,

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I’m the director of Geotechnical Analysis

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for the GeoStudio business unit here at Seequent.

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And I have the pleasure of introducing our speaker,

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Dr. Ray Yost.

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Ray has nearly 20 years of experience

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working in the fields of geology, hydrogeology,

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and geotechnical engineering

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for the civil and mining sectors.

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His career has included tenures

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at Oregon Department of Transportation,

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Rio Tinto minerals, Teck Resources

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and more recently, as a principal geotechnical engineer

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at Advisian.

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In this role,

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Ray serves as a subject matter expert

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for a wide range of engineering applications,

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including underground mining,

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surface mine design, tailing storage facilities,

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geo-hazard management, and much more.

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Today Ray will talk to us

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about a framework for understanding risk

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in geotechnical engineering.

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Ray, over to you.

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<v ->Thank you, Chris.</v>

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So my talk is about understanding geotechnical risk

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and the corresponding uncertainty we often face.

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It’s a structure for understanding uncertainty.

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The next slide, please.

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It starts us with this idea

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that small data sets and the corresponding uncertainty

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that comes with them

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are a common circumstance in geological engineering.

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And by small data sets,

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I mean, either actual the small number of values

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that we might have,

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or small in a sense of a sample to volume ratio.

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We’re trying to characterize a very large volume of ground

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with a very small number of data points.

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And it creates two problems these small data sets

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in understanding risk.

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The first is pretty immediate.

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I mean, we have an analysis to do,

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we only have a few data points to choose from,

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and we have to pick an appropriate point

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that we think represents the ground conditions

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or wherever else we’re trying to characterize.

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The second problem is less immediate,

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but ultimately it’s a lot more important.

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And it’s the focus of this talk really.

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Because in selecting this value,

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what we’re doing is we’re making some assumptions

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about that range of data.

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And that goes into our analysis,

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and that goes into our risk quantification.

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And ultimately that goes to our resource allocation

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that we have for mitigating that risk.

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And we have this now line

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between the inherent uncertainty that we’re dealing with

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from these small datasets,

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all the way to the end,

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where we’re actually allocating resources

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to mitigating that problem.

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So it’s really important to understand

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how we think about uncertainty

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so that when we get to this resource allocation,

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we’re actually applying optimal levels

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of mitigation to a problem.

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Next slide.

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So one of the things

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its not that when we say uncertainty,

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it’s not just this big black box of unknowns, this void.

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One of the advantages we have in geomechanics

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is that a lot of our data sets,

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or rather, the types of data and information we use

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are fairly quantitative.

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And so, because of that,

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we can develop this relationship

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between the little things that we know

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that the small data set that we have,

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and this larger uncertainty

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about what the possible range could be.

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We have this idea that variation

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is the range of what we know, whatever that range is.

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And the uncertainty is what we don’t know.

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And given that it’s quantitative often

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we can have an open door or closed end to that uncertainty.

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A lot of times the minimum value is often zero.

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The other end, it can be open in certain circumstances,

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Q values, compressive strength, things like that.

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But at a certain point, it doesn’t matter anymore.

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Once you get past a certain data point

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or restraint value or whatever,

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it doesn’t matter if it’s 350 MPa or 325 MPa,

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it’s strong enough, basically.

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So when we start to overlay these two,

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we see that this is a useful building block now

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for understanding risk,

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because we have this chunk of certainty or knowns

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in the middle,

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and then a chunk of a uncertainty around the sides.

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Go to the next slide, please.

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It’s a simple diagram,

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and it’s going to be the basis for what I’m talking about

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with respect to risk.

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But I’ve started off right away

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with this very idealistic version of what this looks like.

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I’ve got this range of variation that we know in the middle

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bounded by this equal ranges of uncertainty on either end.

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Chances are going to be a lot better actually

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that there’s an asymmetry involved.

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Either there’s going to be a lot more uncertainty

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on one end or the other.

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And this is going to, again,

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influence how we think about risk

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as a function of uncertainty.

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Now I’ve talked about this being quantitative information.

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So it’s easy to think about this

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in terms of a number line and zero at the left side

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and whatever the maximum value is at the right side.

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And that’s okay to think about it that way.

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Since we’re talking about risk though,

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and sometimes low values can be lower risk

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or high values can be lower risk.

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It’s best not to think about it necessarily as numbers

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just as relative better or worse

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in terms of where this certainty lies.

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There’s also a possibility where it could be gapped.

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We could have some sort of chunk of what we know

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and the uncertainty, another chunk of what we know again,

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and then uncertainty on either side of that.

