Safety and serviceability of highway bridges, during and after an earthquake, is a prerequisite to ensure continuous transport facilities, emergency and evacuation routes. Recently, fragility curves have emerged as important decision support tools to identify the potential seismic risk and consequences during and after an earthquake. There has been a substantial increase in interest among researchers in the topic of seismic fragility assessment of highway bridges as evidenced by the growing number of published literature. Advanced computational techniques and available resources have led to the development of different methodologies for fragility assessment. This study presents a review of the different methodologies developed for seismic fragility assessment of highway bridges along with their features, limitations and applications. This study presents a review of available methodologies and identifies opportunities for future development. This study mainly focuses on the key features of different method...

Seismic fragility assessment of highway bridges: a state-of-the-art review

Highway bridges are one of the most vulnerable constituents of transportation networks when exposed to one or more natural hazards, such as earthquakes, hurricanes, tsunamis, and riverine floods. To facilitate and enhance prehazard and posthazard event mitigation and emergency response strategies of transportation systems and entire communities, probabilistic risk and resilience assessment methodologies have attracted increased attention recently. In this context, fragility and restoration models for highway bridges subjected to a range of hazards are essential tools for efficient and accurate quantification of risk and resilience. This paper provides a comprehensive review of state-of-the-art fragility and restoration models for typical highway bridge classes that are applicable for implementation in multihazard risk and resilience analyses of regional portfolios or transportation networks in the United States. An overview of key gaps in the literature is also presented to guide future research.

Multiple-Hazard Fragility and Restoration Models of Highway Bridges for Regional Risk and Resilience Assessment in the United States: State-of-the-Art Review

A Bayesian network methodology is developed for performing infrastructure seismic risk assessment and providing decision support with an emphasis on immediate post-earthquake applications. The methodology consists of four major components: (1) a seismic demand model of ground motion intensity as a spatially distributed Gaussian random field accounting for multiple seismic sources with uncertain characteristics and including finite fault rupture and directivity effects; (2) a model of the performance of point-site and distributed components under seismic loading; (3) models of system performance as a function of component states; and (4) the extension of the Bayesian network to include decision and utility nodes to aid post-earthquake decision-making.A Bayesian network is a probabilistic graphical model that represents a set of random variables and their probabilistic dependencies. The variables may represent demand or capacity values, or the states of components and systems. Bayesian networks are graphical and intuitive, facilitate information updating, can be used for identification of critical components within a system, and can be extended by decision and utility nodes to solve decision problems. The facility for information updating renders the Bayesian network an ideal tool for infrastructure seismic risk assessment and decision support, particularly in near-real time applications immediately following a destructive earthquake. Evidence on one or more variables (e.g. observed component capacities, demands, or damage states) can be entered into the Bayesian network and this information propagates throughout the network to provide an up-to-date probabilistic characterization of the performance of the infrastructure system under the uncertain and evolving state of information that is characteristic of the post-event period. Like most computational methods, Bayesian networks have limitations. In particular, calculations in Bayesian networks can be highly demanding of computer memory. The present study develops methodologies to minimize computational demands by optimizing network topology and, when necessary, making trade-offs between accuracy and computational efficiency.The study begins with a brief introduction to Bayesian networks. Next, each of the aforementioned components of the methodology is described. The seismic demand model provides distributions of ground motion intensity at discrete points in the geographic domain of a spatially distributed infrastructure system. This model can be used to perform and go beyond conventional probabilistic seismic hazard assessment. In particular, the model provides a full random field characterization of the ground motion intensity, thus allowing assessment of seismic risk for spatially distributed systems. Equally important, the model enables updating of the distribution of intensity at any selected site upon observation of the intensity at other sites. The modeling of random fields via Bayesian network results in a densely connected topology that renders probabilistic inference computationally demanding and possibly intractable. To address this problem, several approaches for approximating the correlation structure of variables drawn from a random field are developed, which amount to selectively removing links and nodes in the Bayesian network. It is found that a method based on numerical optimization achieves the best trade-off of accuracy versus efficiency. Bayesian network formulations are presented for modeling component performance as a function of seismic demand using fragility functions. The framework accounts for potential sources of correlation in component response. Models for point-site and distributed components are presented. The latter is based on an assumption that damages along a component occur according to a non-homogenous Poisson process. Five Bayesian network formulations for modeling system performance as a function of component states are developed. One approach uses a naive topology, two formulations are based on an intuitive interpretation of system performance, and two approaches utilize minimal link and cut sets. The last two formulations are then adapted and refined with the goal of minimizing computational demands by arranging nodes in chain-like structures that reduce the size of conditional probability tables and, consequently, required computation time and memory.The Bayesian network is extended by decision and utility nodes to create a new graphical construct known as an influence diagram. This diagram aids decision-making by specifying decision alternatives that maximize expected utility given all available evidence. The extension of the framework to include decision and utility nodes is demonstrated by application to a post-earthquake decision scenario involving inspection and shutdown decisions. A limited memory influence diagram is constructed to model this decision problem. A heuristic based on a value of information criterion is described for prioritizing component inspections following an earthquake.Two example applications demonstrate the Bayesian network methodology for infrastructure seismic risk assessment and decision support. The second example employs a preliminary and hypothetical model of the proposed California high speed rail system.

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A Bayesian Network Methodology for Infrastructure Seismic Risk Assessment and Decision Support - eScholarship

The optimal decisions to maintain or improve the reliability and functionality of structures and infrastructure systems can only be achieved through proper integrated management planning in a life-cycle comprehensive framework. Structure and Infrastructure Engineering (SIE) is an international journal dedicated to recent advances inmaintenance, management, and life-cycle performance of a wide range of infrastructures. The purpose of this article is to provide a brief review of the recent research accomplishments in the field of design, maintenance, life-cycle management, and optimisation of structures and infrastructures reported in papers published in SIE during the period 2005–2011. The papers are categorised under main topics and very briefly discussed.

Maintenance, management, life-cycle design and performance of structures and infrastructures: a brief review

Long‐term seismic performance of reinforced concrete bridges under steel reinforcement corrosion due to chloride attack

The neutralisation (carbonation) of concrete usually results in material deterioration of a reinforced concrete (RC) bridge, so that the seismic capacity of the structure tends to degrade over time. This paper determined the deteriorated plastic hinge properties of the neutralised RC bridge column and performed the pushover analysis to obtain the decayed seismic capacity curves. As a result, the time-dependent fragility curves with respect to some representative damage levels can be established and the possible seismic loss can be expressed as a function of service time. The S-surfaces representing retrofitting cost versus service time for a neutralised RC bridge subjected to different earthquake levels were determined quantitatively in a case study. Throughout the whole life-cycle of a bridge, critical service times corresponding to dramatically increased slopes in the S-surface associated with cost elevations can be identified to assist in the development of a financially optimised strategy for timely seismic retrofitting.

Yu-Chi Sung

Papers

Time-dependent seismic fragility curves on optimal retrofitting of neutralised reinforced concrete bridges