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Recent national level guidelines and research efforts increasingly suggest the use of resilience and sustainability concepts for natural hazard mitigation and recovery planning at the regional and community levels. This paper aims to distinguish and compare the two abstract concepts in the context of road networks recovering from a hazard event since resilience and sustainability metrics are often interchangeably adopted and can lead to inaccurate equivalencies between the two concepts. A spectrum of resilience and sustainability metrics are defined herein and a research methodology is described to evaluate them probabilistically. A case study demonstration on the road network in the Memphis Metropolitan Statistical Area (MMSA) for a probabilistic suite of earthquake scenarios shows that the correlation between network resilience and sustainability is dependent on the hazard level as well as the graphical abstraction of the real road network. While some correlations are observed between temporal sustainability metrics, such as missed trips and traffic emissions, and resilience metrics, like total travel distance and time, the correlation weakens as the network resolution becomes more sparse. These results show that while for dense networks, time-evolving travel time and travel distance may be used as proxy measures for hazard sustainability evolution, for sparsely connected networks, hazard resilience and post-hazard sustainability remain relatively distinct. [ABSTRACT FROM AUTHOR]

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. DOI: 10.1061/(ASCE)ST.1943-541X.0000260 CE Database subject headings: Bridges; Seismic effects; Deterioration; Corrosion; Probability; Aging. Author keywords: Bridges; Seismic; Deterioration; Corrosion; Probability; Fragility.

Climate change, population dynamics, the aging of built infrastructure, and their growing complexity have gradually increased the vulnerability of coastal communities around the world. Among the many critical coastal infrastructures, the residential coastal building stock has exhibited significant vulnerabilities in past storm and hurricane events. Beyond the initial impact of these hurricane events on the built environment, coastal communities struggle to recover even years after landfall. Moreover, the initial shock as well as the recovery phase do not evenly affect all sectors of the population and frequently uncover social vulnerabilities and inequalities in the preparedness, response, and recovery from disasters. This study explores and expands a performance-based coastal engineering (PBCE) framework that allows for consideration of time-varying aspects of the hazard, depreciation, and aging or deterioration of coastal structures and infrastructure systems by applying it to evaluate the future performance and recovery of a portfolio of residential structures subjected to surge and wave loads. Using the residential building stock of Galveston, Texas as a case study, a Bayesian network framework is leveraged to evaluate the uncertain damage and subsequent recovery of the portfolio for the years 2030 and 2050, and correlations with representative social vulnerability factors are drawn. The correlation analysis between immediate damage and social vulnerability factors, as well as between the recovery index and social vulnerability factors up to six years following the storm landfall, is pursued to expose potential disparities in the impact of the storm to different sectors of the community in the short- and long-term. Results show that changing climate conditions exacerbate the probability of failure of the building stock and associated housing recovery. Also, the correlations in the short- and long-terms show that the elderly and women might be most at risk in future hurricane events. The incorporation of multi-structure systems and time-varying factors in the performance assessment framework is of great importance to inform resilience and adaptation engineering models, in particular, when the effects of chronic hazards, a growing population, and increases in asset values are expected to grow in the future. The methodology and case study also provide useful tools to inform planning and decision-making, resilience assessment, and facilitate recovery efforts in coastal settings while accounting for the impact of the hazard on vulnerable populations. [ABSTRACT FROM AUTHOR]

This paper discusses a computationally efficient methodology for probabilistic fragility and resilience analysis of bridges explicitly incorporating the effects of aftershock seismic events, based on nonlinear time history analysis, stochastic simulation and kriging surrogate modeling. Aftershock fragilities dependent on mainshock-induced damage states are developed and used in conjunction with restoration models for estimating bridge functionality and resilience. The input parameter space for the surrogate models consists of uncertain hazard and structural parameters and deterministic structural/geometrical bridge parameters, leading to probabilistic estimates of bridge performance metrics (i.e., fragility, functionality, resilience). Kriging surrogate modeling is used for estimating these metrics, facilitating efficient adoption of these complex structural and probability models. Relative importance of the predictive parameters is quantified by extending and integrating a probabilistic sensitivity analysis into the proposed framework. An illustrative example of the proposed framework is demonstrated through fragility and resilience assessment of a typical bridge in California subjected to mainshock and aftershock. [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. [ABSTRACT FROM AUTHOR]

Methods for the seismic analysis of dams have improved extensively in the last several decades. Advanced numerical models have become more feasible and constitute the basis of improved procedures for design and assessment. A probabilistic framework is required to manage the various sources of uncertainty that may impact system performance and fragility analysis is a promising approach for depicting conditional probabilities of limit state exceedance under such uncertainties. However, the effect of model parameter variation on the seismic fragility analysis of structures with complex numerical models, such as dams, is frequently overlooked due to the costly and time-consuming revaluation of the numerical model. To improve the seismic assessment of such structures by jointly reducing the computational burden, this study proposes the implementation of a polynomial response surface metamodel to emulate the response of the system. The latter will be computationally and visually validated and used to predict the continuous relative maximum base sliding of the dam in order to build fragility functions and show the effect of modelling parameter variation. The resulting fragility functions are used to assess the seismic performance of the dam and formulate recommendations with respect to the model parameters. To establish admissible ranges of the model parameters in line with the current guidelines for seismic safety, load cases corresponding to return periods for the dam classification are used to attain target performance limit states. [ABSTRACT FROM AUTHOR]