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Tree Risk Assessment Qualification (TRAQ) – ISA Best Management Practices


Acer ventura
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Hi

 

In February of this year, 8 year old Bridget Wright tragically died when she was hit by a branch that failed from a tree at her school in Pitt Town, north-west of Sydney, Australia. On the back of that incident the Department of Education in New South Wales produced a directive to inspect trees in all state public schools for safety. In order to meet the criteria of the directive arborists had to be qualified to Level 5, and be a QTRA Registered User or have completed the ISA’s Tree Risk Assessment Qualification (TRAQ); TRAQ is founded on the ISA’s Best Management Practices - Tree Risk Assessment (‘Risk BMP’).

 

I was in Sydney, and other cities in Australia, in March delivering QTRA & VTA workshops, and was asked by a number of TRAQ arborists whether there was scope for QTRA and the Risk BMP to work in tandem. This point has also been raised by some QTRA Registered Users, who are ISA members, and were looking to become ISA TRAQ accredited. During the Australian trip, and by email and phone after it, I’ve been discussing the Risk BMP with arborists who have attended both courses, and going through the Risk BMP publication along with the manual that accompanies the TRAQ training. What I’ve been looking to try and do is see where the common ground is, and what the Risk BMP risk rankings of ‘Extreme, High, Moderate, and Low’ mean in terms of actual risk.

 

By way of background. QTRA quantifies tree risk using Monte Carlo simulations and a manual or software calculator to produce a Risk of Harm as a probability, which is the ‘risk assessment’. The level of risk can then be compared to levels of tolerable and acceptable risk in the Tolerability of Risk (ToR) Framework by the risk owner (client/manager) who makes the ‘risk management’ decision.

 

ToR.jpg.96c1b2af5502a77b053f3142bf0ff819.jpg

 

The QTRA approach to tree risk assessment, including an advisory on risk management, is outlined in the QTRA Practice Note;

 

Quantified Tree Risk Assessment

 

The Risk BMP uses two matrices. A Likelihood of Failure & Likelihood of Impacting the Target Matrix, which produces a ‘Likelihood’ ranking. Then the Likelihood ranking is fed into a Risk Matrix, where ‘Consequences’ are also ranked, to produce a qualitative risk ranking of ‘Extreme, High, Moderate, or Low’, which is the ‘risk assessment’. The client then manages their level of tree risk at Extreme, High, Moderate, or Low depending on their ‘risk tolerance’, and this constitutes the ‘risk management’ decision.

 

Those of you who are unfamiliar with the Risk BMP can read about it in Arborist News here;

 

Risk BMP

 

I’ve reproduced the two Risk BMP matrices below, which I've abbreviated to the 'Likelihood Matrix' and 'Risk Matrix', and coloured the cells to make it easier to identify the cell categories in each matrix.

 

5976691e64294_RiskBMPLikelihoodMatrix.jpg.b5225b8d58f2fc49fdcf5137976a94d0.jpg

 

5976691e65cec_RiskBMPRiskMatrix.jpg.7264943522c6dcd1644fddf0402fd721.jpg

 

What I would like to do, with the help of TRAQ arborists and anyone else who would like to chip in who is familiar with the Risk BMP, is to go through the process of working out where the risks of ‘Extreme, High, Moderate, and Low’ sit in ToR. I’m going to break this up into easy to digest bite-sized pieces and will start the next post by looking at ‘Likelihood of Impacting the Target’ in the first matrix.

 

Cheers

 

Acer ventura

 

 

 

Update - There is now a formatted and referenceable summary of this thread available as a pdf document, which can be downloaded at the first post of the new thread here:

 

http://arbtalk.co.uk/forum/general-chat/88697-tree-risk-assessment-traq-qtra-compatibility-common-ground.html

 

.

Edited by David Humphries
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Likelihood of Impacting the Target

 

5976691e6895e_RiskBMPLikelihoodofImpact.jpg.85d1ffb7da80e49fc93809a4e2b7772b.jpg

 

Quoted text is from the BMP and/or training manual. I’m going to use the symbols < > for less than and greater than respectively, but will include the wording as well because the symbol’s meaning can be difficult to immediately interpret for some.

 

High

“…most likely impact the target”

“Constant occupancy…24hours a day…7 days a week”

A point value. Not a range

High is 1/1.

 

Medium

“as likely to impact the target as not”

Lowest value of the Medium range is 1/2.

Parked car occupancy at 14 hours is described as “frequent – large proportion of a day - ‘Medium’.”

Medium can be greater than or equal to >1/2, and must be less than High <1/1 so the ranges are explicit and don’t overlap.

 

Low

“not likely…will impact the target.”

Highest value of Low scale range has to be less than Medium <1/2; just below the lowest value of Medium so the ranges are explicit and don’t overlap.

Lowest value of Low range is not clear.

 

Very Low

“likelihood…of impacting…is remote”

Highest value of Very Low is not clear.

 

High = 1/1

Medium = Less than <1/1 and greater than or equal >1/2.

Low = Less than <1/2 -

Very Low = Less than <?

