Seismic performance assessment of highway bridge networks considering post-disaster traffic demand of a transportation system in emergency conditions

References

• Agrawal, A.K., Ghosn, M., Alampalli, S., & Pan, Y. (2012). Seismic fragility of retrofitted multispan continuous steel bridges in New York. Journal of Bridge Engineering, 17, 562–575.10.1061/(ASCE)BE.1943-5592.0000290
   Web of Science ®Google Scholar
• Banerjee, S., & Shinozuka, M. (2010). Uncertainty in bridge fragility curves and its effect on the seismic risk evaluation of a highway transportation network. Bridge Maintenance, Safety, Management and Life-cycle Optimization (pp. 1161–1166). Philadelphia.
   Google Scholar
• Bocchini, P., & Frangopol, D.M. (2011). A stochastic computational framework for the joint transportation network fragility analysis and traffic flow distribution under extreme events. Probabilistic Engineering Mechanics, 26, 182–193.10.1016/j.probengmech.2010.11.007
   Web of Science ®Google Scholar
• Brandes, U. (2001). A faster algorithm for betweenness centrality. The Journal of Mathematical Sociology, 25, 163–177.10.1080/0022250X.2001.9990249
   Web of Science ®Google Scholar
• Buriol, L.S., Resende, M.G., & Thorup, M. (2008). Speeding up dynamic shortest-path algorithms. INFORMS Journal on Computing, 20, 191–204.10.1287/ijoc.1070.0231
   Web of Science ®Google Scholar
• Casey, H. (1955). Applications to traffic engineering of the law of retail gravitation. Traffic Quarterly, 9, 23–35.
   Google Scholar
• Chang, S.E., Shinozuka, M., & Moore, J.E. (2000). Probabilistic earthquake scenarios: Extending risk analysis methodologies to spatially distributed systems. Earthquake Spectra, 16, 557–572.10.1193/1.1586127
   Google Scholar
• Chang, L., Elnashai, A.S., & Spencer, B.F. (2012). Post-earthquake modelling of transportation networks. Structure and Infrastructure Engineering, 8, 893–911.
   Web of Science ®Google Scholar
• Choe, D.E., Gardoni, P., & Rosowsky, D. (2010). Fragility increment functions for deteriorating reinforced concrete bridge columns. Journal of Engineering Mechanics ASCE , 136, 969–978.10.1061/(ASCE)EM.1943-7889.0000147
   Web of Science ®Google Scholar
• Demšar, U., Špatenková, O., & Virrantaus, K. (2008). Identifying critical locations in a spatial network with graph theory. Transactions in GIS, 12, 61–82.10.1111/tgis.2008.12.issue-1
   Google Scholar
• Dijkstra, E.W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.10.1007/BF01386390
   Web of Science ®Google Scholar
• Dong, Y., Frangopol, D.M., & Saydam, D. (2014). Sustainability of highway bridge networks under seismic hazard. Journal of Earthquake Engineering, 18, 41–66.10.1080/13632469.2013.841600
   Web of Science ®Google Scholar
• FEMA. (2012). Multi-hazard loss estimation methodology HAZUS-MH 2.1 technical and user’s manual. Washington, DC: Author.
   Google Scholar
• Hsu, T.H., & Lin, L.Z. (2006). QFD with fuzzy and entropy weight for evaluating retail customer values. Total Quality Management & Business Excellence, 17, 935–958.10.1080/14783360600598223
   Web of Science ®Google Scholar
• Karim, K.R., & Yamazaki, F. (2003). A simplified method of constructing fragility curves for highway bridges. Earthquake Engineering & Structural Dynamics, 32, 1603–1626.10.1002/(ISSN)1096-9845
   Web of Science ®Google Scholar
• Kiremidjian, A., Moore, J., Fan, Y.Y., Yazlali, O., Basoz, N., & Williams, M. (2007). Seismic risk assessment of transportation network systems. Journal of Earthquake Engineering, 11, 371–382.10.1080/13632460701285277
   Web of Science ®Google Scholar
• Liu, M., & Frangopol, D.M. (2005). Time-dependent bridge network reliability: Novel approach. Journal of Structural Engineering ASCE, 131, 329–337.10.1061/(ASCE)0733-9445(2005)131:2(329)
   Web of Science ®Google Scholar
• Martin, W.A., McGuckin, N.A., McGuckin, N.A., & McGuckin, N.A. (1998). Travel estimation techniques for urban planning. Washington, DC: National Academy Press.
