Seismic Resilience of Transportation
Networks
Heba Elsayed, Civil Engineering , ’15
Dr. Paolo Bocchini
ABSTRACT
ANALYSIS AND RESULTS
Resilience is the ability of the system to return to a fully recovered stage
after a disruptive perturbation, taking into consideration time and cost
efficiency.
This research project focuses on optimizing the resilience of a
transportation network subjected to seismic activity. Safe transportation
immediately after an earthquake is a basic necessity for authorities and
civilians to reach essential destinations. This depends heavily on the
damage of bridges in the system. Therefore, this study will demonstrate the
approach on a bridge network located in San Francisco consisting of all
principal roads.
The purpose of this project is to identify a sequence of bridge restorations
in a given network after a seismic attack, sorted by priority to optimize
resilience in the least amount of time. An automatic program finds this
restoration sequence based on the characteristics of the network. After an
extreme event, using this disaster management program, engineers can
start restoring the prioritized bridge and continue on to the next.
R = resilience
to = occurrence time of the extreme event
th = investigated time horizon
Q(t) = times-variant measure of functionality of the network
Cimellaro 2010, Frangopol and Bocchini 2011
SEQUENCE 1 – OPTIMUM RESILIENCE
'P' 'G' 'Gg' 'T' 'Aa' 'F' 'Ff' 'N' 'E' 'O' 'Z' 'Dd' 'R' 'Ee' 'D' 'Y'
'K' 'V' 'U' 'B' 'M' 'A' 'L' 'Ii' 'J' 'E’' 'I' 'Hh' 'Kk' 'Jj' 'H'
Resilience Analysis Sequence 1
Company 8
Company 7
N
Dd
Ff
A
E
R
I
U
Jj
Restoration Schedule
F
M
Company 6
Aa
SAN FRANCISCO NETWORK
D
K
Kk
Company 5
T
Y
B
J
Company 4
Gg
O
V
Hh
Ee
L
Company 3
Company 2
Company 1
Q
G
P
Z
Ii
H
E’
R = 91.5 %
R=91.5%
Mag.
6.5 Earthquake
Epicenter: San Francisco
Time (years)
CONCLUSION
• The above sequence of bridge restoration optimizes the resilience of
the given San Francisco network in the long term to 91. 5%, in the case
of a 6.5 magnitude earthquake with an epicenter in the city.
PARAMETERS
Collected using Hazus, National Bridge Inventory, and Google Maps
• Gathering network information is not efficient. A new system needs to
be developed that is more accessible, comprehensive, and universal.
FURTHER WORK
Links:
Origin, destination node
Number of directions
Travel time
Practical capacity
Length
Nodes:
Longitude and latitude
Travels generated
Travels attracted
RESILIENCE ANALYSIS
Bridges:
Links carried, crossed
Damage condition
Detour time
Detour distance
Detour practical capacity
• Multi-objective analysis – short term and long term resilience
• New versions of resilience analysis MATLAB code
• Uniform network of data
ACKNOWLEDGMENTS
Special thanks to Dr. Paolo Bocchini and Aman Karamlou
for allowing the use of their resilience analysis code.