Performance-based ice engineering: a data-driven multi-scale approach

Ice storms are one of the most devastating natural hazards which have the potential to inflict significant damage to the built environment. The multi-hazard nature of ice events complicates the analysis of their induced risk due to their inherent nonlinear interactions. In addition, the concurrent and interacting hazards are often responsible for several aerodynamical/dynamical instabilities such as the galloping mechanism. Moreover, the existing risk approaches are usually not suited for large-scale risk evaluation over extended geographical regions due to the involved high-computational costs. Therefore, in this study, a novel data-driven multi-scale performance-based ice engineering (PBIE) framework is developed to support the design of new structures subjected to ice storms or the rehabilitation of existing ones. In addition, the proposed PBIE is capable of rapidly estimating the real-time risk over an extended region due to an ice event. Specifically, it leverages the superior capabilities of state-of-the-art data-driven techniques (e.g., machine learning) to efficiently generate the corresponding risk maps and identify the high-risk areas. The proposed PBIE framework is applied to a simplified example in which the galloping-induced risk on iced conductors, in terms of the galloping amplitude, is evaluated for both local and regional scales. The resulting PBIE framework can be readily applied for design or retrofitting purposes or integrated within an emergency response management system to inform preventive actions that can mitigate the ice storm-induced damages and save lives

A Novel Hybrid Machine Learning Model for Rapid Assessment of Wave and Storm Surge Responses Over an Extended Coastal Region

Storm surge and waves are responsible for a substantial portion of tropical and extratropical cyclones-related damages. While high-fidelity numerical models have significantly advanced the simulation accuracy of storm surge and waves, they are not practical to be employed for probabilistic analysis, risk assessment or rapid prediction due to their high computational demands. In this study, a novel hybrid model combining dimensionality reduction and data-driven techniques is developed for rapid assessment of waves and storm surge responses over an extended coastal region. Specifically, the hybrid model simultaneously identifies a low-dimensional representation of the high-dimensional spatial system based on a deep autoencoder (DAE) while mapping the storm parameters to the obtained low-dimensional latent space using a deep neural network (DNN). To train the hybrid model, a combined weighted loss function is designed to encourage a balance between DAE and DNN training and achieve the best accuracy. The performance of the hybrid model is evaluated through a case study using the synthetic data from the North Atlantic Comprehensive Coastal Study (NACCS) covering critical regions within New York and New Jersey. In addition, the proposed approach is compared with two decoupled models where the regression model is based on DNN and the reduction techniques are either principal component analysis (PCA) or DAE which are trained separately from the DNN model. High accuracy and computational efficiency are observed for the hybrid model which could be readily implemented as part of early warning systems or probabilistic risk assessment of waves and storm surge

Modeling rain-induced effects on boundary-layer wind field of tropical cyclones

Despite the significant impacts of heavy rainfall on the tropical cyclone intensity due to the transfer of horizontal momentum between air and raindrops, the comprehensive modeling of rain-induced effects on the boundary-layer wind field remains a challenge. The wind shear zone developed surrounding the falling precipitation results in complicated dynamic interactions between the wind and rain fields. The solution of dynamically coupled, intensively interactive wind and rain fields may be achieved using high-fidelity air-water interaction simulations but needs extremely high computational costs. To consider the wind-rain interactions with a first-order approximation, the fully-coupled dynamic system governing the raindrop motion and the wind field has been simplified herein to a weakly-coupled one represented by aerodynamic drag force. The drag-induced horizontal momentum transfer is integrated into the governing equations of the linear, height-resolving wind field, and an analytical model is accordingly developed to effectively consider the rain-induced effects on the boundary-layer winds of tropical cyclones. The results generated by the present model are consistent with the field measurements. It has been demonstrated that, while the wind speed can be either accelerated or decelerated depending on the location in the tropical cyclones and the rain parameters (e.g., rain rate, relative motion between the air and raindrops, drag coefficient and raindrop size distribution), the rain-induced effects on the boundary-layer wind directions (and hence the inflow angle) also have important significance on the tropical cyclone wind hazard on tall buildings and other structures. Due to its simplicity and high computational efficiency, the proposed model could be easily implemented in the risk assessments for tropical-cyclone wind hazards in engineering applications