   Google Scholar
• Nielson, B.G., & DesRoches, R. (2007). Analytical seismic fragility curves for typical bridges in the central and southeastern United States. Earthquake Spectra, 23, 615–633.
   Web of Science ®Google Scholar
• Nojima, N. and Sugito, M. (2000). Simulation and evaluation of post-earthquake functional performance of transportation network. Proceedings of Transportation Research Board 2009 annual meeting. [CD-ROM]. Washington, DC: TRB.
   Google Scholar
• Padgett, J.E., & DesRoches, R. (2008). Methodology for the development of analytical fragility curves for retrofitted bridges. Earthquake Engineering & Structural Dynamics, 37, 1157–1174.10.1002/(ISSN)1096-9845
   Web of Science ®Google Scholar
• Padgett, J.E., DesRoches, R., & Nilsson, E. (2010). Regional seismic risk assessment of bridge network in Charleston, South Carolina. Journal of Earthquake Engineering, 14, 918–933.10.1080/13632460903447766
   Web of Science ®Google Scholar
• Saydam, D., Bocchini, P., & Frangopol, D.M. (2013). Time-dependent risk associated with deterioration of highway bridge networks. Engineering Structures, 54, 221–233.10.1016/j.engstruct.2013.04.009
   Web of Science ®Google Scholar
• Sheffi, Y., Mahmassani, H., & Powell, W.B. (1982). A transportation network evacuation model. Transportation Research Part A: General, 16, 209–218.10.1016/0191-2607(82)90022-X
   Google Scholar
• Shinozuka, M., Zhou, Y., Kim, S., Murachi, Y., Banerjee, S., Cho, S., & Chung, H. (2008). Socio-economic effect of seismic retrofit implemented on bridges in the Los Angeles highway network (Report No. CA06-0145). Irvine, CA: Department of Civil and Environmental Engineering, University of California.
   Google Scholar
• Shiraki, N., Shinozuka, M., Moore, J.E., Chang, S.E., Kameda, H., & Tanaka, S. (2007). System risk curves: Probabilistic performance scenarios for highway networks subject to earthquake damage. Journal of Infrastructure Systems, 13, 43–54.10.1061/(ASCE)1076-0342(2007)13:1(43)
   Web of Science ®Google Scholar
• Wang, M., & Takada, T. (2005). Macrospatial correlation model of seismic ground motions. Earthquake Spectra, 21, 1137–1156.10.1193/1.2083887
   Web of Science ®Google Scholar
• Ward, S.N. (2000). San Francisco Bay area earthquake simulations: A step toward a standard physical earthquake model. Bulletin of the Seismological Society of America, 90, 370–386.
   Web of Science ®Google Scholar
• Wardrop, J. (1952). Some theoretical aspects of road traffic research. Proceedings of the Institute of Civil Engineers, London.
   Google Scholar
• Watts, D.J., & Strogatz, S.H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393, 440–442.10.1038/30918
   PubMed Web of Science ®Google Scholar
• Yang, H., Bell, M.G.H., & Meng, Q. (2000). Modeling the capacity and level of service of urban transportation networks. Transportation Research Part B: Methodological, 34, 255–275.10.1016/S0191-2615(99)00024-7
   Web of Science ®Google Scholar
• Yuan, Y.F. (2008). Impact of intensity and loss assessment following the great Wenchuan Earthquake. Earthquake Engineering and Engineering Vibration, 7, 247–254.10.1007/s11803-008-0893-9
   Web of Science ®Google Scholar
• Zadeh, L.A. (1965). Fuzzy sets. Information and Control, 8, 338–353.10.1016/S0019-9958(65)90241-X
   Web of Science ®Google Scholar
• Zhou, H., Tan, L., Ge, F., & Chan, S. (2014). Traffic matrix estimation: Advanced-tomogravity method based on a precise gravity model. International Journal of Communication Systems, 28, 1709–1728.
   Web of Science ®Google Scholar