Search

This paper presents the formulation of a time-dependent seismic fragility format for bridges, as well as new insights into the potential effects of aging and deterioration on seismic vulnerability traditionally neglected in fragility modeling, including joint impacts of multiple component deterioration not investigated to date. The study evaluates the impact of lifetime exposure to chlorides from deicing salts on the seismic performance of multispan continuous highway bridges, considering corrosion of reinforced concrete columns and steel bridge bearings. The components' degradation and their influence on seismic response are illustrated through three-dimensional nonlinear dynamic analysis. A full probabilistic analysis accounting for variation in bridge, ground motion, and corrosion parameters is conducted to develop time-dependent seismic fragility curves. These fragility curves indicate the evolving potential for component and system damage under seismic loading considering time-dependent corrosion-induced deterioration. The results indicate that while corrosion may actually decrease the seismic vulnerability of some components, most critical components suffer an increase in vulnerability. Quadratic models depicting the change in lognormal seismic fragility parameters are proposed to capture the time-dependent effect of aging on the fragility of the bridge system. Overall, the seismic vulnerability significantly increases throughout the lifetime of the representative bridge geometry, with a 32% shift in the median value of complete damage fragility near the end of the bridge's life. [ABSTRACT FROM AUTHOR]

Abstract: Bridges constitute key elements of the nation’s infrastructure and are subjected to considerable threats from natural hazards including seismic events. A range of potential bridge retrofit measures may be used to mitigate seismic damage in deficient bridges, and help to avoid associated social and economic losses. However, since resources are often limited for investment in seismic upgrade, particularly in regions of large but infrequent events, a risk-based approach for evaluating and comparing the cost effectiveness of different mitigation strategies is warranted. This paper illustrates a method for evaluating the best retrofits for non-seismically designed bridges based on seismic life-cycle costs and cost–benefit analysis. The approach integrates probabilistic seismic hazard models, fragility of as-built and retrofitted bridges for a range of damage states, and associated costs of damage and retrofit. The emphasis on life-time performance and benefits, as opposed to initial retrofit cost alone, not only permits risk-wise investment, but also helps to align upgrade actions with highway agency missions for sustainable infrastructure. An application of the seismic life-cycle cost–benefit analysis is conducted for four representative bridges of different classes, and seven different retrofit options ranging from the use of seat extenders, to isolation bearings, to steel jackets. The same bridges are evaluated located at three sites of varying seismicity: Caruthersville, Missouri; Charleston, South Carolina; and Los Angeles, California. A summary of the proposed optimal retrofit measures for the case-study bridges and locations is presented. The results show that not only do the magnitude of the expected losses and resulting retrofit cost–benefit differ by location, but the most cost-effective retrofit for a particular bridge may vary as well due to local seismic hazard characteristics and the effect of retrofit at different damage levels. [Copyright &y& Elsevier]

Abstract: Steel girder bridges are common bridges in North America which have exhibited considerable vulnerabilities in past earthquake events. This paper conducts nonlinear time history analysis using detailed three-dimensional models of typical multi-span simply supported and multi-span continuous steel girder bridges to evaluate the effectiveness of various retrofit strategies. Restrainer cables, steel jackets, shear keys, and elastomeric isolation bearings are assessed for their influence on the variability and peak longitudinal and transverse response of critical components in the bridges. The results indicate that different retrofit measures may be more effective for each class of bridges. For example, the restrainer cables are effective for the multi-span simply supported (MSSS) bridge, yet not the multi-span continuous (MSC) bridge; shear keys improve the transverse bearing response in the MSC bridge but are not effective in the MSSS bridge which exhibits less transverse vulnerability; and elastomeric bearings improve the response of the vulnerable columns in both the MSSS and MSC bridges yet lead to increased abutment demands in the MSC bridge. The study reveals that while a retrofit may have a positive influence on the targeted component, other critical components may be unaffected or negatively impacted. This lends support to vulnerability assessments that consider the impact of retrofit on system vulnerability reflecting the contribution of multiple components. [Copyright &y& Elsevier]

