14.5 Synergistic Methodologies
207
The category of a dam coupled with classic FoS (as defined by the engineers
under various loadings) and the semi-empiric FoS/category curves leads to the annual
probability of failure p f estimate (Sect. 11.2). As there are various loading conditions
and there may be pessimistic/optimistic evaluations of each one of the diagnostic
nodes, uncertainties are transparently expressed. In order to allow swift, affordable
analyses, the symptom-driven methodology anchored by hundred years of failure
history and in-depth calibration has proven to offer the best solution when data are
scarce and large portfolio require prioritization.
Table 14.1 summarizes the key space- or drone-observable elements considered
when such observations become available. These, among others, contribute to the
probability of failure and allow swift updates of the risks generated by a single dams
or a portfolio of dams.
As per the consequences analyses, the considered space- or drone-observable data
are:
• topography precise enough to allow, if needed, detailed calculations (the presence
of man-made fills and ditches, even only a couple meters high/deep or a few meters
wide, has significant impact on the flooding behaviour);
• bathymetry, if water bodies are present;
• land use/occupation, residences, infrastructure, lines, pipes, storage facilities, parking lots, etc.
Consequences can be evaluated following a number of methodologies and simulations, from the simplest to the most sophisticated, in function of the context of the
study (Chap. 12).
In Fig. 10.5 (Sect. 10.2.1) we show the annual p f results for a real-life tailings
dams portfolio with 15 dams. We explained there that the horizontal axis shows the
various dams of the dam portfolio as well as four bench-marking values, namely:
Table 14.1 Space observable diagnostic points summary
Elements
General diagnostic nodes
Dam system description
Tailings (beaches slopes, surface irregularities); water (size and
position of pond with respect to dam crest, bathymetry, volume);
reclaim pumps barge; equipment on crown (transport lines,
spigotting, traffic); weir; intake tower/pennstock; diversion ditch
Construction
Material; berms and erosion; cross section; supervision; divergence
from plans; known errors and omissions
Stability analyses
This data will come from client (if they have any records …), but
possible instabilities (deformations, cracks, slumps) may be visible
from space observation
Instability symptoms
Wet spots on the D/S face; streaming, ponding at toe (temperature
differential); tailings deposited at toe
Settlements analyses
All this data will come from client (if they have any records …) but
settlement may be visible from space observation
207
The category of a dam coupled with classic FoS (as defined by the engineers
under various loadings) and the semi-empiric FoS/category curves leads to the annual
probability of failure p f estimate (Sect. 11.2). As there are various loading conditions
and there may be pessimistic/optimistic evaluations of each one of the diagnostic
nodes, uncertainties are transparently expressed. In order to allow swift, affordable
analyses, the symptom-driven methodology anchored by hundred years of failure
history and in-depth calibration has proven to offer the best solution when data are
scarce and large portfolio require prioritization.
Table 14.1 summarizes the key space- or drone-observable elements considered
when such observations become available. These, among others, contribute to the
probability of failure and allow swift updates of the risks generated by a single dams
or a portfolio of dams.
As per the consequences analyses, the considered space- or drone-observable data
are:
• topography precise enough to allow, if needed, detailed calculations (the presence
of man-made fills and ditches, even only a couple meters high/deep or a few meters
wide, has significant impact on the flooding behaviour);
• bathymetry, if water bodies are present;
• land use/occupation, residences, infrastructure, lines, pipes, storage facilities, parking lots, etc.
Consequences can be evaluated following a number of methodologies and simulations, from the simplest to the most sophisticated, in function of the context of the
study (Chap. 12).
In Fig. 10.5 (Sect. 10.2.1) we show the annual p f results for a real-life tailings
dams portfolio with 15 dams. We explained there that the horizontal axis shows the
various dams of the dam portfolio as well as four bench-marking values, namely:
Table 14.1 Space observable diagnostic points summary
Elements
General diagnostic nodes
Dam system description
Tailings (beaches slopes, surface irregularities); water (size and
position of pond with respect to dam crest, bathymetry, volume);
reclaim pumps barge; equipment on crown (transport lines,
spigotting, traffic); weir; intake tower/pennstock; diversion ditch
Construction
Material; berms and erosion; cross section; supervision; divergence
from plans; known errors and omissions
Stability analyses
This data will come from client (if they have any records …), but
possible instabilities (deformations, cracks, slumps) may be visible
from space observation
Instability symptoms
Wet spots on the D/S face; streaming, ponding at toe (temperature
differential); tailings deposited at toe
Settlements analyses
All this data will come from client (if they have any records …) but
settlement may be visible from space observation