Earthquakes are among the most devastating and unpredictable of natural hazards that affect civil infrastructure and have the potential for causing numerous casualties and significant economic losses over large areas. Every region that has the potential for great earthquakes should have an integrated plan for a seismic design and risk mitigation for civil infrastructure. This plan should include methods for estimating the vulnerability of building inventories and for forecasting economic losses resulting from future events. This study describes a methodology to assess risk to distributed civil infrastructure due to large-scale natural hazards with large geographical footprints, such as earthquakes, hurricanes and floods, and provides a detailed analysis and assessment of building losses due to earthquake. The distinguishing feature of this research, in contrast to previous loss estimation methods incorporated in systems such as HAZUS-MH, is that it considers the correlation in stochastic demand on building inventories due to the hazard, as well as correlation in building response and damage due to common materials, construction technologies, codes and code enforcement. These sources of correlation have been neglected, for the most part, in previous research. The present study has revealed that the neglect of these sources of correlation leads to an underestimation of the estimates of variance in loss and in the probable maximum loss (PML) used as a basis for underwriting risks. The methodology is illustrated with a seismic risk assessment of building inventories representing different occupancy classes in Shelby County, TN, considering both scenario earthquakes and earthquakes specified probabilistically. It is shown that losses to building inventories estimated under the common assumption that the individual losses can be treated as statistically independent may underestimate the PML by a factor of range from 1.7 to 3.0, depending on which structural and nonstructural elements are included in the assessment. A sensitivity analysis reveals the statistics and sources of correlation that are most significant for loss estimation, and points the way forward for supporting data acquisition and synthesis.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/43676 |
| Date | 30 March 2012 |
| Creators | Vitoontus, Soravit |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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