Probabilistic seismic demand models are a common and often essential step in generating analytical fragility curves for highway bridges. With these probabilistic models being traditionally conditioned on a single seismic intensity measure (IM), the degree of uncertainty in the models is dependent on the IM used. Selection of an optimal IM for conditioning these demand models is not a trivial matter and has been the focus of numerous studies. Unlike previous studies that consider a single structure for IM selection, this study evaluates optimal IMs for use when generating probabilistic seismic demand models for bridge portfolios such as would be found in HAZUS-MH. Selection criteria such as efficiency, practicality, sufficiency, and hazard computability are considered in the selection process. A case study is performed considering the multi-span simply supported steel girder bridge class. Probabilistic seismic demand models are generated considering variability in the geometric configurations and material properties, using two suites of ground motions-one synthetic and one recorded motion suite. Results show that of the 10 IMs considered, peak ground acceleration (PGA) and spectral acceleration at the fundamental period are the most optimal for the synthetic motions, and that cumulative absolute velocity is also a close contender when using recorded motions. However, when hazard computability is considered, PGA is selected as the IM of choice. Previous studies have shown that spectrally based quantities perform better than PGA for a given structure, but the findings of this study indicate that when a portfolio of bridges is considered, PGA should be used. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

• Refined Multivariate Return Period (MRP)-based Ground Motion Selection with Improved Computational Efficiency. • Extended applicability of the MRP-based ground motion selection to seismic risk assessment (SRA). • Demonstration of the merits of the MRP-based ground motion selection in different use cases of SRA. A multivariate return period (MRP)-based ground motion selection methodology was recently proposed to identify hazard-consistent ground motions by holistically incorporating the joint rate of exceedance of vector conditioning intensity measures (IMs). Despite these advances, the high computational cost involved in target spectra identification hinders the use of high-dimensional vector conditioning IMs and especially for large return periods, and thus may limit the practical application of this ground motion selection methodology. In this paper, we introduce a two-step adaptive refinement procedure to substantially alleviate the computational hurdle, while still offering reliable target spectra selection. The refined MRP-based ground motion selection then further facilitates the development of a MRP-based seismic risk assessment (SRA) framework to extend its applicability into risk-based assessment. The efficacy and computational efficiency of the proposed refinement are evaluated through comparison with the original unrefined approach, and the unbiasedness and variability of the risk estimates under different use cases of SRA are thoroughly examined and validated through case-study analyses. Aiming at facilitating more confident and reliable seismic risk estimates, implications and advantages of leveraging the MRP-based ground motion selection in SRA are elaborated. [ABSTRACT FROM AUTHOR]

Important advances have been made in the methodologies for assessing the safety of dams, resulting in the review and modification of design guidelines. Many existing dams fail to meet these revised criteria, and structural rehabilitation to achieve the updated standards may be costly and difficult. To this end, probabilistic methods have emerged as a promising alternative and constitute the basis of more adequate procedures of design and assessment. However, such methods, in addition to being computationally expensive, can produce very different solutions, depending on the input parameters, which can greatly influence the final results. Addressing the existing challenges of these procedures to analyze the stability of concrete dams, this study proposes a probabilistic-based methodology for assessing the safety of dams under usual, unusual, and extreme loading conditions. The proposed procedure allows the analysis to be updated while avoiding unnecessary simulation runs by classifying the load cases according to the annual probability of exceedance and by using an efficient progressive sampling strategy. In addition, a variance-based global sensitivity analysis is performed to identify the parameters most affecting the dam stability, and the parameter ranges that meet the safety guidelines are formulated. It is observed that the proposed methodology is more robust, more computationally efficient, and more easily interpretable than conventional methods. [ABSTRACT FROM AUTHOR]

• Proposed novel multivariate seismic surrogate demand models (MvSDMs). • Compared the predictive performance of different MvSDMs. • Evaluated the validity of the MvSDMs against benchmark system fragilities. • Identified promising systematic trend model and error covariance model for MvSDMs. Probabilistic seismic risk assessment of complex multi-response structural systems requires accurate estimations of multiple engineering demand parameters (EDPs) that are related to different failure modes. One conventional way is developing multiple separate univariate seismic demand models for each of the EDPs. However, such an approach lacks sufficient consideration of the correlations among different EDPs and also requires repetitive model tuning and training. Another alternative, which has not yet been extensively studied, is to develop a single multivariate surrogate demand model (MvSDM) that is able to jointly provide the estimates for all the EDPs. To facilitate more accurate and realistic seismic demand modeling of multi-response structural systems, the present study conducts a comprehensive investigation of different MvSDMs, which are constituted by a systematic trend model that characterizes the mean response hypersurface, and a covariance model that quantifies the correlated model errors. Three types of systematic trend models including multivariate linear regression (MvLR), partial least squares regression (PLSR) and artificial neural network (ANN); along with three model error covariance estimation methods including joint probabilistic demand model (JPSDM) method, sample regression residual covariance (SCOV) method and multi-output Gaussian process regression (MOGP), are considered. The influence of trend model and error covariance model selection on seismic demand and fragility modeling is then studied based on a typical three-span concrete-girder highway bridge structure in the Central and Southeastern US. The results suggest that kernel PLSR and ANN as the systematic trend models, and the SCOV method as the error covariance model are promising alternatives that can contribute to accurate and reliable multivariate surrogate demand modeling yet with good computational efficiency. [ABSTRACT FROM AUTHOR]

