Neural Networks for Estimating Storm Surge Loads on Storage Tanks

Failures of aboveground storage tanks (ASTs) during past storm surge events have highlighted the need to evaluate the reliability of these structures. To assess the reliability of ASTs, an adequate estimation of the loads acting on them is first required. Although finite element (FE) models are typically used to estimate storm surge loads on ASTs, the computational cost of such numerical models can prohibit their use for reliability analysis. This paper explores the use of computationally efficient surrogate models to estimate storm surge loads acting on ASTs. First, a FE model is presented to compute hydrodynamic pressure distributions on ASTs subjected to storm surge and wave loads. A statistical sampling method is then employed to generate samples of ASTs with different geometries and load conditions, and FE analyses are performed to obtain training, validation, and testing data. Using the data, an Artificial Neural Network (ANN) is developed and results indicate that the trained ANN yields accurate estimates of hydrodynamic pressure distributions around ASTs. More importantly, the ANN model requires less than 0.5 second to estimate the hydrodynamic pressure distribution compared to more than 30 CPU hours needed for the FE model, thereby greatly facilitating future sensitivity, fragility, and reliability studies across a broad range of AST and hazard conditions. To further highlight its predictive capability, the ANN is also compared to other surrogate models. Finally, a method to propagate the error associated with the ANN in fragility or reliability analyses of ASTs is presented.The authors acknowledge the financial support of the National Science Foundation under award #1635784. The first author was also supported in part by the Natural Sciences and Engineering Research Council of Canada. The authors thank Prof. Clint Dawson for providing the ADCIRC+SWAN results. The computational resources were provided by the Big-Data Private-Cloud Research Cyberinfrastructure MRI-award funded by NSF under grant CNS-1338099 and by Rice University. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors

Seismic Resilience of a Rail-Truck Intermodal Freight Network

This study introduces a framework for seismic resilience assessment of rail-truck intermodal freight networks. Although highways constitute the leading mode of freight transport in terms of value and tonnage, railroads primarily support efficient long-haul transport, leading the way in terms of ton-miles of freight traffic. Disruptions to rail and highway infrastructure from hazards such as earthquakes can have distinct impacts on intermodal transport of goods at various spatial and temporal scales. In this study, a framework is proposed for evaluating the temporal evolution of intermodal network resilience, building on past research on performance of intermodal freight networks under disruption. The generic framework is capable of accounting for various costs associated with transporting a freight shipment from its designated origin to its destination. In this study, two simple applications of the framework are shown, in terms of the value weighted connectivity and the value weighted inverse travel distance, the formulations for which are explained in relevant sections. The proposed framework facilitates the estimation of quantities such as overall network throughput at various stages of recovery, which can be used by economists to study the corresponding effects on local and nationwide economy.This study is based on research supported by the Center for Risk-Based Community Resilience Planning and its financial support is gratefully acknowledged. The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellencethe Center is funded through a cooperative agreement between the U.S. National Institute of Science and Technology and Colorado State University (NIST Financial Assistance Award Number: 70NANB15H044). The views expressed are those of the authors/presenters, and may not represent the official position of the National Institute of Standards and Technology or the US Department of Commerce

Seismic Reliability Assessment of Aging Highway Bridge Networks with Field Instrumentation Data and Correlated Failures. I: Methodology

The state-of-the-practice in seismic network reliability assessment of highway bridges often ignores bridge failure correlations imposed by factors such as the network topology, construction methods, and present-day condition of bridges, amongst others. Additionally, aging bridge seismic fragilities are typically determined using historical estimates of deterioration parameters. This research presents a methodology to estimate bridge fragilities using spatially interpolated and updated deterioration parameters from limited instrumented bridges in the network, while incorporating the impacts of overlooked correlation factors in bridge fragility estimates. Simulated samples of correlated bridge failures are used in an enhanced Monte Carlo method to assess bridge network reliability, and the impact of different correlation structures on the network reliability is discussed. The presented methodology aims to provide more realistic estimates of seismic reliability of aging transportation networks and potentially helps network stakeholders to more accurately identify critical bridges for maintenance and retrofit prioritization

