Damage Prediction Of Low Rise Buildings Under Hurricane Winds

Author : Mehrshad Amini
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (127 download)

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Download or read book CFD-Based Probabilistic Framework for Evaluation of Coastal Residential Buildings Under Combined Hurricane Wind and Surge Flood written by Mehrshad Amini and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Coastal residential buildings are vulnerable to significant damage due to hurricane related hazards such as storm surge, wind loads, and inundation. Recent damage to residential buildings caused by hurricanes in coastal areas illustrates poor performance of coastal structures against hurricane related hazards, which indicates that recent standards and building code provisions need to be improved in terms of loading and design requirements. A fundamental problem with current standards is that most follow the deterministic approach to some extent. For instance, both uncertainties regarding flood hazards and building structure characteristics such as elevation, number of stories, and size have not been considered in current flood risk assessment methods, which causes many concerns in terms of validity and reliability. On the other hand, Performance-Based Engineering (PBE) methodology is a well-known design approach to address inherent uncertainties for assessing and mitigating the risk associated with engineering structures. However, with only limited PBE frameworks in hurricane engineering fields proposed during recent years, there is lack of sufficient understanding of different aspects for development of standards needed for hurricane resistant design and retrofit of residential buildings. Furthermore, given the concurrent multi-hazard nature of hurricanes, designers need to address more complex loading conditions and design decisions. Based on the performance of coastal residential buildings in past hurricanes, elevating the lowest floor above the expected Base Flood Elevation (BFE) has been found to be the most effective strategy to reduce direct damage caused by flood and storm surge. However, elevated buildings can be exposed to different levels of wind loads due to unique aerodynamic characteristics, which leads to the need for more stringent design of structural and foundation systems. In addition, past hurricanes have shown that the actual flood levels can be several feet higher than the BFE, which means even pile-elevated houses may still be vulnerable to damage. Therefore, some communities encourage homeowners to add freeboard to the specific BFE in order to mitigate the risk of damage. The amount of freeboard depends on many factors, for which there is no rational approach for building owners and designers to make the most efficient decision. This study proposes a probabilistic framework in order to investigate the combined interaction of hurricane wind and coastal surge flood on typical residential homes upgraded based on various retrofit strategies. The goal of developing such a framework is to contribute to holistic and quantitative approach in evaluating the potential damage to retrofitted, particularly elevated coastal residential buildings. This proposed probabilistic framework consists of four main modules, namely hazard analysis, structural analysis, damage assessment, and loss measurement. A literature review was carried out to evaluate the performance of coastal residential buildings with respect to direct and indirect damage. The result of the literature review on mitigation techniques are discussed according to hurricane wind and flood-related hazards. Identification and quantification of these hurricane-associated hazards is the first step to understanding the behavior of residential buildings and identifying common failure mechanisms and mitigation techniques. The Computational Fluid Dynamic (CFD) analysis was performed to obtain realistic loading scenarios (wind and wave effects) and corresponding engineering demand parameters, respectively. A comprehensive parametric analysis was conducted to understand the effect of various factors, including wind angle, wave type (regular and irregular waves), building elevation, and pier distribution on wind- and wave-induced loads on elevated coastal residential buildings. The CFD models were validated based on available data in terms of wind and wave loadings separately due to lack of current laboratory experiments. The resistance capacities and statistical characteristics for various building components under positive and negative pressures were obtained from experimental tests available in the literature review. The procedure relies on the Monte Carlo Simulation (MCS) to propagate uncertainties through the CFD analysis. Finally, damage assessment and vulnerability analysis were conducted based on selected failure criteria (e.g., HAZUS database) to develop physics-based fragility curves based on four different damage states, and finally obtain loss curves in terms of the building elevation for the selected residential building. A typical wood-frame residential building was selected for the case study to develop the fragility curves for four damage states and the corresponding loss curve based on HAZUS-MH. The building was assumed to be located in the Bolivar Peninsula, where it was heavily impacted by Hurricane Ike as a Category 2 storm. The fragility curves and loss curve were developed for two different scenarios: the building with 8d and 6d common nails used for the connection of roof and floor sheathings. These loss curves predict the expected damage ratio of the building due to combined effects of wind and waves considering the specific house elevation, which can help design professionals and home builders in order to select a reasonable freeboard above the base flood elevation determined based on a probabilistic approach rather than available deterministic methods. This framework can also be utilized in risk assessment and decision analysis of other types of structures against various environmental hazards.