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For the purposes of this talk

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and just to simplify matters a little bit,

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I’m going treat this as basically a bi-modal variation.

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And we just have the same sort of circumstance.

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There’s a range of certainty that we know about or we know,

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and then a range of uncertainty on either side of it.

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Next slide please.

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So now we want to talk about upside or downside asymmetry.

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So I’ve talked about this idea

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that we can have significantly

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more uncertainty on one side or the other

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of our range of what we know.

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And to do that,

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we want to think about this critical value,

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this concept of a critical value.

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This is the value at which

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if you have an input value,

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you’re going to get an output value.

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And if you put in a lower input value,

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you will get a worse answer.

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Or anything to the left of that will be worse.

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Anything to the right is better.

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So this is the value.

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I mean, probably the easiest way to think about it

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is, say a stability analysis,

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and you need a certain compressive strength

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to produce a factor of safety.

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So if you have a less compressive strength

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or a lower compressive strength,

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you’ll get a worse factor of safety,

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and a higher compressive strength

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is a better factor of safety.

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So it’s this critical value.

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And now we can start to see where does our variation lie

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and versus where does our uncertainty lie

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relative to this critical value?

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So we can have asymmetric downside risk,

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we’re basically what we don’t know makes the problem worse,

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or asymmetric upside risk,

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what we don’t know makes the problem better

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relative to this critical value.

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Next slide, please.

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Now we want to talk about magnitude of uncertainty.

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How big is this range?

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We can have of course,

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significant downside uncertainty

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in the case that I’ve shown.

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There’s a lot of uncertainty below this critical value,

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or we can have minor downside uncertainty.

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There’s just a little bit.

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Again, if we think about

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a lot of different geomechanical data,

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the minimum value is zero.

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So if the far lowest known point is slightly more than zero,

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yeah, there’s some uncertainty,

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maybe there’s a value that would fit into that range,

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but it’s a pretty small range

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between zero and whatever our minimum value is.

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So again, for the purposes of this talk

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and to keep things simple,

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if we have minor downside uncertainty,

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I’m not even going to think about that as uncertainty,

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it’s just treated with extending your variation

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a little bit more.

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Really the purpose of this

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and talking about risk and uncertainty

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is talking about circumstances

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where we have significant

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either downside or upside uncertainty,

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where we have a lot of unknowns on one side or the other

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of that critical value.

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Next slide, please.

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Now, of course,

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there’s a sensitivity too that we have to consider.

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This is how sensitive is the output value

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to a change in the input value.

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We can have circumstances

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where our output is insensitive and reasonably linear

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as we make these changes and gradual changes

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in putting in a higher or lower values

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relative to this critical value,

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we don’t see much change or an answer,

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or we can have very sensitive

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and potentially non-linear answers relative to inputs.

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We can start to see

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that we either get a significant change

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in the slope of that output,

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or we have just a very significant sensitivity

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at the end of the day.

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So either one is a cause for concern in this case.

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Next slide, please.

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And then of course, risk.

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We have all of the different things around the probability

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and the range of inputs,

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and essentially what that value is going to look like.

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And the other half of risk is the of course, consequences.

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And our consequences can like sensitivity

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be low to moderate.

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As we’re changing that input value,

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we don’t really see a change in the consequence that much.

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So say again factor of safety in a stability analysis

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is our example.

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And we’re reducing that input material strength,

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and we’re getting a failure.

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But the size of the failure

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is not really changing,

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the run-out distance isn’t changing.

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We’re not really seeing huge differences

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in the consequences of that,

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even though the factor of safety might be dropping,

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it’s not really having an effect

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on what the impacts of that would be.

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So we can have this lower,

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and again, linear consequences

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as we go down this potential range of uncertainty,

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or we can have very high consequences,

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and even non-linear consequences again.

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Now one note on the consequences,

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we have both downside consequences.

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These are often going to take

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the form of unmitigated liability.

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And why I say liability instead of risk,

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is that it’s sort of the next piece.

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Things could be worse than we assume,

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higher risks, and then these risks

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haven’t been attenuated or mitigated

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because we aren’t aware of them,

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and that’s going to create a liability.

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So that’s the downside consequences,

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this unmitigated liability.

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And the upside consequences are going to be more

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in the form of opportunity costs.

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Essentially we could have had a leaner, meaner construction

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of whatever sort.

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We didn’t have to have a to have slope angle that was that shallow.

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We didn’t have to have an embankment that was that big.

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We dedicated resources to something

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that we didn’t need to necessarily

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to achieve our desired outcome in terms of safety.

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Next slide, please.