The estimation of wind pressure on building envelopes is critical for accurate risk and resilience evaluation of coastal structures. It is possible and convenient sometimes to interpolate existing wind-tunnel tests for the estimation of untested buildings. However, the complex bluff-body aerodynamics pose a challenge for wind tunnel test interpolation. Specifically, the nonlinear interpolation needs to be conducted simultaneously on coordinate and wind and building condition, which cannot be accomplished using conventional interpolation methods. To address this issue, a novel machine learning approach called Conditional Neural Network (CondNN) is developed. In the CondNN, a two-level architecture is utilized to interpolate existing test data for a certain building (i.e., the lower level takes the coordinate on building envelope as input) and across different buildings (i.e., the upper level takes the wind and building condition as input). Wind tunnel measurements are further used for verifying the performance of the proposed conditional natural network for wind pressure estimation. The CondNN not only paves a path toward the fast fragility evaluation of a diverse set of buildings exposed to coastal wind hazards, but also illuminates the use of machine learning techniques in a broader spectrum of engineering applications. • A novel machine learning approach, i.e., Conditional Neural Network (CondNN), is developed for the estimation of wind loads. • A two-level architecture is used in CondNN to achieve the simultaneous intra- and inter-model interpolation. • Wind tunnel tests are used for verifying the performance of the CondNN for wind pressure estimation. • The CondNN paves a path toward the fast fragility evaluation of a diverse set of buildings exposed to coastal wind hazards. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Proposed methodology for the fragility and risk assessments of storage tanks. • Addressed and highlighted the importance of the multi-hazard nature of storm events. • Highlighted the benefits of surrogate models to facilitate fragility and risk assessments. • Performed scenario-based and probabilistic risk assessments for an industrial complex. Comprehensive tools to assess the performance of aboveground storage tanks (ASTs) under multi-hazard storm conditions are currently lacking, despite the severe damage suffered by ASTs in past storms resulting in the release of hazardous substances. This paper presents a rigorous yet efficient methodology to develop fragility models and perform risk assessments of ASTs subjected to combined surge, wave, and wind loads. Parametrized fragility models are derived for buckling and dislocation from the ground. The buckling strength of ASTs is assessed using finite element analysis, while the stability against dislocation is evaluated using analytical limit state functions with surrogate modeling-based load models. Scenario and probabilistic risk assessments are then performed for a case study region by convolving the fragility models with hazard models. Results demonstrate that the derived fragility models are efficient tools to evaluate the performance of ASTs in industrial regions. Insights obtained from the fragility and risk assessments reveal that neglecting the multi-hazard nature of storms, as existing studies have done, can lead to a significant underestimation of vulnerability and risks. This paper also highlights how using surrogate model techniques can facilitate and reduce the computational complexity of fragility and risk assessments, particularly in multi-hazard settings. [ABSTRACT FROM AUTHOR]

• Developed and validated a numerical model to estimate wave loads on storage tanks. • Performed dynamic and static buckling analysis of storage tanks during storm surge events. • Wave loads significantly affect the buckling behavior of tanks subjected to surge. • Dynamic effects of wave loads are negligible to estimate the buckling critical load factor. • Static buckling analysis provide reasonable estimates of the buckling critical load factor. This study investigates the buckling behavior of aboveground storage tanks (ASTs) subjected to storm surge and wave loads during hurricane events and explores the importance of dynamic effects on the buckling behavior. First, a computational fluid dynamics model is developed to estimate water pressures on ASTs subjected to wave loads. The modeling assumptions of this model are also validated with experimental data. Next, a methodology is presented to perform dynamic buckling analysis of ASTs subjected to surge and wave loads by adapting procedures commonly used for ASTs subjected to wind or seismic loads; the methodology is illustrated with a case study AST located in the Houston Ship Channel. For comparison, static buckling analyses are also performed to determine the significance of dynamic effects. Lastly, design of experiments principles and regression analyses are employed to investigate the effects of varying AST and loading parameters on the buckling behavior and the relative importance of dynamic effects. Results indicate that wave loads can significantly affect the buckling behavior of ASTs subjected to storm surge and need to be considered, while the dynamic effects induced by waves have a negligible influence on the buckling strength of ASTs. Simpler and computationally inexpensive static buckling analysis provides reasonable estimates for ASTs subjected to surge and waves. However, dynamic buckling analysis might still be required if the objective is to assess the post-buckling behavior of ASTs subjected to waves, rather than only to estimate the critical load. [ABSTRACT FROM AUTHOR]

