Multi-hazard socio-physical resilience assessment of hurricane-induced hazards on coastal communities

Hurricane-induced hazards can result in significant damage to the built environment cascading into major impacts to the households, social institutions, and local economy. Although quantifying physical impacts of hurricane-induced hazards is essential for risk analysis, it is necessary but not sufficient for community resilience planning. While there have been several studies on hurricane risk and recovery assessment at the building- and community-level, few studies have focused on the nexus of coupled physical and social disruptions, particularly when characterizing recovery in the face of coastal multi-hazards. Therefore, this study presents an integrated approach to quantify the socio-physical disruption following hurricane-induced multi-hazards (e.g., wind, storm surge, wave) by considering the physical damage and functionality of the built environment along with the population dynamics over time. Specifically, high-resolution fragility models of buildings, and power and transportation infrastructures capture the combined impacts of hurricane loading on the built environment. Beyond simulating recovery by tracking infrastructure network performance metrics, such as access to essential facilities, this coupled socio-physical approach affords projection of post-hazard population dislocation and temporal evolution of housing and household recovery constrained by the building and infrastructure recovery. The results reveal the relative importance of multi-hazard consideration in the damage and recovery assessment of communities, along with the role of interdependent socio-physical system modeling when evaluating metrics such as housing recovery or the need for emergency shelter. Furthermore, the methodology presented here provides a foundation for resilience-informed decisions for coastal communities

Parametrized Wind-Surge-Wave Fragility Functions for Wood Utility Poles

Wood poles are among key components of the overhead grid infrastructure that are highly vulnerable to wind hazards. In coastal regions where hurricanes are often accompanied with a storm surge, in addition to high wind pressure, poles may experience significant surge and wave loads with the potential of triggering multiple failure mechanisms. However, multihazard pole fragility models that consider various modes of failure are lacking. This paper proposed a set of parameterized fragility models that are a function of wind-, surge-, and wave-related intensity measures and properties of poles. For this purpose, a design of experiment was conducted to generate realizations of intensity measures and pole-specific deterministic and uncertain parameters. For each realization, the state of survival/failure of pole was estimated for each mode of failure. Subsequently, for each class of pole and soil type, a logistic regression was carried out to generate fragility models for pole rupture at the ground line and pole overturning due to foundation failure. The results indicated that both pole rupture and foundation failure can be significant modes of failure conditioned on the type of soil. For example, for medium-strength cohesive soils, both modes of failure were significant, whereas for very-stiff-strength cohesive soils, pole rupture was the dominant mode of failure. The results of this study are key for risk and resilience analysis of coastal electric power systems and provide useful insights for decision making and risk management processes

Hurricane Fragility Assessment of Power Transmission Towers for a New Set of Performance-Based Limit States

With the increasing reliance on the constant flow of electricity, risk-based management strategies are increasingly needed to ensure that with limited available resources, the grid can maintain high reliability and resilience. A growing concern in meeting this objective is the impact of climatic extremes, as the wide exposure of the power grid infrastructure has resulted in a system that is inherently vulnerable to extreme climatic hazards which are exacerbated by climate change. Analyzing the likelihood of damage induced by extreme hazards is critical for developing risk-informed strategies. Overhead structures, in particular, may experience a wide spectrum of damage types and degrees during hurricanes. Beyond the collapse state of transmission towers, which has been investigated in the past, non-collapse damage states in lattice towers require further attention as they can assist with performance-based design, grid recovery planning, and hardening decisions in preparation for extreme events. The present study establishes a set of performance-based limit states for lattice transmission towers subject to wind-induced extreme loadings. Specifically, five damage states including no damage, slight, moderate, and extensive damage, and collapse are defined. These limit states are founded on the nonlinear behavior of lattice towers and the type and severity of failures in tower elements and connections, as they relate to the repair or replacement requirements of towers. Focusing on a double circuit vertical steel lattice transmission tower as a case study, the proposed limit states are evaluated by generating a large number of random realizations of a diverse set of uncertain variables including those related to wind pressure and material properties using Latin Hypercube sampling method. The generated realizations are used in a set of nonlinear pushover analyses to investigate the performance of the tower at various loading levels. Subsequently, multi-state fragility functions are developed via logistic regression. These fragility models constitute a key step toward reliable extreme wind hazard risk assessment of the transmission grid and can assist with risk-informed decision-making in support of a resilient power grid

Resilience Enhancement of Electric Power Distribution Grids against Wildfires

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This paper applies a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources (RESs), e.g., wind and solar energy, micro turbines (MTs) as well as energy storage systems (ESSs) when exposed to progressive wildfires. The wildfire event is characterized comprehensively and the dynamic line rating (DLR) of overhead distribution branches is used to model the impacts of wildfires on distribution power lines. A scenario-based optimization formulation is applied to tackle the system prevailing uncertainties. The applied framework is evaluated on the IEEE 33-node test system and the numerical results reveal the promising efficacy of the methodology

Resilient Operation of Electric Power Distribution Grids under Progressive Wildfires