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So given this construct with sensitivity,

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greater or lesser than,

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the asymmetry in the outcome upside or downside,

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and then the consequences either higher or lower.

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We have this box of possibilities

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in terms of these risks scenarios now,

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and uncertainty scenarios

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that we’ve got eight different circumstances

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that we can look at

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in terms of all of these different ways

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we can think about risk as a function of uncertainty.

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Next slide, please.

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So we’ve talked about now

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the first two pieces of that flow diagram

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that I showed in the earlier slide

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with uncertainty and assumptions.

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That’s how we start to think about risk.

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Now we talk about the analysis and the risk mitigation,

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and this is through the tools that we use.

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These are all these different tools

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that are available to us as geotechnical engineers

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to address this uncertainty.

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How do we think about uncertainty?

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I won’t say that this is the definitive way

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to think about these tools,

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but I will say that there is a continuum of sorts

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between all these different tools that we have.

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And this slide isn’t meant to capture all the tools

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that are available to us as geotechnical engineers.

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But to talk about them in terms of these broad categories,

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where we have tools

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that are based in inductive reasoning and inference,

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these are things like the first picture

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where we have something about A that we don’t know.

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This could be again, a material strength.

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We know a lot about A, or something about A,

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we tend to know a lot more about B

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including that material strength

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this target thing we want to know.

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And A and B share enough characteristics

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that we can assume that whatever material strength B has,

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A has the same strength or similar strength.

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We have tools that fall into this

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sort of proportional relationship.

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A is somehow relative to B,

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think about, we have a few compressive strength samples

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where we’ve incurred the higher cost

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to do the compressive strength testing

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and a whole lot of point load samples.

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And there’s a proportionality between those two.

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So we can look at the range

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of compressive strength variation

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as a function of the range of point load strength variation.

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There’s a lot more, of course,

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again, these are not meant to be exhaustive lists

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of all these different tools,

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just to get the sense of what an inference

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or inductive reasoning type of tool looks like.

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We have parametric tools, or these are basically

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this one of the kitchen-sink approach.

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We’re throwing a lot of different things

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in sampling from them into a bin.

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This can be a lot of different types of variables.

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And we’re trying to come up

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with some sort of parametric analysis

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based on Monte Carlo, Latin Hypercube sampling, whatever

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that produces this range of outcomes.

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And we can start to look at that range of outcomes

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and make some conclusion from that.

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There’s a lot of things around say subjective probability

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that might fall into this as well.

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A lot of different tools

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where we’re basically just looking at

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what the distributions are

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across a lot of different ranges of our variables.

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And then we have these direct

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or deductive reasoning types of tools

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where we’re just either looking at

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what information we have, this is the variation,

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or maybe we’re extrapolating from something.

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A lot of times frequency or recurrence interval

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might fall into these types of deductive reasoning tools.

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We have a bunch of data from a time history,

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and we’re going to extrapolate that out a little bit

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and pretty much this is what we can assume

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about the circumstance from the information we have.

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We could also assume some cases, just the minimum value.

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If we know that the range is bound in a certain way,

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it starts at zero, it goes to a hundred,

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maybe we pick one or the other

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as far as an upper or lower bound to what that would be.

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So these things as well,

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I’m going to argue, fall on a continuum.

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There’s not any necessarily hard lines between them but–

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Next slide, please.

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We will talk about the relative strengths

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and weaknesses of them.

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And again, not meant to be an exhaustive list

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just to illustrate that each of these has a place and a use

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in terms of addressing uncertainty.

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With inference and inductive reasoning,

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a lot of times we’re using a lot of our knowledge

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and understanding as geotechnical engineers

[00:16:25.590]
to relate one thing to another,

[00:16:27.940]
or use some other bit of data to modify

[00:16:33.330]
or increase the precision of our estimate.

[00:16:35.342]
We could say, if we don’t know a material strength,

[00:16:39.250]
we can assume it’s zero, that’s pretty conservative.

[00:16:42.140]
We don’t want to do that necessarily,

[00:16:43.990]
so we’re using inference

[00:16:46.140]
to increase the precision of that estimate.

[00:16:49.370]
Of course, the weakness of this,

[00:16:50.620]
it it’s really based on knowledge

[00:16:52.380]
from practitioner to practitioner, that can vary.

[00:16:55.340]
I might be really good at estimating material strength

[00:16:57.970]
from all these other materials strengths that I’m aware of,

[00:17:00.640]
the next person has maybe more of a limited expertise

[00:17:05.340]
in that area.