An analytical framework for rapid estimate of rain rate during tropical cyclones

An analytical framework for rapid estimate of rain rate during a translating tropical cyclone was proposed in this study. The efficient analysis framework for rain field is based on the observation that rain-induced momentum flux at Earth's surface cannot be ignored. The total surface stress results mainly from momentum flux contributions of wind and rain. A height-resolving wind field was utilized during the model construction leading to a linear, analytical solution of the surface rain rate. The obtained rain rate model explicitly depends on parameters for a typical tropical cyclone wind field simulation, namely storm location, approach angle, translation speed, radius of maximum wind, pressure profile, surface drag coefficient, and turbulent diffusivity. Hence, it could be readily implemented into state-of-the-art tropical cyclone risk assessment using the Monte Carlo technique. The rainbands in the proposed methodology were simulated using a local perturbation scheme. Sensitivity analysis of the rainfall field to the abovementioned parameters was comprehensively conducted. The results generated by the present analytical framework for rapid estimate of rain rate during tropical cyclones are consistent with field measurements

A linear height-resolving windfield model for tropical cyclone boundary layer

The wind field model is one of the most important components for the tropical cyclone hazard assessment, thus the appropriate design of this element is extremely important. While solving the fully non-linear governing equations of the wind field was demonstrated to be quite challenging, the linear models showed great promise delivering a simple solution with good approximation to the wind field, and can be readily adopted for engineering applications. For instance it can be implemented in the Monte Carlo technique for rapid tropical-cyclone risk assessment. This study aims to develop a height-resolving, linear analytical model of the boundary layer winds in a moving tropical cyclone. The wind velocity is expressed as the summation of two components, namely gradient wind in the free atmosphere and frictional component near the ground surface. The gradient wind was derived straightforwardly, while the frictional component was obtained based on the scale analysis of the fully non-linear Navier-Stokes equations. The variation of wind field with respect to the angular coordinate was highlighted since its contribution to the surface wind speed and associated spatial distribution cannot be ignored in the first-order approximation. The results generated by the present model are consistent with tropical cyclone observations

Modeling tropical cyclone boundary layer: Height-resolving pressure and wind fields

The high-accurate wind field of a tropical cyclone boundary layer, which is essentially governed by the Navier-Stokes equations, could be efficiently obtained by predefining the pressure field. Conventionally, the prescribed pressure filed is a 1-D function varying with the distance to the cyclone center (radius). In this study, the pressure field model has been extended to a 2-D function with respect to both radius and height. In addition, a number of field measurements in the tropical cyclone boundary layer indicate rapid variation of the thermodynamic temperature and moisture with time and space. Hence, their effects on the wind field were considered in terms of the virtual temperature, which was integrated in the modeling of pressure field. The analytical solutions of the wind field, as a sum of gradient and frictional wind components, were derived based on a height-resolving scheme using the updated pressure field. Since the tropical cyclone gradient wind and depth of boundary layer are mutually dependent, the iteration approach was utilized in the computation. The proposed height-resolving pressure and wind analytical models have been comprehensively validated with the global positioning system (GPS)-based dropsonde data. The significant importance to consider the height-varying pressure, thermodynamic temperature and moisture in the modeling of the wind field in the tropical cyclone boundary layer were also demonstrated

A semi-empirical model for mean wind velocity profile of landfalling hurricane boundary layers

The existence of the super-gradient-wind region, where the tangential winds are larger than the gradient wind, has been widely observed inside the hurricane boundary layer. Hence, the extensively used log-law or power-law wind profiles under near-neutral conditions may be inappropriate to characterize the boundary layer winds associated with hurricanes. Recent development in the wind measurement techniques overland together with the abundance of data over ocean enabled a further investigation on the boundary layer wind structure of hurricanes before/after landfall. In this study, a semi-empirical model for mean wind velocity profile of landfalling hurricanes has been developed based on the data from the Weather Surveillance Radar-1988 Doppler (WSR-88D) network operated by the National Weather Service and the Global Positioning System (GPS) dropsondes collected by the National Hurricane Center and Hurricane Research Division. The proposed mathematical representation of engineering wind profile consists of a logarithmic function of the height z normalized by surface roughness z0 (z/z0) and an empirical function of z normalized by the height of maximum wind δ (z/δ). In addition, the consideration of wind direction in terms of the inflow angle is integrated in the boundary layer wind profile. Field-measurement wind data for both overland and over-ocean conditions have been employed to demonstrate the accuracy of simulation and convenience in use of the developed semi-empirical model for mean wind velocity profile of landfalling hurricanes

