Franco Oboni & Cesar Oboni Despite numerous efforts by several institutions and authors[1,2,3,4,5,6] it appears that:


• data are incomplete and present very significant gaps; • records do not deliver enough details to elicit proper taxonomies;


• causality/failure mode classification are inhomogeneous and doubtful to say the least with significant percentages of “unknown” cases.


As a result, when extracting information from these databases there still is a clear hazard of reaching fuzzy


results due to the mixing of different populations of dams and past accidents. On the risk assessment front, practitioners, including the authors of this paper, have dealt with single dams using various approaches going from qualitative to semi-quantitative, semi-empirical and quantitative[7]


. Many of these approaches deal separately for each failure mode (stability, internal erosion,


seismic, etc.) and adopt mono-dimensional consequences evaluations, which are misleading. Indeed, the accidents consequences are the sum of dimensions such as harm to people, environmental damages, business interruption, direct and indirect losses, third parties losses, etc. and neither the worst of one of these dimensions, nor merely harm to people as some guidelines suggest. In 2014 a MSc Thesis at UBC[8] linked various failure modes using a well-known reliability formula known as the series-system equation, which assumes the various failure modes were totally independent from each other, and each failure mode occurrence could be fatal to the structure. Following the same idea the US Federal Energy Regulatory Commission[9]


states that failure modes should be combined by summation of their probabilities, an


approximation of the series-system equation result only valid if the probabilities are all very small (NB: if that is not the case the sum could become greater than one, which is against the basic axiom of probabilities). Only in the last years the idea of portfolio risk assessment has been pushed forward[10]


. In this paper we


describe how we built a general model for TSF risk assessment which allowed to develop a “world model” covering to date more than hundred tailings dams of various types, age and built, which apparently mimics the behavior of the world-wide tailings dams portfolio.


2. Driving Concepts Linking specific types of failures to a potential failure mode or cause taxonomy is challenging because only a few failures might fit into a category of that taxonomy. For example, as demonstrated in[11]


, the “Failure


distribution by cause” shows a small sample size. Similarly, the “Failure distribution by dam height” also has a limited sample size. When we try to analyze correlations, such as “Failure distribution by cause” given a certain dam height, the sample size becomes so small that one ends up with either a single case or no case at all. From a risk point of view, i.e. when consequences of failures are included, risk is constantly evolving in


both directions: probabilities (quality of design, evolving standard of care, lack of maintenance, operations, climate change, etc.) and consequences (demographic pressure, land use, public opinion, legal, etc.).


2.1. Databases may be misleading and give a false sense of security The above always made us uneasy about blindly relying on historical data, but one should take advantage of extant data, after careful considerations of the limitations stated earlier.


182 | Dam Engineering | Vol XXXIII Issue 3


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