15.4 Success and Failure Criteria
231
and monitoring malfunctions during its expected life. Slow contaminant releases,
slow/small entity outflows are excluded from this analysis. A truly holistic view
should of course include slow releases, slow/small entity outflows into the environment. However, their inclusion would add complexity to the case study without
adding insightful information on the procedures to be implemented.
15.5 Probability of Failure of the Portfolio’s Dams
In this section we will use a combination of techniques as described in Chaps. 10
and 11, based on availability of data and ease of implementation. As the risk register
of the portfolio should be built “for future evolution”, scalability should be insured.
That means that if data for a given dam receive a complement, then those records
could be easily updated either using Bayesian concepts (see Sect. 10.1.2) or simply
new data inputs.
When the SLM method (Silva et al. 2008), later modified by Altarejos-García
(Altarejos-García et al. 2015) is used, Table 11.4 adapted for each portfolio’s dam is
displayed in the next sections with explicit indication of the category determination.
The thirty diagnostic “points” used in ORE2_Tailings assessments of tailings
dams (see Sects. 11.2.1, 11.2.3 and 14.5) are summarized as follows:
• Physical aspects of the dam and its equipment (weirs, pipes, spigots, penstocks,
etc.);
• Construction: type of materials, cross section, supervision, berms and erosion,
divergence from plans, etc.;
• Geotechnical investigations and testing;
• Prior analyses and documentation of the project;
• Various stability, deformation, erosion, liquefaction aspects:
– stability analyses (of various types, ESA, USA, pseudo-static, etc.)
– instability symptoms
– settlements (actual and analyses)
– liquefaction
– internal erosion
• Operations, monitoring, maintenance and repairs.
Based on the list above, evaluations can be easily updated to take into account
generally available or observable data. Furthermore, the lack of knowledge—i.e.,
uncertainties on data or missing data—is explicitly entered in the evaluations. Explicit
consideration of uncertainties is a fundamental step towards reasonable, transparent
and ethical risk assessments.
231
and monitoring malfunctions during its expected life. Slow contaminant releases,
slow/small entity outflows are excluded from this analysis. A truly holistic view
should of course include slow releases, slow/small entity outflows into the environment. However, their inclusion would add complexity to the case study without
adding insightful information on the procedures to be implemented.
15.5 Probability of Failure of the Portfolio’s Dams
In this section we will use a combination of techniques as described in Chaps. 10
and 11, based on availability of data and ease of implementation. As the risk register
of the portfolio should be built “for future evolution”, scalability should be insured.
That means that if data for a given dam receive a complement, then those records
could be easily updated either using Bayesian concepts (see Sect. 10.1.2) or simply
new data inputs.
When the SLM method (Silva et al. 2008), later modified by Altarejos-García
(Altarejos-García et al. 2015) is used, Table 11.4 adapted for each portfolio’s dam is
displayed in the next sections with explicit indication of the category determination.
The thirty diagnostic “points” used in ORE2_Tailings assessments of tailings
dams (see Sects. 11.2.1, 11.2.3 and 14.5) are summarized as follows:
• Physical aspects of the dam and its equipment (weirs, pipes, spigots, penstocks,
etc.);
• Construction: type of materials, cross section, supervision, berms and erosion,
divergence from plans, etc.;
• Geotechnical investigations and testing;
• Prior analyses and documentation of the project;
• Various stability, deformation, erosion, liquefaction aspects:
– stability analyses (of various types, ESA, USA, pseudo-static, etc.)
– instability symptoms
– settlements (actual and analyses)
– liquefaction
– internal erosion
• Operations, monitoring, maintenance and repairs.
Based on the list above, evaluations can be easily updated to take into account
generally available or observable data. Furthermore, the lack of knowledge—i.e.,
uncertainties on data or missing data—is explicitly entered in the evaluations. Explicit
consideration of uncertainties is a fundamental step towards reasonable, transparent
and ethical risk assessments.