Optimizing tailings dam safety: integrating human factors and evolving hazards


Furthermore the goal of the approach was to provide simple, deconvoluted probabilities estimates of single “basic events/situations” and not “failure mode” range, to avoid confusing the users. The approach also had to stay as close as possible to “standard engineering” understanding of the very convoluted issues that may arise in dams quantitative risk assessment, as it will be discussed in the following sections.


2.2. Past different than future In addition, it was understood that using databases without any checks would likely lead to neglect three important points, namely that:


• the quality of dams, especially the most modern “big” ones has improved over time;


• over time ancillary water management facilities may have deteriorated, as maintenance and design deviations may have developed;


• climate change will make the statement “past different from future” even more true than it is today[12] .


2.3. Human factors do not show up in databases Furthermore, it must be considered that traditional databases were not prepared with a “human factor” focus, whereas, based on numerous examples of catastrophic failures we were inclined to think that these were paramount to explain why a world portfolio of dams generally designed with the same FoS generated enough failures to stir public opinion (and regulatory/ political) outcry. That is why we started looking at the human factors first, followed by all the other hazards, root causes and elements/sub elements discussions.


3. Failure Chain Process Driven by Human Causes Dams failure processes, which included “soft” root causes were studied[13]


. A systemic approach of the


“failure chain process” of tailings dams using a probabilistic causality analysis based on publicly available incident and accidents data from the last 60 years was presented after considering valid the particular use of the database made as it looked at very specific aspects of the failure process. Citing directly[13]


: “The


predictive model, geared toward filling the gap between common practice and “path to zero failures” requirements (as “requested” in the aftermath of major failures), accommodates data-mining analytics. The model “constructs” the probability of failure of a dam which is consistent with factual historical world-data. The causality of various factors entering in the dam’s service life can then be individually discussed with a sensitivity analysis.” Then the 2016 paper[13]


showed where and how mitigative actions


can benefit the most with a practical example. Attention was focused on Common Cause Failure (CCF) in operations, risk assessment, peer reviewing and inspections of tailings dams during the service life of dams. Investigations, Design and Construction extended to Management, Monitoring & Water Balance control of the dam (shortened to e-IDC in[13]


) were analyzed with a probabilistic causality analysis based on publicly


available incident and accidents data from the last hundred years. It was noted that a significant number of risk studies do not start with the considered tailings system definition, its functional analysis, and they oftentimes confuse hazards, risks and consequences[14]


leading to misleading results. For instance it was noted: “it is for example rather common to see “insufficient FoS” considered as a hazard (or a risk), whereas Vol XXXIII Issue 3 | Dam Engineering | 183


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