Highlights • The tip-over event of a generic vertical concrete dry cask is studied with a probabilistic approach. • Metamodels are developed for the canister maximum strain and overpack maximum acceleration. • Fragility and risk analysis is performed for the selected responses. • Effect of aging and variation of design parameters on the fragilities is explored. Abstract Vertical concrete dry casks, which provide an interim solution for the storage of spent nuclear fuel, are vulnerable to tip-over due to lateral loads. This paper explores the probabilistic structural response of a representative dry cask subjected to tip-over impact loads and aging effects. Considering concrete and soil material properties, thickness of the pad, angular velocity at the onset of tip-over, and age of the cask as variables, 200 configurations of the tip-over problem are generated by Latin Hypercube Sampling. To consider aging and the associated temporal variations, material properties of the steel parts vary in the generated configurations due to temperature variations. The generated tip-over scenarios are analyzed by numerical models developed and validated in a commercial finite element analysis program. Machine learning algorithms such as polynomial response surface models, multivariate adaptive regression splines, regression trees, support vector machines, Gaussian processes and neural networks are trained on datasets provided by the finite element analysis to develop metamodels for the maximum strain of the canister and maximum acceleration of the concrete overpack. These metamodels offer statistical approximating functions that enable efficient impact fragility analysis and evaluation of the sensitivity of the fragility to age, concrete material properties of the cask and pad, and pad thickness. The effect of aging due to alkali-silica reaction on the strain fragility is also studied, and recommendations regarding the design of vertical concrete dry casks are provided. Risk analysis is performed to estimate the dry storage cask's risk of failure (i.e., probability of limit state exceedance) and to uncover the effect of age and design parameters on the probability of strain and acceleration exceeding predefined limits. The risk analysis shows that the probability of canister failure in the tip-over event is negligible. That is, despite expected cracks in the concrete overpack, the structural integrity of the dry cask system subjected to the tip-over impact loads is retained. [ABSTRACT FROM AUTHOR]

Severe storms and associated flooding pose a significant risk to urban mobility. Consequently, 40 to 63% of flood-related deaths are linked to roadway-related incidents in developed countries. The dynamic nature of flooding and the lack of real-time information make it challenging to sense flooding and its impact on roadways. Hence, existing state-of-the-art methods fall short of providing a robust, reliable, and affordable tool to facilitate situational awareness during storms. Such a tool is indispensable to aid emergency response, especially considering the potential increase in risk to flood exposure due to climate change and other factors. This study addresses this need by providing an open-source framework that couples real-time rainfall data, a physics-based flood model, and network and spatial analyses to sense real-time flood impact on the road transportation system. Case studies using three recent storms in Houston, Texas demonstrate the framework's ability to provide vehicle-class specific roadway conditions for even minor roads and residential streets—a problem existing approaches struggle with. Aside from road-link conditions, the framework can also estimate network-level flood impacts, such as identifying regions without access to critical facilities like hospitals, giving decision-makers a more holistic view of network performance. Further, the framework is interoperable with existing situational awareness tools and could augment their ability to sense road conditions during flooding. Finally, the proposed framework can equip flood-prone communities and emergency responders with reliable and accessible situational awareness content using open-source tools and data to promote safer mobility during flooding—a key goal of intelligent transportation systems. • A new situational awareness framework for real-time sensing of flood impact on roads. • Estimates road conditions using radar data, a flood model, and network analysis. • Vehicle-specific roadway status to aid emergency response and promote safer mobility. [ABSTRACT FROM AUTHOR]

Fragility curves that can be applicable for vulnerability modeling of various configurations of structures, such as bridges, are required to facilitate regional seismic risk assessment. However, it is cumbersome and time-consuming to develop unique fragility curves for each structure across a regional portfolio. One strategy that has been adopted to address this challenge is to group bridges into classes with similar design or structural performance. Traditionally, this grouping has been performed based on a relatively subjective identification of sub-classes. However, such an identification leads to a number of bridge classes with unwarranted grouping. To overcome this limitation, a new grouping technique based on an analysis of covariance on probabilistic seismic demand models is suggested in this paper. The proposed analysis of covariance based grouping helps to identify important attribute-related parameters and to create distinct bridge sub-classes. The effectiveness of the proposed approach is demonstrated in this paper through case studies of four concrete box-girder bridges in California. This is the first systematic approach to sub-binning bridge classes for the regional risk assessment. The results suggest that the proposed grouping method significantly reduces bridge sub-classes from all possible sub-class combinations. The proposed method is relevant and can be applicable to other bridge types. [ABSTRACT FROM AUTHOR]

