1. Prediction of Soil Corrosivity Index: A Bayesian Belief Network Approach Gizachew A. Demissie, PhD student (gizachew.demissie@ubc.ca) Solomon Tesfamariam, Associate Professor Rehan Sadiq, Professor School of Engineering, The University of British Columbia

2. Outlines  Introduction  Soil corrosivity  Methodology  SCI-BBN model  Performance evaluation (sensitivity analyses)  Case study  Summary and conclusion 2 Introduction Soil corrosivity SCI-BBN model Case study Conclusion

3. Introduction Factors contributes to pipe failures:  Physical factors  Pipe diameter, age, length, diameter, etc.  Operational factors  Rate of Maintenance/Rehabilitation/Replacement (M/R/R) programs  Water pressure and velocity  Constriction methods  The quality of the transported water and others  Environmental factors  Soil corrosivity  Extreme events like flood and drought  Climate change (daily, monthly and annual weather changes) 3 Introduction Soil corrosivity SCI-BBN model Case study Conclusion

4. Soil corrosivity Low soil resistivity of soil Lower PH Presence of sulfate-reducing bacteria + sulfates Sulfides content Degree of compaction in soil layer Differential aeration of soil around the pipe Redox potential (fluctuation or oxidation reduction) Soil temperature 4 SCI-BBN model Case study Conclusion Introduction

5. Previous studies: 1. Pont scoring scales  AWWA 10 scoring point methods (AWWA 1999)  25 point scoring method (Spickelmire 2002) 2. Statistical methods 3. Probabilistic methods and others Limitations: Models are only based on measured data Interdependency between soil parameters Not able to incorporate multiple information sources Consider only few soil parameters 5 Introduction Soil corrosivity SCI-BBN model Case study Conclusion

6. Objective of the study Develop a SCI BBN model: Consider inter-dependency of soil parameters in prediction of SCI. Consider different information/data sources e.g “in situ”, laboratory analysed, expert knowledge, recorded data 6 Introduction Soil corrosivity SCI-BBN model Case study Conclusion

7. Soil corrosivity 7 Introduction SCI-BBN model Case study Conclusion  ↑ O xygen content of soil ⇒ ↑ RePo  ↓ R ePo ⇒ Less aeration in a soil porous media  Compacted soil ⇒ Less aeration  ↑ Moisture contents of the soil ⇒ Less aeration of soil porous media ⇒ Less oxygen content ⇒ Less RePo Inter-dependencies between soil parameters:

8. Methodology 8 Introduction SCI-BBN model Case study Conclusion Soil corrosivity Bayesian belief network (BBN) Combines graphical and probabilistic theory Represented by Directed Acyclic Graph (DAG) w/c consists of Nodes and links Nodes represent stochastic variables of interest Links identify direct causal influences between variables The dependencies are quantified through conditional probability tables (CPT).

9. Methodology 9 Introduction SCI-BBN model Case study Conclusion Soil corrosivity  P(H) is a prior probability distribution of hypothesis  P(E) probability the evidence (E) takes place  P(E|H) is the statistical model used to describe the distribution of the data or evidence (E) given the hypothesis (H)  P(H|E) is a posterior probability distribution Bayesian theorem

10. Proposed framework 10 Introduction SCI-BBN model Case study Conclusion Soil corrosivity

11. SCI-BBN model Data processing: 11 Introduction SCI-BBN model Case study Conclusion Soil corrosivity Resistivity measuring points… Calgary WSS

12. SCI-BBN model 12 Introduction SCI-BBN model Case study Conclusion Soil corrosivity BBN-Sensitivity analysis

13. Case study Calgary water supply system (WSS) (www.Calgary.ca)  Consists of over 4650 kms of water mains  Above 2035 kms are metallic Pipe materials (% from metallic pipes) CI (40%) DI (52%) CU and ST (8%) Total count of breaks CI (39.5%) DI (53.5%) CU and ST (7%) Case study 13 Introduction SCI-BBN model Conclusion Soil corrosivity

14. Case study 14 Introduction SCI-BBN model Conclusion Soil corrosivity

15. Summary and conclusion 15 Introduction SCI-BBN model Conclusion Soil corrosivity Case study Conclusion: The model can be used as an aid to identify corrosion sensitive areas and implement the rehabilitation and renewal programs for metallic pipe systems Can be used for pipe and system level decision making Limitations:  The model needs either quality information and/or qualified expertise in the are of soil corrosivity.  Should be validated using recorded breakage rate.

16. Thank you… Acknowledgement NSERC Collaborative Research and Development Grants