1. Quantitative Risk Analysis – Fallacy of the Single Number World Tunnel Congress 2015 Dubrovnik Dubrovnik, 27.05.2015 Philip Sander sander@riskcon.at Alfred Mörgeli alfred.moergeli@moergeli.com Technikerstr. 32 6020 Innsbruck Austria www.riskcon.at Rosengartenstr. 28 Schmerikon Switzerland www.moergeli.com John Reilly john@johnreilly.us 1101 Worchester Road Framingham MA 01701 USA www. johnreilly.us

2. Slide 2 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us 1.Uncertainty 2.Probabilistic and Deterministic Approach 3.Examples from Real Projects 4.Summary Overview

3. Slide 3 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Uncertainty – Distinguish Between Basic Elements and Risk  Will always occur (e.g. elements in a cost estimation)  Exact price or time is uncertain Uncertainty in predictions Basic Elements (Cost, Time, etc.) Risk  Has a probability of occurrence  Consequences (costs, time, etc.) are uncertain

4. Slide 4 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Uncertainty in a 14 Day Weather Forecast Example temperatures (German television): Exemplary risk: No construction works below 2°C  Additional probability that risk will occur Increasing deviation Date Munich Temperatures

5. Slide 5 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Information re Deterministic Versus Probabilistic Method in Project Development  large range for large uncertainties  narrower range for smaller uncertainties

6. Slide 6 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Probabilistic and Deterministic Approach

7. Slide 7 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Comparisons of Deterministic and Probabilistic Method Input Single risk Result Overall risk potential Single number: probability of occurrence times impact 50 % X 20k USD = 10k USD  Uncertainty not considered Deterministic MethodProbabilistic Method Distribution: probability of occurrence and several values for the impact (e.g., minimum, most likely, and maximum)  Considers uncertainty 10k USD 20k USD 50k USD 50 % & A simple mathematical addition to give the aggregated consequence for all risks. This results in an expected consequence for the aggregated risks. Simulation methods produce a probability distribution based on thousands of realistic scenarios.

8. Slide 8 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Fallacy of the Deterministic Approach (1) A deterministic method can give  equal weight to risks that have a  low probability of occurrence and high impact and risks that have a  high probability of occurrence and low impact using a  simple multiplication of probability and impact. This approach is incorrect.

9. Slide 9 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Fallacy of the Deterministic Approach (2) - Example Very Likely Likely Possible Unlikely Very Unlikely NegligibleMinorModerateSignificantSevere 1 2 3 4 5 5 4 3 2 1 5 5 Flat tire TBM fire  Give equal weight to completely different scenarios.  By multiplying the two elements of probability and impact, these values are no longer independent.  Loosing the probability information  Loosing the scenario impact information  The actual impact will definitely deviate from the deterministic value (i.e., the mean)  see following example. Example deterministic calculation: TBM fire: (1/500) x 4,000,000 $ = 8,000 $ Tire damage mine dumper: 80% x 10,000 $ = 8,000 $ NPP accident: (1/10,000,000) x 80.000,000,000 $ = 8,000 $

10. Slide 10 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Examples from Real Projects Applying the Probabilistic Method

11. Slide 11 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Examples from Current Projects Koralm Base Tunnel (Southern Austria) With a total length of 32.8 km and a maximum cover of 1.250 m the base tunnel will traverse the Koralpe mountain range. The tunnel system is designed with two single-track tubes (approx. 66-71 m² per tube) and cross drifts at intervals of 500 m. Excavation for the Koralm tunnel is executed by two double shield TBM’s for long distances.

12. Slide 12 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 1: Customized Distribution Function – The Scenario Scenario: A tunnel with 1,000 m of TBM excavation is designed without a final lining as a result of expected favorable geological conditions. However, a final lining may become necessary in some sections if geological conditions turn out to be less favorable. If it will be necessary to excavate 700 m or more with a final lining, final lining will be implemented for the full length of 1,000 m.

13. Slide 13 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 1: Individual Distribution Function – Estimation and Result The quantity is modeled by the individual distribution. The financial impact is modeled by a deterministic value: 2,000 USD

14. Slide 14 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Examples from Current Projects Hydro Electric Power Plant Spullersee (Vorarlberg /Austria) Planned in 3 scenarios 2 surface scenarios 1 subsurface scenario For comparison consider basic costs and risks for each scenario.  Ground risks subsurface scenario  Production outage surface scenario

15. Slide 15 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 2: Event Tree Analysis – Scenario Description Scenario: Access road to the construction site of the reservoir Probability of 40% that the access road will not be permitted (nature reserve)  In this case (risk does occur) there will be 2 alternatives: 1. Extension of the existing public road to the reservoir. Estimated probability for permission only 20% 2. No permission for the public road => new cableway for material transport Most expensive scenario (80%) The whole scenario can be modeled by an event tree.

16. Slide 16 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 2: Event Tree Analysis – The Model 40% 60% 20% 80% 8% 32% 60% Costs for the access road are estimated to be 1,000,000. If there will be no permission, the costs for the access road are saved in a first step. Omitted access road 8% -1,000,000 Extension of public road467,500550,000880,000 Min Most likely Max Omitted access road 32% -1,000,000 Cableway for material transport1,912,5002,250,0002,925,000 Triangle

17. Slide 17 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 2: Event Tree Analysis – The Result Cost bandwidth scenario public road (opportunity) Cost bandwidth scenario cableway for material transport After simulation the result is a probability distribution that displays the overall risk potential. There is a probability of 60% that the risk will not occur (see red distribution function). 8% x (-1,000,000 + 550,000) + 32% x (-1,000,000 + 2,250,000) + 60% x 0 = -36,000 + 400,000 + 0 364,000 will not occur in reality Deterministic Approach:

18. Slide 18 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Example 2: Event Tree Analysis – Risk Administration and Analysis Tool (RIAAT) http://riaat.riskcon.at

19. Slide 19 www.riskcon.at www.moergeli.com Quantitative Risk Analysis – Fallacy of the Single Number www.johnreilly.us Summary  Every cost estimate for future events comes with significant uncertainties.  The probabilistic method delivers comprehensive information range of probable cost probability information specifics of potential risk event  In particular, probabilistic methods support owners and contractors to better understand their risks. allowing contractors to price their work knowing those risks allowing owners to budget accordingly e.g. 80% risk potential coverage