Nonlinear dynamic analysis and seismic fragility assessment of a corrosion damaged integral bridge

Purpose In this paper the impact of corrosion of reinforcing steel in RC columns on the seismic performance of a multi-span concrete integral bridge is explored. A new constitutive model for corroded reinforcing steel is used. This model simulates the buckling of longitudinal reinforcement under cyclic loading and the impact of corrosion on buckling strength. Cover concrete strength is adjusted to account for corrosion induced damage and core concrete strength and ductility is adjusted to account for corrosion induced damage to transverse reinforcement. This study evaluates the impact which chloride induced corrosion of the reinforced concrete columns on the seismic fragility of the bridge. Fragility curves are developed at a various time intervals over the lifetime. The results of this study show that the bridge fragility increases significantly with corrosion. Design/methodology/approach This paper firstly evaluates the impact which chloride induced corrosion of the columns has on bridge fragility. Finally, fragility curves are developed at various time intervals over the lifetime of the bridge. The results of this study show that the bridge fragility increases significantly with corrosion. Findings 1) It was found that columns dominate the system fragility at all levels of deterioration. Therefore, it highlights the importance of good column design in terms of both seismic detailing and durability for this integral bridge type. 2) In terms of foundation settlement coupled with corrosion, it was found that settlements on the order of the discrete levels adopted for this study increased the system fragility at the slight, moderate and extensive damage states but their impact at the complete damage states is negligible. 3) Ageing considerations are currently neglected in widespread regional risk assessment and loss estimation packages for transport infrastructure. The result of this study provides a methodology that enables bridge managers and owners to employ in seismic risk assessment of existing aging bridges. Originality/value The modelling technician developed in this paper considers the impact of detailed corrosion damaged of RC column on nonlinear dynamic response and fragility of a corroded integral bridge under earthquake loading. The current modelling technique is the most comprehensive 3D fibre element model for seismic analysis and risk assessment of corroded bridges. </jats:sec

Seismic Damage Accumulation of Highway Bridges in Earthquake Prone Regions

Civil infrastructures, such as highway bridges, located in seismically active regions are often subjected to multiple earthquakes, such as multiple main shocks along their service life or main shock-aftershock sequences. Repeated seismic events result in reduced structural capacity and may lead to bridge collapse causing disruption in normal functioning of transportation networks. This study proposes a framework to predict damage accumulation in structures under multiple shock scenarios after developing damage index prediction models and accounting for the probabilistic nature of the hazard. The versatility of the proposed framework is demonstrated on a case study highway bridge located in California for two distinct hazard scenarios: a) multiple main shocks along the service life, and b) multiple aftershock earthquake occurrences following a single main shock. Results reveal that in both cases there is a significant increase in damage index exceedance probabilities due to repeated shocks within the time window of interest

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

The Eleventh and Twelfth Data Releases of the Sloan Digital Sky Survey: Final Data from SDSS-III

The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All of the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 deg2 of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include the measured abundances of 15 different elements for each star. In total, SDSS-III added 5200 deg2 of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 deg2; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra. \ua9 2015. The American Astronomical Society

Evaluating the Effects of SARS-CoV-2 Spike Mutation D614G on Transmissibility and Pathogenicity.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant

Genomic assessment of quarantine measures to prevent SARS-CoV-2 importation and transmission

Mitigation of SARS-CoV-2 transmission from international travel is a priority. We evaluated the effectiveness of travellers being required to quarantine for 14-days on return to England in Summer 2020. We identified 4,207 travel-related SARS-CoV-2 cases and their contacts, and identified 827 associated SARS-CoV-2 genomes. Overall, quarantine was associated with a lower rate of contacts, and the impact of quarantine was greatest in the 16–20 age-group. 186 SARS-CoV-2 genomes were sufficiently unique to identify travel-related clusters. Fewer genomically-linked cases were observed for index cases who returned from countries with quarantine requirement compared to countries with no quarantine requirement. This difference was explained by fewer importation events per identified genome for these cases, as opposed to fewer onward contacts per case. Overall, our study demonstrates that a 14-day quarantine period reduces, but does not completely eliminate, the onward transmission of imported cases, mainly by dissuading travel to countries with a quarantine requirement