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

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

ISLAND: Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator

Cloud occlusion is a common problem in the field of remote sensing, particularly for thermal infrared imaging. Remote sensing thermal instruments onboard operational satellites are supposed to enable frequent and high-resolution observations over land; unfortunately, clouds adversely affect thermal signals by blocking outgoing longwave radiation emission from Earth's surface, interfering with the retrieved ground emission temperature. Such cloud contamination severely reduces the set of serviceable thermal images for downstream applications, making it impractical to perform intricate time-series analysis of land surface temperature (LST). In this paper, we introduce a novel method to remove cloud occlusions from Landsat 8 LST images. We call our method ISLAND, an acronym for Informing Brightness and Surface Temperature Through a Land Cover-based Interpolator. Our approach uses thermal infrared images from Landsat 8 (at 30 m resolution with 16-day revisit cycles) and the NLCD land cover dataset. Inspired by Tobler's first law of Geography, ISLAND predicts occluded brightness temperature and LST through a set of spatio-temporal filters that perform distance-weighted spatio-temporal interpolation. A critical feature of ISLAND is that the filters are land cover-class aware, making it particularly advantageous in complex urban settings with heterogeneous land cover types and distributions. Through qualitative and quantitative analysis, we show that ISLAND achieves robust reconstruction performance across a variety of cloud occlusion and surface land cover conditions, and with a high spatio-temporal resolution. We provide a public dataset of 20 U.S. cities with pre-computed ISLAND thermal infrared and LST outputs. Using several case studies, we demonstrate that ISLAND opens the door to a multitude of high-impact urban and environmental applications across the continental United States.Comment: 22 pages, 9 figure

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