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Evolution of the Operational Loss over Time
25
San Andreas 8.0
20
Hayward 7.50
15
San Andreas
7.50 (worst case)
10
5
0
0
30
60
90
120
150
180
210
240
270
300
330
360
Time after the event (days)
Fig. 19.3. Evolution of operational loss over time for three scenarios
days after the event has occurred to obtain the operational losses of the system. Exam-
ple computation of daily losses due to reduced traffic on various links because of bridge
closures isshown inFigure 19.3
The losses are integrated over the down time duration to obtain the total operational
loss. Figure 19.4 shows the operational losses for each scenario earthquake on the San
Andreas Fault. As can be seen from that figure, the operational losses reach a value of
$1.4Billionsignificantlyexceedingthedirectstructurallossesof$1.18B.Similarresults
are obtained also for the Hayward Fault scenario (see Stergiou and Kiremidjian, 2006);
however,theoperationallossesreach$2.12BillionforthelargestHaywardFaultscenario
- almost twice the estimated direct structural loss value. The main reason for the large
operational loss from the Hayward Fault scenarios is that there are more TAZs affected
by these events and the traffic volume is larger on the links affected by these events.
In these estimates only commuter traffic was considered and it was assumed that the
demand remains constant after an earthquake . If freight traffic has also been considered,
the operational losses increase by a substantial amount since freight trips are five to six
times more expensive than passenger trips.
3.5. ANNUALSEISMIC RISK ASSESSMENT
The structural and operational loss estimates for each scenario event are combined using
eq. (19.16) to evaluate the annual risk for the transportation system. Figure 19.5 shows
the annual risk curve and the corresponding best fitted equation. From the figure it can
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