Lyceum 2021 | Вместе в завтрашний день

Знание условий геологической среды имеет решающее значение для успешного геотехнического анализа и проектирования.

Все же геомеханические свойства по своей природе изменчивы, и их трудно узнать, что приводит к неопределенности. Как же тогда мы сможем правильно понять геотехнический риск? На этом занятии представлено средство для характеристики риска как функции взаимосвязи между неопределенностями — представлены разделы для набора инструментов, доступных специалистам в области геонаук и инженерной геологии для устранения неопределенности в оценке рисков, и предложена структура, которая сопоставляет инструменты с характером риска в целях улучшения результатов этой оценки.

Обзор

спикеров

Рэй Йост (Ray Yost)
Главный инженер‑геомеханик, Advisian

КРИС КЕЛЛН (CHRIS KELLN)
Директор по геотехническому анализу, GeoStudio — Seequent

Продолжительность

30 минут

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Расшифровка видеозаписи

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<encoded_tag_open />v -<encoded_tag_closed />Hello and welcome to this presentation<encoded_tag_open />/v<encoded_tag_closed />
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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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<encoded_tag_open />v -<encoded_tag_closed />Thank you, Chris.<encoded_tag_open />/v<encoded_tag_closed />
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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.
[00:15:19.460] 
A lot of times frequency or recurrence interval
[00:15:23.320] 
might fall into these types of deductive reasoning tools.
[00:15:27.220] 
We have a bunch of data from a time history,
[00:15:29.455] 
and we’re going to extrapolate that out a little bit
[00:15:31.867] 
and pretty much this is what we can assume
[00:15:33.930] 
about the circumstance from the information we have.
[00:15:37.780] 
We could also assume some cases, just the minimum value.
[00:15:40.790] 
If we know that the range is bound in a certain way,
[00:15:45.940] 
it starts at zero, it goes to a hundred,
[00:15:48.120] 
maybe we pick one or the other
[00:15:49.340] 
as far as an upper or lower bound to what that would be.
[00:15:53.590] 
So these things as well,
[00:15:54.730] 
I’m going to argue, fall on a continuum.
[00:15:56.850] 
There’s not any necessarily hard lines between them but—
[00:16:01.940] 
Next slide, please.
[00:16:02.990] 
We will talk about the relative strengths
[00:16:06.140] 
and weaknesses of them.
[00:16:07.510] 
And again, not meant to be an exhaustive list
[00:16:09.780] 
just to illustrate that each of these has a place and a use
[00:16:14.480] 
in terms of addressing uncertainty.
[00:16:17.250] 
With inference and inductive reasoning,
[00:16:20.370] 
a lot of times we’re using a lot of our knowledge
[00:16:23.610] 
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.
<wpml_invalid_tag original=»PHA+» />[00:24:35.258] 
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Понимание геотехнических рисков: Структура для неопределенности

Знание условий геологической среды имеет решающее значение для успешного геотехнического анализа и проектирования.

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