Probabilistic Resilience Quantification and Visualization Building Performance to Hurricane Wind Speeds

Natural and man made disasters are unpredictable and unavoidable in today\u27s world. Their frequency of occurrence and damages keep increasing. Due to the efforts to reduce negative consequences from such disasters, the concept of resilience has gained so much popularity in disaster management area especially after disasters like the September 11 attacks and Hurricane Katrina. Complex systems of today are under operational risks because of increasing threats and their high level of vulnerability. Hence, such systems need to adapt the concept of resilience for continuous operations. Resilience is a proactive concept which should incorporate both pre-event (preparedness and mitigation) and post-event (response and recovery) activities. As a new concept, resilience engineering is really about monitoring threats to a system and taking necessary actions to reduce the probability of failure of the system. Particularly, quantitative approaches for measuring resilience need to be developed to compare different mitigation strategies, to come up with the most appropriate one, and to provide better support and decision making. In order to achieve this goal, a methodology for quantification of resilience of different building types against different categories of hurricane is proposed. The formulation presented in this dissertation for resilience quantification is based on several parameters such as structural loss ratios and conditional probabilities of exceedance for damage states, estimated and actual recovery times, and wind speed probability. The proposed formulation is applicable to a community consisting of buildings with different types besides being applicable to individual building types. Numerical results for Monte Carlo and sensitivity analyses for resilience of various building types against Category 1, 2 and 3 hurricanes are presented. A dashboard representation consisting of green, yellow and red zones is defined, and histograms are presented to demonstrate into which zone the resilience of each building type falls. Resilience of different building types is compared based on the numerical results. In addition, sensitivities of the resilience of various building types to different parameters are evaluated. Moreover, resilience values are computed before and after various mitigation actions are taken. These resilience values are compared to assess the effectiveness of the mitigation actions. The proposed formulation can be used to determine resilience values and compare resilience of different building types or communities against a specific hurricane category

Handling Emergency Management in [an] Object Oriented Modeling Environment

It has been understood that protection of a nation from extreme disasters is a challenging task. Impacts of extreme disasters on a nation's critical infrastructures, economy and society could be devastating. A protection plan itself would not be sufficient when a disaster strikes. Hence, there is a need for a holistic approach to establish more resilient infrastructures to withstand extreme disasters. A resilient infrastructure can be defined as a system or facility that is able to withstand damage, but if affected, can be readily and cost-effectively restored. The key issue to establish resilient infrastructures is to incorporate existing protection plans with comprehensive preparedness actions to respond, recover and restore as quickly as possible, and to minimize extreme disaster impacts. Although national organizations will respond to a disaster, extreme disasters need to be handled mostly by local emergency management departments. Since emergency management departments have to deal with complex systems, they have to have a manageable plan and efficient organizational structures to coordinate all these systems. A strong organizational structure is the key in responding fast before and during disasters, and recovering quickly after disasters. In this study, the entire emergency management is viewed as an enterprise and modelled through enterprise management approach. Managing an enterprise or a large complex system is a very challenging task. It is critical for an enterprise to respond to challenges in a timely manner with quick decision making. This study addresses the problem of handling emergency management at regional level in an object oriented modelling environment developed by use of TopEase software. Emergency Operation Plan of the City of Hampton, Virginia, has been incorporated into TopEase for analysis. The methodology used in this study has been supported by a case study on critical infrastructure resiliency in Hampton Roads

Resilience Assessment for Power Distribution Systems

Power distribution systems are essential and crucial infrastructures for the development and maintenance of a sustainable society. These systems are extremely vulnerable to various types of natural and man-made disasters. The assessment of resilience focuses on preparedness and mitigation actions under pre-disaster conditions. It also concentrates on response and recovery actions under post-disaster situations. The aim of this study is to present a methodology to assess the resilience of electric power distribution poles against wind-related events. The proposed methodology can improve the accuracy and rapidity of the evaluation of the conditions and the assessment of the resilience of poles. The methodology provides a metric for the evaluation of the resilience of poles under pre-disaster and post-disaster conditions. The metric was developed using mathematical expressions for physical forces that involve various variables, such as physical dimensions of the pole, the inclination of the pole, and wind speed. A three-dimensional imaging technology (photogrammetry) was used to determine the inclination of poles. Based on expert opinion, the proposed metric was used to define zones to visualize resilience. Visual representation of resilience is helpful for decision makers to prioritize their resources before and after experiencing a wind-related disaster. Multiple electric poles in the City of Beaumont, TX were used in a case study to evaluate the proposed methodology

Mortality analysis of COVID-19 infection in chronic kidney disease, haemodialysis and renal transplant patients compared with patients without kidney disease: A nationwide analysis from Turkey

© The Author(s) 2020.Background. Chronic kidney disease (CKD) and immunosuppression, such as in renal transplantation (RT), stand as one of the established potential risk factors for severe coronavirus disease 2019 (COVID-19). Case morbidity and mortality rates for any type of infection have always been much higher in CKD, haemodialysis (HD) and RT patients than in the general population. A large study comparing COVID-19 outcome in moderate to advanced CKD (Stages 3-5), HD and RT patients with a control group of patients is still lacking. Methods. We conducted a multicentre, retrospective, observational study, involving hospitalized adult patients with COVID-19 from 47 centres in Turkey. Patients with CKD Stages 3-5, chronic HD and RT were compared with patients who had COVID-19 but no kidney disease. Demographics, comorbidities, medications, laboratory tests, COVID-19 treatments and outcome [in-hospital mortality and combined in-hospital outcome mortality or admission to the intensive care unit (ICU)] were compared. Results. A total of 1210 patients were included [median age, 61 (quartile 1-quartile 3 48-71) years, female 551 (45.5%)] composed of four groups: Control (n = 450), HD (n = 390), RT (n = 81) and CKD (n = 289). The ICU admission rate was 266/ 1210 (22.0%). A total of 172/1210 (14.2%) patients died. The ICU admission and in-hospital mortality rates in the CKD group [114/289 (39.4%); 95% confidence interval (CI) 33.9-45.2; and 82/289 (28.4%); 95% CI 23.9-34.5)] were significantly higher than the other groups: HD = 99/390 (25.4%; 95% CI 21.3-29.9; P<0.001) and 63/390 (16.2%; 95% CI 13.0-20.4; P<0.001); RT = 17/81 (21.0%; 95% CI 13.2-30.8; P = 0.002) and 9/81 (11.1%; 95% CI 5.7-19.5; P = 0.001); and control = 36/450 (8.0%; 95% CI 5.8-10.8; P<0.001) and 18/450 (4%; 95% CI 2.5-6.2; P<0.001). Adjusted mortality and adjusted combined outcomes in CKD group and HD groups were significantly higher than the control group [hazard ratio (HR) (95% CI) CKD: 2.88 (1.52- 5.44); P = 0.001; 2.44 (1.35-4.40); P = 0.003; HD: 2.32 (1.21- 4.46); P = 0.011; 2.25 (1.23-4.12); P = 0.008), respectively], but these were not significantly different in the RT from in the control group [HR (95% CI) 1.89 (0.76-4.72); P = 0.169; 1.87 (0.81-4.28); P = 0.138, respectively]. Conclusions. Hospitalized COVID-19 patients with CKDs, including Stages 3-5 CKD, HD and RT, have significantly higher mortality than patients without kidney disease. Stages 3-5 CKD patients have an in-hospital mortality rate as much as HD patients, which may be in part because of similar age and comorbidity burden. We were unable to assess if RT patients were or were not at increased risk for in-hospital mortality because of the relatively small sample size of the RT patients in this study