Flood-induced scour is a common phenomenon that causes the loss of lateral support at bridge foundations. However, the impact of scour is usually ignored when assessing the anticipated response of bridges under earthquake loads. This study quantifies the effect of scour on the dynamic behavior and seismic performance of reinforced concrete (RC) bridges. Additionally, this study investigates potential structural design alternatives to mitigate damage from earthquake exposure given scour conditions. Since the structural periods corresponding to the first few vibration modes increase with the increase of scour depth, this study finds that the sensitivity of the periods to the scour depth varies for different types of bridges. Also, bridge variety determines whether scour has a beneficial or detrimental effect on the seismic column fragility. In addition, scour may amplify the potential seismic damage of the bridge via transferring damage from the column to the pile foundations, or increasing the probability of deck unseating. Regarding strategies to manage detrimental scour effects, this study finds that increasing the foundation stiffness is effective for reducing the seismic damage of scoured bridges, while increasing the foundation depth only has a minor effect. Also, the foundation type has great impact on the seismic performance of scoured bridges. The findings of this study can be used to guide the seismic design of new bridges or the retrofit of existing bridges in transportation networks along flood-prone regions. [ABSTRACT FROM AUTHOR]

Highlights: [•] Seismic performance of yielding shear panel device (YSPD) is evaluated. [•] Probabilistic performance evaluation techniques are used. [•] Uncertainties of seismic demand and structural capacity are considered. [•] Effect of varying seismicity is reflected through considering multiple sites. [•] Various size and configuration of YSPDs are utilized for the analysis. [Copyright &y& Elsevier]

Abstract: Vulnerability estimation as well as quantitative and qualitative assessment of the seismic risk to highway bridges is crucial in obtaining reliable estimates of the resilience of highway transportation systems. Although previous studies have evaluated the seismic response and fragility of various bridge classes common to the central and southeastern United States, there is very little research that explores the differences in performance of bridge classes built with and without seismic detailing. This paper addresses this gap by investigating the influence of seismic detailing of four multispan bridge classes on the seismic performance, as well as the failure probability through the development and comparison of fragility curves. The primary differences between seismically and non-seismically designed bridges are the column reinforcement details and bridge bearings. Component and system fragility curves are developed to provide insight into the difference in performance of non-seismically and seismically designed bridges and are compared with those currently found in HAZUS-MH. Confidence bounds are finally developed to characterize the uncertainty about the proposed median fragility curves for the bridge classes. Bridge classes with seismic provisions incorporated in their design tend to have reduced confidence bandwidth at higher damage states. Significant improvement in performance is seen in all bridge classes considered in this study when seismic design and details are used. [Copyright &y& Elsevier]

This study develops the probabilistic seismic response and capacity models for the broad collection of standard pile designs that are routinely incorporated into bridges in California. A review of the state bridge inventory indicates considerable variations in design details for different standard pile types used across multiple eras. For each pile type, fiber-section-based pile models attached with p-y soil springs are built to incorporate nonlinear behaviors of soil materials, a wide array of heterogeneous soil profiles, full-range damage states of piles, and realistic connection details between piles and footings (i.e., pile caps). Moreover, force-displacement responses of pile-soil systems under a large number of pushover analyses are regressed as response models consisting of five parameters [termed as response five parameter (R5P) models], which can capture all essential behaviors of laterally-loaded piles. Capacity damage states and limit state models are defined for different pile types by linking pile global responses to fiber-scale material behaviors at plastic hinge locations. Procedures are further provided to expand R5P models to pile foundation models at the regional scale, taking into account the pile group effect and capacities of pile cap backfills. R5P models for all distinct pile types are further summarized in an Excel workbook to facilitate their practical implementations. In general, this study provides a comprehensive and consistent set of response and capacity models to quantify the seismic damage potential of regional pile foundations, as well as to capture their dynamic interplays with other crucial bridge components, such as columns and abutment components. The proposed pile models are expected to significantly enhance the existing modeling capability toward improved seismic risk assessment of California's bridge infrastructure. [ABSTRACT FROM AUTHOR]