 

5976691e6a99d_RiskBMPLikelihoodofImpactProbabilities.jpg.edde07c9ccd240726eed4b976ab3fbab.jpg

 

This is far as I’ve got with Likelihood of Impact. Anyone with suggestions for where ‘Low’ ends and where ‘Very likely’ begins; either as hours/minutes/seconds in a day, a percentage, or a probability?

 

I’ll post the Likelihood of Failure rankings tomorrow.

 

Cheers

 

Acer ventura

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What I would like to do, with the help of TRAQ arborists and anyone else who would like to chip in who is familiar with the Risk BMP, is to go through the process of working out where the risks of ‘Severe, High, Moderate, and Low’ sit in ToR.

 

Serious question... why?

Edited by daltontrees
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Likelihood of Failure

 

Likelihood of failure over a year to allow comparison with annual risk thresholds described in ToR, or other measured annual risks.

 

597669240f9b8_RiskBMPLikelihoodofFailure.jpg.4621fe49dd05a385f69afa46b251834f.jpg

 

Imminent

A no brainer at 1/1 and within a much shorter time frame than one year.

 

Probable

“failure may be expected within the review period.”

Probable range has an upper value of 1/1. Potential conflict of Imminent and Probable ranges not being explicit and overlapping, but Imminent is over a much shorter time frame than one year.

Lower value of Probable has to be greater than >1/2.

 

Possible

“Failure could occur, but is unlikely”

Highest value of Possible range must be 1/2 because the lowest value of Probable is greater than >1/2, so the ranges are explicit and don’t overlap.

Lowest value of Possible not clear.

 

Improbable

“..not likely to fail”

Highest value of Improbable not clear

 

Imminent = 1/1

Probable = 1/1 – Greater than >1/2

Possible = 1/2 - ?

Improbable = <?

 

59766924129a0_RiskBMPLikelihoodofFailureProbabilities.jpg.228e70a5a7144d9c01df5524b22db92a.jpg

 

I can’t find a boundary between Possible and Improbable to define the lowest value of Possible and highest value of Improbable. To me, Possible’s lower value of “unlikely” means the same as Improbable’s “not likely” and I can’t separate the two. Any suggestions? Or comments about the ranges for Likelihood of Failure?

 

I'll put together the outcomes of the Likelihood Matrix tomorrow, and take on any suggestions for Likelihood of Impacting the Target and Likelihood of Failure ranges in the meantime.

 

Cheers

 

Acer ventura

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not wanting to make light:001_smile: but when using TRAQ in my mind it kind of goes it will ,it might, it could, be unlucky if it did.

 

Hi Craig

 

Good to get another take in it from a TRAQ arborist.

 

I don’t think you make light, but highlight an important issue. That being, the lack of clarity that can be inherent with subjective and ambiguous descriptors for input and output ranges with risk matrices. Inherent subjectivity and ambiguity in risk matrices is something the Risk BMP highlights in its Tree Risk Assessment Basics section, where it goes on to stress the importance of clear explanations and significance of the definitions with the ratings for likelihood, consequences, and risk.

 

Another point related to this is there’s some research that even when subjective descriptors are given with clear parameters to reduce the ambiguity, the user’s personal cognitive bias of the word's definition can override how the range is meant to be inputted. To illustrate, when a ‘Probable’ event is defined as being greater than >90%, the user’s own interpretation of what ‘Probable’ means to them can be what they actually apply. So, someone who is risk averse could regard ‘Probable’ as being greater than >50% and apply this definition despite the guidance that 'Probable' means greater than >90%.

 

I understand why you’ve gone with your own words so you can make sense of what you’re making a decision on with Likelihood of Failure, but perhaps you should be careful here. For example, I struggle to see how your ‘Might’ is the same as the Risk BMP’s ‘Probable’. And isn’t ‘Might’ the same as ‘Could’?

 

Cheers

 

Acer ventura

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I'm not sure how it's going to pan out trying to assign any kind of numerical value to these descriptive words but I like the way you are trying to find common ground between both systems.

 

I haven't given this a lot of thought but how about:

 

Imminent = 1/1

Probable = 1/1 – Greater than >1/2

Possible = 1/2 - 1/1000

Improbable = 1/1000 - 1/1000000

 

I'm not very clever (particularly with maths) so hope I haven't contributed utter nonsense!

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Imminent - Fast approaching, about to happen, almost a certainty

 

But is it definite?? 1/1?? nah, not quantifiable imo

 

 

Probable - Likely to happen, odds-on, plausible

 

It either happens, or it doesn't... statistics can be bent to any situation depending on the viewpoint... 50/50 (0.5)

 

 

Possible - able to be done, that may happen, feasible, capable of occurring

 

Again, it either happens, or it doesn't... 50/50 (0.5)

 

 

Improbable - Not likely to happen, unexpected, (didn't know this but this word comes from the Latin "hard to prove" - "in probabilis")

 

This ones' a funny one. Let me give you an example... According to the Second Law of Thermodynamics, it is improbable that when dissolving a teaspoon of sugar into your cup of tea, that by stirring, and stirring, and stirring that the sugar will reform back into crystal granules. Not impossible, but improbable.

 

0.999999999 recurring.

 

 

 

Maths, mmmmmm

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