Development of a prediction model for windborne debris damage assessment of coast communities under hurricanes - Dong, Yue, author; Guo, Yanlin, advisor; van de Lindt, John W., committee member; Ellingwood, Bruce R., committee member; Gao, Xinfeng, committee member (Colorado State University. Libraries, 2023)
Urban high-rise building envelopes may suffer severe damage induced by windborne debris during hurricanes. Breaches in urban building envelopes may lead to cascading building content loss due to rain intrusion and building functionality loss for long periods of time, thus disrupting the resilience of urban coastal communities. Such a societal disruption may become worse due to the rapid growth of population and development of economy in coastal communities. Objective of this dissertation is to develop an efficient prediction model for assessing windborne debris damage to building envelopes and apply it to the hurricane scenarios identified through a de-aggregation process to enable risk-informed resilience assessment of coastal communities for windborne debris impact. A set of scenario hurricanes corresponding to a stipulated return period (RP) for resilience assessment of coastal communities for debris impact is systematically identified using the de-aggregation approach proposed in this dissertation. The existing building envelope damage assessment models for urban high/mid-rise buildings often neglect the geometry of building clusters or simply assume a homogeneous configuration, which can introduce errors and uncertainties for urban building clusters with varying geometries and layouts. This dissertation proposes a new fragility modeling approach for urban buildings envelopes, which explicitly considers geometric configurations of urban buildings, to improve the accuracy of risk assessment for urban buildings. Estimating the urban wind field that drives the debris flight is critical for constructing fragilities for debris damage. The traditional tools for simulating urban wind fields, such as wind tunnel tests and Computational Fluid Dynamic (CFD), are usually expensive or time-consuming for complex urban environments and cannot offer an efficient prediction of the urban wind field that is sufficient for risk assessment at a community level. Therefore, an efficient machine learning (ML) based prediction model of wind fields around building clusters is proposed using conditional Generative Adversarial Networks (cGANs). Uncertainty analysis is conducted on the models and parameters involved in this procedure of debris damage assessment. The uncertainty in the final prediction of windborne debris damage is evaluated through uncertainty propagation among models and parameters. Sensitivity analysis with some critical factors is also conducted to identify the dominant contributors to the uncertainty in the estimation of debris damage. In the end, windborne debris damage on building envelopes in virtual communities under synthetic hurricanes scenarios is investigated to illustrate the damage assessment procedure for windborne debris impact at the community level.
Keywords: hurricane risk; urban wind field; community resilience; windborne debris; machine learning