Wildfires have been growingly recognized as a prominent threat in regions with high temperatures during the summer. Power distribution systems, especially those passing through forest regions, are exposed and highly vulnerable to wildfires. This paper provides a general formulation to enhance the operational resilience of power distribution networks equipped with renewable energy resources (RESs), e.g., wind and solar energy, micro turbines (MTs) as well as energy storage systems (ESSs) when exposed to progressive wildfires. The wildfire incident is characterized comprehensively and the dynamic heat balance (DHB) equations of power distribution branches are used to model the impacts of wildfires on overhead line conductors. A mixed-integer quadratic optimization formulation is applied to optimally operate and coordinate all local energy resources to reduce load outages and enhance the system resilience. The applied framework is evaluated on the IEEE 33-node test system. Comprehensive sensitivity analyses are conducted to assess the efficacy of the applied framework where the numerical results reveal the resilient operation of power distribution networks in the face of wildfire emergencies

Fragility Analysis of Coastal Roadways and Performance Assessment of Coastal Transportation Systems Subjected to Storm Hazards

Coastal transportation systems are extremely vulnerable due to the coupled impacts of storm surge, waves, and inundation. Existing literature has developed coastal fragility models for bridges. However, to date, flood fragility models for coastal roadways are lacking. For this purpose, the current study proposes a data-driven fragility model based on logistic regression for coastal roadways, with failure probability conditioned on distance to shoreline and inundation duration, using hindcast data for Hurricane Ike. In addition, the effect of bridge and roadway damage on transportation network performance is investigated through a case study on Galveston Island, Texas. The results indicate the spatial distribution of storm impacts on the transportation network, with select roads highly vulnerable if they are located within a couple of hundred meters of the shoreline. In addition, considering roadway damage in addition to bridge damage alone, which is the current state of the art, can have a significant impact on decreasing the performance of the transportation network. Such analyses shed light on potential policy or risk mitigation practices that are expected to be increasingly important in the future as sea level rise further reduces roadway distance to the shoreline or as storm intensity and frequency changes

A Preliminary Study on the Usage of a Data-Driven Probabilistic Approach to Predict Valve Performance Under Different Physiological Conditions

Predicting potential complications after aortic valve replacement (AVR) is a crucial task that would help pre-planning procedures. The goal of this work is to generate data-driven models based on logistic regression, where the probability of developing transvalvular pressure gradient (DP) that exceeds 20 mmHg under different physiological conditions can be estimated without running extensive experimental or computational methods. The hemodynamic assessment of a 26 mm SAPIEN 3 transcatheter aortic valve and a 25 mm Magna Ease surgical aortic valve was performed under pulsatile conditions of a large range of systolic blood pressures (SBP; 100-180 mmHg), diastolic blood pressures (DBP; 40-100 mmHg), and heart rates of 60, 90 and 120 bpm. Logistic regression modeling was used to generate a predictive model for the probability of having a DP \u3e 20 mmHg for both valves under different conditions. Experiments on different pressure conditions were conducted to compare the probabilities of the generated model and those obtained experimentally. To test the accuracy of the predictive model, the receiver operation characteristics curves were generated, and the areas under the curve (AUC) were calculated. The probabilistic predictive model of DP \u3e 20 mmHg was generated with parameters specific to each valve. The AUC obtained for the SAPIEN 3 DP model was 0.9465 and that for Magna Ease was 0.9054 indicating a high model accuracy. Agreement between the DP probabilities obtained between experiments and predictive model was found. This model is a first step towards developing a larger statistical and data-driven model that can inform on certain valves reliability during AVR pre-procedural planning

Impact of blood pressure on coronary perfusion and valvular hemodynamics after aortic valve replacement

Objective: Our objective was to evaluate the impact of various blood pressures (BPs) on coronary perfusion and valvular hemodynamics following aortic valve replacement (AVR). Background: Lower systolic and diastolic (SBP/DBP) pressures from the recommended optimal target range of SBP \u3c 120–130 mmHg and DBP \u3c 80 mmHg after AVR have been independently associated with increased cardiovascular and all-cause mortality. Methods: The hemodynamic assessment of a 26 mm SAPIEN 3 transcatheter aortic valve (TAV), 29 mm Evolut R TAV, and 25 mm Magna Ease surgical aortic valve (SAV) was performed in a pulsed left heart simulator with varying SBP, DBP, and heart rate (HR) conditions (60 and 120 bpm) at 5 L/min cardiac output (CO). Average coronary flow (CF), effective orifice areas (EOAs), and valvulo-arterial impedance (Zva) were calculated. Results: At HR of 60 bpm, at SBP \u3c 120 mmHg and DBP \u3c 60 mmHg, CF decreased below the physiological lower limit with several different valves. Zva and EOA were found to increase and decrease respectively with increasing SBP and DBP. The same results were found with an HR of 120 bpm. The trends of CF variation with BP were similar in all valves however the drop below the lower physiological CF limit was valve dependent. Conclusion: In a controlled in vitro system, with different aortic valve prostheses in place, CF decreased below the physiologic minimum when SBP and DBP were in the range targeted by blood pressure guidelines. Combined with recent observations from patients treated with AVR, these findings underscore the need for additional studies to identify the optimal BP in patients treated with AVR for AS