• Development of parameterized fragility models for port structures subjected to storm surge and waves. • Inclusion of time-dependent models for capturing structural aging and deterioration. • Comparison of surrogate models for classification problems in coastal fragility modeling. • Applicability of the developed models to region vulnerable to hurricanes. This study proposes a methodology for the development of parameterized fragility models for open type port structures subjected to hurricane-induced storm surge and wave loading, while also considering the potential influences of time-dependent aging and deterioration. The proposed framework uses a simulation-based strategy to propagate uncertainty in loads, geometrical configurations, material properties and capacities associated with different exposure conditions and failure modes (i.e. uplift, flexure, shear). Alternative classification-type statistical learning methods are tested to derive parameterized fragility models for each failure mode in order to evaluate limit state exceedance probability given a vector of deterministic or probabilistic parameters that define the structure and exposure conditions. Such parameterized coastal fragility models enable rapid application to a portfolio of aging port structures to explore the impact of current and future storm conditions, as illustrated via case study application for two terminals located in the Houston Ship Channel. The results reveal the dominant failure mode, spatial distribution of failure probabilities for structures within and across terminals, and influence of aging on surge and wave vulnerability of port structures. [ABSTRACT FROM AUTHOR]

• Parameterized fragility models for individual spans of different bridge classes. • Inclusion of spatial variability of wave loads and variations in structural characteristics. • Bridge system fragility assessment framework for system reliability assessment. • Investigation of bridge performance across a region for different storm scenarios. Past hurricanes in the United States have highlighted the structural vulnerability of bridges exposed to storm surge and wave loads, triggering the development of fragility models. These models are essential for estimating the likelihood of structural failures in bridges during extreme events to support risk mitigation and resilience enhancement. However, the literature still lacks fragility models that can accommodate the effects of spatial variability of surge and wave loads and bridge characteristics like superstructure type and span slope. In addition, the presence of different types of superstructure within the same bridge necessitates the development of fragility models which can handle such variations. In this light, first, this study develops fragility models for individual spans of different bridge classes (concrete girder, steel girder, slab, box girder) considering spatial variability of wave loads and variations in structural characteristics. Herein, nonlinear dynamic analyses are employed for calculating the structural response and stepwise logistic regression is used for deriving the predictive fragility models. Then, a bridge system fragility assessment framework is proposed for system reliability assessment of the entire bridge, which accounts for different types of superstructures within the bridge and partially correlated span failures. In order to demonstrate the application of the fragility models and the system fragility assessment framework for regional level risk mitigation, the performance of bridges in the Houston-Galveston region is evaluated for two storm scenarios. The results show that the system failure probability obtained using no correlated span failure assumption is close to the actual failure probability obtained using the proposed approach highlighting the effects of correlations (or the lack of) on the system fragility. [ABSTRACT FROM AUTHOR]

This article investigates the effect of alkali–silica reaction (ASR) damage on the structural performance of reinforced concrete (RC) dry casks for spent nuclear fuel (SNF) storage. Dry casks have an initial licensed lifetime of 20 or 40 years with the possibility of an extension of another 40 years. Due to lack of a permanent storage facility in the United States, an extension of the service life of dry casks that are currently in use and that were designed for shorter service lives is a critical step. Deterioration of concrete due to ASR is a major concern as has been reported for some nuclear facilities. In this study, the effect of ASR aging on the tip-over response of a vertical RC cask is experimentally studied for the first time using 1/3-scale physical models. An accelerated aging method was used to represent the long-term degradation of the structure. A series of non-destructive tests were performed to monitor the aging progress. At the end of the aging period, the structural performance of the casks is evaluated experimentally under tip-over impact. The results are compared with data obtained from a tip-over test on an undamaged cask. In addition, a detailed finite element model of the cask is developed to simulate the tip-over impact in aged conditions. [ABSTRACT FROM AUTHOR]

• Framework to evaluate the accessibility of NaTech accidents during storm surge events. • Storage tank fragility models to identify locations and likelihood of NaTech events. • Bridge fragility models and GIS analysis to determine road network disruptions. • Probabilistic network analysis to quantify time-evolving accessibility metrics. • Benefits of framework are demonstrated for a case study region in Texas. Recent hurricane events have exposed the susceptibility of petrochemical facilities to severe transportation network disruptions due to flooding or storm surge. Network disruptions can result in cascading impacts or amplify the consequences of damage to petrochemical infrastructure due to delayed emergency response and limited access to the site. This study presents a scenario-based framework to assess the accessibility of petrochemical facilities by emergency responders and workers during storm surge events. First, the framework couples storm surge modeling with aboveground storage tank fragility models to determine the locations where natural hazard-triggered technological (NaTech) events could occur. Then, storm surge modeling is coupled with bridge fragility models and geographic system analysis to evaluate the potential for network disruptions such as bridge failures and road inundations. Finally, probabilistic network analyses are performed to evaluate the time-evolving accessibility of NaTech sites to emergency responders and facility workers. As a proof of concept, the framework is applied to a case study area. Results for the case study area demonstrate that the proposed framework is a powerful tool to quantify the accessibility of potential NaTech events, facilitate mitigation and emergency activities, and improve the management of critical resources and personnel during and after a storm. [ABSTRACT FROM AUTHOR]