[00:17:06.670]
And you’re going to get very different answers

[00:17:08.190]
from inference and inductive reasoning

[00:17:10.280]
from practitioner to practitioner,

[00:17:12.110]
probably the basis of a lot of arguments

[00:17:13.780]
that we have as geological engineers.

[00:17:18.450]
The direct or deductive reasoning,

[00:17:20.760]
the strength there is that

[00:17:22.230]
since you’re assuming either from what you know,

[00:17:26.250]
or from more importantly from some end value of this,

[00:17:33.420]
you sort of covered all the bases.

[00:17:35.330]
You’re not going to be surprised

[00:17:37.140]
by something that wasn’t captured in your assumption.

[00:17:40.130]
The weakness of course,

[00:17:41.040]
is that these can be fairly conservative estimates.

[00:17:44.730]
With the parametric tools,

[00:17:48.030]
the strength is that it’s actually

[00:17:50.260]
kind of drawing on the strengths

[00:17:51.640]
of both inference and inductive reasoning

[00:17:54.640]
as well as direct and deductive reasoning.

[00:17:57.440]
And so it’s pulling the best of each of those.

[00:18:00.770]
The weakness is that

[00:18:02.230]
this can require considerable time expertise.

[00:18:04.942]
You would have to pull from a lot of different people.

[00:18:07.880]
You’re going to have to deal

[00:18:08.860]
with some of those issues around,

[00:18:10.450]
again, both of the weaknesses of each method.

[00:18:13.760]
The other one that it can cause

[00:18:16.490]
is that you end up with this range of possible outcomes

[00:18:20.180]
that’s going to vary

[00:18:21.900]
from some extreme adverse outcome to extreme good outcome,

[00:18:27.224]
and you’re going to have to make some decisions

[00:18:29.780]
around which one’s going to be the appropriate outcome.

[00:18:32.610]
How do you decide?

[00:18:33.500]
Do you have a cluster of outcomes around a central value?

[00:18:37.490]
And that’s a good thing.

[00:18:38.720]
Or do you have these long tails

[00:18:40.790]
that you have to make some decisions about?

[00:18:43.590]
It can sort of solve some of the problems

[00:18:46.127]
of inference or deductive reasoning on the front end,

[00:18:50.300]
but cause more problems on the backend.

[00:18:53.230]
So no one tool is perfect,

[00:18:55.990]
but they all have their advantages and disadvantages.

[00:18:59.460]
So next slide.

[00:19:02.580]
So now we’ve compartmentalized

[00:19:05.200]
all these different circumstances of uncertainty and risk,

[00:19:10.340]
and now we have the different tools that we apply.

[00:19:12.930]
And we’re going to start talking about

[00:19:15.100]
how each of those tools fits

[00:19:17.030]
each of these different circumstances.

[00:19:19.030]
So we have that box of possibilities

[00:19:21.270]
from the previous slide,

[00:19:22.190]
and we split it,

[00:19:23.340]
and we’re looking at the downside on the left side,

[00:19:26.140]
and the upside on the right side.

[00:19:28.130]
We can start to look at how these tools

[00:19:30.330]
apply to these different circumstances

[00:19:33.140]
as a function of sensitivity and consequence,

[00:19:36.490]
and then upside or downside risk.

[00:19:39.310]
So I’d like to talk about these for a little bit,

[00:19:41.950]
and I’ll start with the downside risk

[00:19:44.160]
in the lower left-hand corner.

[00:19:47.070]
We have a situation where we have pretty low consequences,

[00:19:50.350]
pretty low sensitivity, or in sensitivity,

[00:19:53.720]
it’s downside risk, but essentially you’re not going to,

[00:19:57.380]
because of this insensitivity and lower consequences,

[00:20:02.360]
you can assume fairly extreme values

[00:20:05.530]
without really any cost in terms of allocation of resources.

[00:20:09.220]
So that’s a pretty good place for that tool to sit.

[00:20:12.650]
In this middle band,

[00:20:14.087]
we have either the higher consequences

[00:20:17.043]
with the higher sensitivity,

[00:20:19.105]
(coughing) excuse me.

[00:20:20.765]
Some of these inductive tools are going to be more important

[00:20:24.680]
because now either we have to think about consequences

[00:20:29.690]
or we have to think about that sensitivity.

[00:20:31.330]
We do want a little bit more precision

[00:20:33.394]
in how we approach this.

[00:20:34.890]
We want to be aware of implausible or extreme values

[00:20:40.320]
and how those might affect our answer,

[00:20:42.065]
but we don’t want to let them influence our answer too much

[00:20:46.490]
because they could lead to such an extreme outcome

[00:20:49.250]
in our risk assessment

[00:20:50.410]
that we’re, again, misallocating resources.

