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Tsunami Damage Prediction for Buildings: Development of Methods for Empirical and Analytical Fragility Function Derivation

Macabuag, Joshua; (2018) Tsunami Damage Prediction for Buildings: Development of Methods for Empirical and Analytical Fragility Function Derivation. Doctoral thesis (Ph.D), UCL (University College London). Green open access

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Abstract

Over the past two decades, tsunami have been the cause of 33% of total deaths and 35% of total economic losses due to natural disasters globally, and currently 6 out of 10 of the most populous megacities in the world are at risk of being severely affected by tsunami. Quantifying tsunami risk is therefore centrally important for land use and emergency planning in the DRR sector, for human and financial loss estimation in the insurance sector, and for performance-based design in the engineering sector. Tsunami fragility functions are statistical models that relate a measure of tsunami intensity (e.g. inundation depth) to probabilities of damage exceedance for a number of damage states, and form a key component of tsunami risk models. This thesis presents improved derivation methods for empirical fragility functions (those derived from observed damage data from past tsunami), and research towards methodologies for deriving analytical fragility functions (those constructed from structural analysis in the absence of past damage data). First, a critical review of the literature related to the prediction of building damage due to tsunami is presented. This review highlights that it is unclear which of the many available statistical methods available provide optimal empirical fragility functions. It is also seen that analytical methods are required for damage prediction in the vast majority of at-risk areas, however few such functions exist. Hence tsunami loads on buildings and methods of structural analysis under tsunami loading are critically reviewed so as to identify and justify the loading and analysis assumptions to be employed throughout this thesis. A methodology for deriving optimal empirical tsunami fragility functions for a given dataset is then developed and demonstrated using a unique, disaggregated building damage dataset from the 2011 Japan Tsunami. The proposed methodology identifies the key Tsunami Intensity Measures (TIMs) and improved statistical methods to be used for fragility function derivation. A number of techniques novel in the field of empirical fragility function derivation are introduced: Multiple Imputation, K-fold Cross-Validation, and semi-parametric models. Furthermore, a preliminary methodology is also presented for quantifying debris-related effects on fragility functions. Methods for structural analysis for the derivation of analytical fragility functions are then developed. First an investigation is carried out on how time-dependent effects, ductility and overstrength (a structure’s ability tomaintain a load greater than its yield value) affect structural damage analysis. This is then extended to develop a simplified method for estimating tsunami-induced structural damage under tsunami loading, suitable for use in the large number of analyses required to derive analytical fragility functions of populations of buildings. By introducing advanced methods for selecting optimal TIMs and statistical models, and by furthering the field of structural analysis under tsunami loading, this research has the potential to influence how both empirical and analytical tsunami fragility curves are constructed in the future.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Tsunami Damage Prediction for Buildings: Development of Methods for Empirical and Analytical Fragility Function Derivation
Event: UCL (University College London)
Open access status: An open access version is available from UCL Discovery
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10047419
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