Abstract In performance-based earthquake engineering, probabilistic seismic demand models of structures are essential components that provide probabilistic estimates of earthquake-induced demands as a function of a variable(s) called the ground motion intensity measure (IM). Uncertainties in these models are often dependent on the IM used. Extending from traditional integer order IMs, this study assesses the performance of fractional order (FO, order of α) IMs on the probabilistic seismic demand modeling of extended pile-shaft supported bridges sited in liquefiable and laterally spreading ground. Uncertainties in structural and geotechnical material properties as well as geometric parameters of the bridges are considered in finite element models to achieve comprehensive scenarios. The FO IMs considered include peak ground response (PGR α), cumulative absolute response (CAR α) and its modified version (CAR 5 α), spectral acceleration at 2.0 s for a fractionally damped single degree of freedom (SDF) system (S ad- 20 α) and for a conventional SDF system with fractional response (S ar- 20 α), spectrum intensity for a fractionally damped SDF system (SI dα), as well as for a conventional SDF system with fractional response (SI rα). Metrics such as efficiency, practicality, proficiency and sufficiency are measured to assess the optimal α with respect to different demand parameters. Results show the advantages of FO IMs as they increase confidence in demand models compared to traditional integer order IMs. In particular, the proposed fractional spectrum intensities (SI dα and SI rα) with their optimal α values produce significant improvements in practicality, efficiency and proficiency, while maintaining sufficiency. Therefore, FO IMs can provide more reliable demand models for probabilistic seismic demand analysis of extended pile-shaft supported bridges in liquefiable and laterally spreading ground. Highlights • Novel fractional order (FO) seismic intensity measures (IMs) are proposed. • A coupled bridge-soil-foundation model considering lateral spreading is developed. • The proposed FO IMs show significant advantages compared to traditional IMs. • Optimal fractional orders for the FO IMs are stable for various modeling scenarios. [ABSTRACT FROM AUTHOR]

Highlights • FEMs predict accurate backfill backbone curves against full-scale abutment tests. • Probabilistic backfill models capture soil uncertainty from field investigation. • Height adjustment factors bear matching backbone curves at different heights. • The models capture temporal evolution of backfill behaviors in California bridges. • The models affect the longitudinal demand estimates of various bridge components. Abstract Seismic responses of ordinary highway bridges that feature stiff superstructures have been shown to be strongly affected by abutment-backfill interactions. Seismic risk assessment of these bridges at the regional scale faces the added challenge of having to deal with the large uncertainty in backfill properties. In this regard, the study develops probabilistic backfill models to better quantify the uncertainties in modeling the abutment. First, efforts to establish the validity of the numerical method in predicting the pushover response of backfills are described. Advanced plasticity materials are used in finite element models (FEMs) to simulate sandy and clayey soils that yield consistent backfill force-displacement relationships against full-scale test results. Second, a probabilistic analysis framework is constructed to incorporate soil uncertainties that are identified from field investigations. Statistical moments are extracted from the resultant pushover curves to fully define the probabilistic backfill models, which are verified to bear appropriate uncertainty treatment and reasonable height adjustment factors. Further, statistical analysis tools are used to investigate the influences of different backfill models on the bridge demand estimates of two common bridge classes. The study reveals that backfill models will affect the response estimates of different bridge components in both diaphragm and seat-type abutment bridges. However, probabilistic models shall be especially considered on backfills for the bridge components that are expected to have dominant responses in the longitudinal direction. The proposed backfill models appear to outperform previous deterministic models in predicting realistic bridge responses. The models can be employed in the task of regional seismic assessment of various bridge classes. [ABSTRACT FROM AUTHOR]

One option to mitigate the seismic risk of highway bridges in Quebec is to replace typical elastomeric bearings used there with natural-rubber seismic-isolator devices. To support this alternative, this paper assesses the seismic vulnerability of typical bridge classes retrofitted with seismic-isolator devices through the development of fragility curves. Retrofitted-bridge fragility curves provide a powerful tool to evaluate the impact of a retrofit measure on the performance of different bridge classes. The analytical fragility approach uses nonlinear time-history analysis with 3-D detailed models for typical configurations of highway bridges. Experimental results of square bearings with different sizes and shape factors are used to account for uncertainties related to the mechanical properties of seismic-isolators. Critical load tests are conducted on slender seismic isolation bearings and a finite-element model is calibrated with the test results to define the seismic-isolator limit states. The fragility curves of different key components of the bridge system are compared and the results reveal that seismic isolation is effective significantly in reducing the probability of damage to columns and foundations. Due to insufficient clearance between the superstructure and abutment wing walls, however, the probability of damage in wing walls is increased and the fragility of this component controls the bridge-system fragility for all bridge classes evaluated. Concrete-girder bridges are found to be more vulnerable than steel-girder bridges due to the larger superstructure mass involved in the seismic response. The results from this work can be used to evaluate and select bridge retrofits, and can form the basis for cost-benefit retrofit studies. [ABSTRACT FROM AUTHOR]

