Showing papers by "Hussam Mahmoud published in 2017"

Framework for Lifecycle Cost Assessment of Steel Buildings under Seismic and Wind Hazards

Abstract: Despite the importance of considering multiple extreme events when designing structures, the current treatment of multiple hazard design in code provisions and assessment guidelines is rather vague. This is primarily because design and assessment of structures have traditionally been geared toward meeting the demand of a single hazard. In recent years, there has been a spike in interest by researchers and engineers in evaluating and designing structures for different hazard combinations. In this paper, a probabilistic framework is provided for assessing design alternatives based on estimating the lifecycle cost for two steel buildings with different heights subjected to different seismic and wind intensities. The intensities are specified based on probability of exceedance as per code standards. The total lifecycle cost is estimated using the initial cost and failure cost where the failure cost is a function of the probability of failure, which is calculated based on specified performance objectiv...

Spatial and temporal variations in resilience to tropical cyclones along the United States coastline as determined by the multi-hazard hurricane impact level model

Abstract: The United States coastline, where over 50% the population lives, is vulnerable to hurricanes along the East and Gulf coasts. However, the question remains as to whether these two areas are equally resilient to a landfalling hurricane. In addition, while it is assumed that improvements in building codes, infrastructure protections, and changing policy over the past century have been effective in reducing the impacts to a community from historically extreme hurricane events, such an assumption is still to be validated. Here, a multi-hazard artificial neural network model is used to address these questions. The Hurricane Impact Level Model is the first prediction model to utilize machine-learning techniques (artificial neural networks) to established complex connections between all meteorological factors (wind, pressure, storm surge, and precipitation resulting in inland flooding) of a tropical cyclone and how those interact with the location of landfall to produce a certain level of economic damage. This model allows for a more all-encompassing assessment of how the impacts of tropical cyclones vary along the coastline. The Hurricane Impact Level Model was trained with historical tropical cyclone events from 1998 to present day, resulting in established locational associations to modern relevant building codes and mitigation practices. Simulating the meteorological factors from historical events allows for a new assessment of economic impact changes due to infrastructure improvements and policy adaptations over time. In essence, if Hurricane Sandy hit Florida instead of New York, it would have a lower economic impact due to lower population density and more stringent building codes, which the artificial neural network has associated with the latitudes and longitudes within the state of Florida. If the Galveston hurricane were to hit today, the seawall would not succeed in lowering the economic impact to the Texas coastline. Over the years, significant effort has been put in to improving the resiliency of the United States coastline, mainly in the southern states, but it has not been enough to counteract the effects of population growth within coastal counties.

Real-time Application of the Multihazard Hurricane Impact Level Model for the Atlantic Basin

Abstract: Tropical cyclones are an example of a multi-hazard event with impacts that can highly vary depending on landfall location, wind speed, storm surge, and inland flooding from precipitation. These storms are typically categorized by their wind speed and pressure, while evacuation orders are typically given based on storm surge. The general public relies on these single hazard assessment parameters when attempting to understand the risk of an oncoming event. However, after the fact, these events are ranked by economic damage and death toll. Therefore, it is imperative that when these events are communicated to the public, during the forecast period, the multiple hazards are incorporated in terms the public can easily associate with, such as economic damage. This paper provides an evaluation on the potential for real-time use of artificial neural networks, through the utilization of an already developed Hurricane Impact Level Model, to forecast a range of economic damage from tropical cyclone events, during the 2015 and 2016 United States Hurricane Season. The Hurricane Impact Level Model is built prior to the start of each season and simulated every three hours, in conjunction with National Hurricane Center issued advisories, for oncoming tropical cyclones forecasted to make landfall. Weaker and more common tropical cyclones have a less varied forecast and produce more accurate Impact Level predictions. More complicated and uncertain events, such as 2016 Hurricane Matthew, require the user’s discretion in communicating varying landfall locations for a complex track forecast to the model. As National Hurricane Center (NHC) forecasts change with respect to both track and meteorological hazards affecting land, the estimated Impact Level and the Hurricane Impact Level (HIL) model confidence will also change. In other words, if a track shifts to a more vulnerable location, or to more locations, or the meteorological hazards increase, the Impact Level will subsequently increase. All tropical cyclones from the 2015 and 2016 seasons demonstrate the validity of the Hurricane Impact Level Model with a forecast confidence of at least 60% for up to 30 hours out from an impending landfall as well as reliability for real-time use, if data is available.