A knowledge‐enhanced deep reinforcement learning‐based shape optimizer for aerodynamic mitigation of wind‐sensitive structures

Structural shape optimization plays an important role in the design of wind‐sensitive structures. The numerical evaluation of aerodynamic performance for each shape search and update during the optimization process typically involves significant computational costs. Accordingly, an effective shape optimization algorithm is needed. In this study, the reinforcement learning (RL) method with deep neural network (DNN)‐based policy is utilized for the first time as a shape optimization scheme for aerodynamic mitigation of wind‐sensitive structures. In addition, “tacit” domain knowledge is leveraged to enhance the training efficiency. Both the specific direct‐domain knowledge and general cross‐domain knowledge are incorporated into the deep RL‐based aerodynamic shape optimizer via the transfer‐learning and meta‐learning techniques, respectively, to reduce the required datasets for learning an effective RL policy. Numerical examples for aerodynamic shape optimization of a tall building are used to demonstrate that the proposed knowledge‐enhanced deep RL‐based shape optimizer outperforms both gradient‐based and gradient‐free optimization algorithms

Hurricane wind and storm surge effects on coastal bridges under a changing climate

Hurricanes and their cascading hazards have been responsible for widespread damage to life and property, and are the largest contributor to insured annual losses in coastal areas of the U.S.A. Such losses are expected to increase because of changing climate and growing coastal population density. An effective methodology to assess hurricane wind and surge hazard risks to coastal bridges under changing climate conditions is proposed. The influence of climate change scenarios on hurricane intensity and frequency is explored. A framework that couples the hurricane tracking model (consisting of genesis, track, and intensity) with a height-resolving analytical wind model and a newly developed machine learning-based surge model is used for risk assessment. The proposed methodology is applied to a coastal bridge to obtain its traffic closure rate resulting from the observed (historical) and future (projected) hurricane winds and storm surges, demonstrating the effects of changing climate on the civil infrastructure in a hurricane-prone region

Geospatial environments for hurricane risk assessment: Applications to situational awareness and resilience planning in New Jersey

Mitigation of losses due to coastal hazards has become an increasingly urgent and challenging problem in light of rising seas and the continued escalation of coastal population density. Unfortunately, stakeholders responsible for assuring the safety of these coastal communities are not equipped with the engineering research community’s latest tools for high-fidelity risk assessment and geospatial decision support. In the event of a hurricane or nor’easter, such capabilities are exceptionally vital to project storm impacts on critical infrastructure and other municipal assets and to inform preemptive actions that can save lives and mitigate property damage. In response, a web-based visualization environment was developed using the GeoNode content management system, informed by the needs of municipal stakeholders. Within this secure platform, registered users with roles in planning, emergency management and first response can simulate the impact of hurricanes and nor’easters using the platform’s storm Hazard Projection (SHP) Tool. The SHP Tool integrates fast-to-compute windfield models with surrogate models of high-fidelity storm surge and waves to rapidly simulate user-defined storm scenarios, considering the effects of tides, sea level rise, dune breaches and track uncertainty. In the case of a landfalling hurricane, SHP tool outputs are automatically loaded into the user’s dashboard to visualize the projected wind, storm surge and wave run-up based on the latest track information published by the National Hurricane Center. Under either use case, outputs of the SHP Tool are visualized within a robust collaborative geospatial environment supporting the seamless exploration of centralized libraries of geographic information system (GIS) data from federal, state, county and local authorities, with tools to add user-supplied annotations such as notes or other geospatial mark-ups. This paper will overview the development and deployment of this platform in the State of New Jersey, detailing the cyberinfrastructure design and underlying computational models, as well as the user stories that inspired the platform’s functionalities and interfaces. The study concludes with reflections from the process of piloting this project with stakeholders at the state and municipal level to support more risk-responsive and data-informed decision making