More than 46% of the 2672 multiple-span bridges in the Province of Quebec (Canada) are concrete girder bridges. Due to the advanced age and the lack of seismic detailing, these bridges may be vulnerable to earthquake events. In an effort to prevent an interruption of the transportation network, which could be catastrophic for Quebec, a comprehensive research program was conducted whose aims were to assess the seismic vulnerability of typical bridge classes retrofitted with natural rubber seismic isolator devices through the development of fragility curves. The understanding of the impact of various modeling parameters on structural-component responses is the first step in a forward-vulnerability assessment of bridges in Quebec. This paper presents part of a comparative study using detailed three-dimensional nonlinear models that was conducted to assess the longitudinal and transverse responses of multi-span continuous (MSC) and multi-span simply supported (MSSS) concrete bridges in their as-built and retrofitted configurations using different synthetic ground motions (SGM) for the eastern part of Canada. Deterministic responses in terms of column curvature demand, abutment footing deformations, and abutment wall deformations are provided for the longitudinal and transverse directions of bridge models. The influences of various modeling parameters were assessed using analysis of variance (ANOVA) and design-of-experiments (DOE) principles to perform screening of 12 structural parameters. This study revealed that the most important modeling parameters that affect critical components responses of MSC and MSSS bridges are the isolator effective stiffness, the abutment stiffness, and the gap between abutment and deck, in addition to the variation in gross bridge geometry and ground-motion contents. This screening study also gives an indication that the variation in other modeling parameters such as structural damping and skew angle affect the seismic response of MSC and MSSS bridges and should be considered carefully in a seismic-vulnerability assessment for these portfolios of concrete bridges. In addition, it was found that the use of seismic isolation devices effectively reduces the curvature demand placed on columns but has a negative impact on the deformation demand placed on abutment walls. [ABSTRACT FROM AUTHOR]

Damage by severe storms of infrastructure containing chemicals can cause widespread pollution of the atmosphere and nearby bodies of water. Because atmospheric monitoring equipment is inoperable in the periods after these storms, transport and fate modeling approaches are necessary to estimate the regional atmospheric concentrations of evaporated spill material and secondary pollutants from such events. Hypothetical spills from a single storage tank in Houston were used to evaluate the impact of different meteorological scenarios (Hurricanes Harvey in 2017 and Ike in 2008), leaked materials (oils and organic solvents), background chemical conditions, and cloud conditions on simulated air pollution. Due to differences in evaporation rate, downwind oil plumes are predicted to cover a broader region than organic solvent plumes, which remain concentrated along the path of the prevailing wind. Depending on assumptions regarding evaporation, mixing ratios of spilled material of up to 90 parts per million are predicted. Substantial formation of ozone (up to an enhancement of 130 parts per billion) and secondary organic aerosol (up to an enhancement of 30 μg m−3) could occur in the short-term aftermath of these storms within the downwind solvent plumes, with the magnitude dependent on the solar radiation, type of material, and background pollutant level. This highlights the potential vulnerability of residents and workers in downwind regions to evaporated spill materials and their degradation products. • A model was developed to simulate the fate of evaporated spill materials. • Different meteorology, materials, and pollution levels were tested. • Exposure to spilled materials is of significant concern. • Formation of secondary pollution is enhanced within the downwind plumes. [ABSTRACT FROM AUTHOR]

The deterioration and cracking of reinforced concrete (RC) bridges due to the chloride-induced corrosion of steel reinforcement is an inherently time-dependent stochastic phenomenon. In the current practice of bridge management systems, however, the determination of the condition states of deteriorated bridges is highly dependent on the opinion of experienced inspectors. Taking such complexity into account, the current paper presents a new stochastic predictive methodology using a non-homogeneous Markov process, which directly relates the visual inspection data (corrosion rate and crack widths) to the structural vulnerability of deteriorated concrete bridges. This methodology predicts the future condition of corrosion-induced damage (concrete cracking) by linking structural vulnerability analysis and a discrete-time Markov chain model. The application of the proposed methodology is demonstrated through a case-study corrosion-damaged RC bridge pier. This article is part of a discussion meeting issue 'A cracking approach to inventing new tough materials: fracture stranger than friction'.