[00:20:52.610]
So we want to start using some of these inference tools

[00:20:55.140]
to increase the precision of our input assumptions.

[00:20:59.400]
And then finally, when we get to the upper right side

[00:21:02.180]
on the upper right quadrant on the downside risk,

[00:21:06.698]
it really speaks to,

[00:21:08.150]
we have a lot of sensitivity, high consequences.

[00:21:10.870]
We need to look at this parametric approach

[00:21:12.700]
because we want to capture potentially some relationships

[00:21:15.970]
between different, either within a variable

[00:21:19.410]
or due to non-linear responses,

[00:21:22.330]
or maybe some combination of variables

[00:21:24.315]
that may not be intuitive.

[00:21:26.720]
We really want to see what that full range of outcomes

[00:21:29.180]
looks like.

[00:21:30.770]
So for the upside,

[00:21:33.780]
we have a similar set of different tools

[00:21:36.433]
that are going to be applied to these different compartments,

[00:21:40.340]
but a slight difference.

[00:21:41.602]
If we start in the lower right-hand corner of this time,

[00:21:46.490]
we have low sensitivity, low consequences,

[00:21:49.060]
but because it’s more of a matter of opportunity costs,

[00:21:52.790]
we want to use this parametric approach

[00:21:54.830]
to understand those a little bit better.

[00:21:58.450]
There’s some value in looking at those

[00:22:00.850]
so that we’re understanding

[00:22:01.870]
that we’re again, allocating resources appropriately.

[00:22:05.380]
For these middle two boxes

[00:22:07.263]
where we have the higher consequences, but less sensitivity,

[00:22:11.730]
these again, these indirect and inductive reasoning methods

[00:22:14.950]
are important to increase that precision around our answer.

[00:22:20.800]
But once we get into the lower consequences

[00:22:23.620]
with greater sensitivity,

[00:22:25.360]
the direct and deductive approaches

[00:22:27.320]
are more important to use.

[00:22:30.040]
And of course, when we get up

[00:22:31.130]
into the upper left quadrant there

[00:22:34.370]
with greater sensitivity and higher consequences,

[00:22:37.760]
we want to use those parametric approaches

[00:22:39.616]
again, to understand

[00:22:41.520]
if there’s some sort of non-intuitive outcome

[00:22:44.810]
that we can experience,

[00:22:46.795]
or to look at whether those outcomes are clustered

[00:22:50.400]
around some sort of central value

[00:22:52.100]
or have these longer tails

[00:22:53.610]
that might be important to consider.

[00:22:55.650]
Again, it speaks to

[00:22:57.600]
how do we start to look at the tools

[00:23:00.410]
versus the circumstance

[00:23:01.561]
to properly mitigate risk

[00:23:05.930]
or allocate resources to mitigate risk.

[00:23:08.730]
Next slide, please.

[00:23:12.510]
So where do we come to with all this?

[00:23:15.450]
We can use this relationship between what we do know

[00:23:19.590]
and the range of what we may not know or don’t know

[00:23:23.156]
to think about how to characterize uncertainty

[00:23:26.930]
relative to risk.

[00:23:29.520]
And you can agree or disagree

[00:23:31.330]
with any parts of this discussion

[00:23:34.810]
or any parts of my presentation.

[00:23:38.160]
What it does come down to

[00:23:39.410]
again, is this fundamental idea

[00:23:42.290]
that we can discuss this and go on and on

[00:23:45.540]
and talk about our different approaches and whatnot.

[00:23:50.460]
But we do have to think at the end of the day

[00:23:52.340]
about that allocation of resources.

[00:23:54.800]
And so the purpose of all this

[00:23:56.540]
is just to highlight

[00:23:57.730]
that there is this structure to uncertainty.

[00:24:01.081]
There are impacts that the tools

[00:24:03.940]
that we use as geological engineers have

[00:24:06.160]
to how we think about that.

[00:24:07.750]
And when we start to marry those two

[00:24:10.230]
and look at the circumstances of uncertainty

[00:24:13.140]
and the tools that we have for addressing it,

[00:24:16.790]
we really want to make sure that that’s a good marriage

[00:24:19.900]
in terms of producing this optimal allocation of resources

[00:24:24.970]
at the end of the day.

[00:24:26.520]
And that’s really the message of this entire talk.

[00:24:31.260]
Next slide, please.

[00:24:33.870]
Thank you for your time and attention.

[00:24:35.258]
(upbeat music)