Approximate Bayesian Network Formulation for the Rapid Loss Assessment of Real-World Infrastructure Systems

This paper proposes to learn an approximate Bayesian Network (BN) model from Monte-Carlo simulations of an infrastructure system exposed to seismic hazard. Exploiting preliminary physical simulations has the twofold benefit of building a drastically simplified BN and of predicting complex system performance metrics. While the approximate BN cannot yield exact probabilities for predictive analyses, its use in backward analyses based on evidenced variables yields promising results as a decision support tool for post-earthquake rapid response. Only a reduced set of infrastructure components, whose importance is ranked through a random forest algorithm, is selected to predict the performance of the system. Further, owing to the higher importance of evidenced nodes, the ranking method is enhanced with a recursive evidence-driven BN-building algorithm, which iteratively inserts evidenced components into the subset identified by the random forest algorithm. This approach is applied to a French road network, where only 5 to 10 components out of 58 are kept to estimate the distribution of system performance metrics that are based on traffic flow. Sensitivity studies on the number of selected components, the number of off-line simulation runs and the discretization of variables reveal that the reduced BN applied to this specific example generates trustworthy estimates

A Pan-European representative ground motion model

This poster is ESC2016-527 in the 35th General Assembly of the European Seismological Commission (Trieste, 4-10 September 2016)Ground motion prediction equations (GMPE) are recognised as a key component of any seismic risk analysis. The consideration of both aleatory and epistemic sources of variability in the ground motion models may have significant influence on the overestimation or underestimation of the final losses. With the increased availability of new developed GMPEs over the past few years, it has been observed that the epistemic uncertainty due the choice between potential GMPEs is not decreasing, even though related knowledge is improving.The proposed model enables a complex problem to be represented by a minimum number of branches for single-site hazard analysis and mapping. A preliminary application is carried out for a critical infrastructure risk analysis in the framework of the EU-funded INFRARISK project (European Commission’s FP7 programme, Grant Agreement No. 603960)Peer Reviewe

FRACAS: a capacity spectrum approach for seismic fragility assessment including record-to-record variability

This paper presents a new approach for the derivation of fragility curves, named FRAgility through Capacity spectrum ASsessment (FRACAS). FRACAS adapts the capacity spectrum assessment method and uses inelastic response spectra derived from earthquake ground motion accelerograms to construct fragility curves. Following a description of the FRACAS approach, the paper compares the predicted maximum interstory drift (MIDR) response obtained from FRACAS and nonlinear time history analyses (NLTHA) for two case-study buildings subjected to 150 natural accelerograms. FRACAS is seen to represent well the response of both case-study structures when compared to NLTHA. Observations are made as to the sensitivity of the derived fragility curves to assumptions in the capacity spectrum assessment and fragility curve statistical model fitting. The paper also demonstrates the ability of FRACAS to capture inelastic record-to-record variability and properly translate this into resulting fragility curves. In particular, through an example application, it is shown that the variability in spectral ordinates for periods beyond the natural period of the undamaged structure is directly correlated to the standard deviations of the fragility curves

Examining the contribution of near real-time data for rapid seismic loss assessment of structures

This study proposes a probabilistic framework for near real-time seismic damage assessment that exploits heterogeneous sources of information about the seismic input and the structural response to the earthquake. A Bayesian Network is built to describe the relationship between the various random variables that play a role in the seismic damage assessment, ranging from those describing the seismic source (magnitude and location) to those describing the structural performance (drifts and accelerations) as well as relevant damage and loss measures. The a-priori estimate of the damage, based on information about the seismic source, is updated by performing Bayesian inference using the information from multiple data sources such as free-field seismic stations, Global Positioning System receivers, and structure-mounted accelerometers. A bridge model is considered to illustrate the application of the framework, and the uncertainty reduction stemming from sensor data is demonstrated by comparing prior and posterior statistical distributions. Two measures are used to quantify the added value of information from the observations, based on the concepts of pre-posterior variance and relative entropy reduction. The results shed light on the effectiveness of the various sources of information for the evaluation of the response, damage and losses of the considered bridge and on the benefit of data fusion from all considered sources

A Bayesian network-based probabilistic framework for updating aftershock risk of bridges

The evaluation of a bridge's structural damage state following a seismic event and the decision on whether or not to open it to traffic under the threat of aftershocks (ASs) can significantly benefit from information about the mainshock (MS) earthquake's intensity at the site, the bridge's structural response, and the resulting damage experienced by critical structural components. This paper illustrates a Bayesian network (BN)-based probabilistic framework for updating the AS risk of bridges, allowing integration of such information to reduce the uncertainty in evaluating the risk of bridge failure. Specifically, a BN is developed for describing the probabilistic relationship among various random variables (e.g., earthquake-induced ground-motion intensity, bridge response parameters, seismic damage, etc.) involved in the seismic damage assessment. This configuration allows users to leverage data observations from seismic stations, structural health monitoring (SHM) sensors and visual inspections (VIs). The framework is applied to a hypothetical bridge in Central Italy exposed to earthquake sequences. The uncertainty reduction in the estimate of the AS damage risk is evaluated by utilising various sources of information. It is shown that the information from accelerometers and VIs can significantly impact bridge damage estimates, thus affecting decision-making under the threat of future ASs

Sensitivity analysis of different capacity spectrum approaches to assumptions in the modeling, capacity and demand representations

Several capacity spectrum assessment methods exist for determination of structural performance of building models subjected to earthquake loading. The repetition of such analysis for earthquakes of increasing intensity will result in the derivation of analytical fragility functions. A comparison of three capacity spectrum assessment approaches (N2, SPO2IDA and FRACAS) has been carried out, highlighting the advantages and limitations of the approaches. Two experimental case studies have been chosen to evaluate the IM-EDP (Sa-Sd, ISDmax%) estimates obtained from the three different capacity spectrum procedures, as well as from non-linear time-history analyses (NLTHA). It is found that all three approaches perform well in estimating the response of a simple steel frame, but that FRACAS provides the best estimate of the response of an irregular reinforced concrete frame. It is concluded that further comparisons of the capacity spectrum approaches with large-scale experiments on structures are required to draw more general conclusions

Visualization of membrane loss during the shrinkage of giant vesicles under electropulsation

We study the effect of permeabilizing electric fields applied to two different types of giant unilamellar vesicles, the first formed from EggPC lipids and the second formed from DOPC lipids. Experiments on vesicles of both lipid types show a decrease in vesicle radius which is interpreted as being due to lipid loss during the permeabilization process. We show that the decrease in size can be qualitatively explained as a loss of lipid area which is proportional to the area of the vesicle which is permeabilized. Three possible mechanisms responsible for lipid loss were directly observed: pore formation, vesicle formation and tubule formation.Comment: Final published versio

Integrated multi-hazard framework for the fragility analysis of roadway bridges

This paper presents a method for the development of bridge fragility functions that are able to account for the cumulated impact of different hazard types, namely earthquakes, ground failures and fluvial floods. After identifying which loading mechanisms are affecting which bridge components, specific damage-dependent component fragility curves are derived. The definition of the global damage states at system level through a fault-tree analysis is coupled with a Bayesian Network formulation in order to account for the correlation structure between failure events. Fragility functions for four system damage states are finally derived as a function of flow discharge Q (for floods) and peak ground acceleration PGA (for earthquakes and ground failures): the results are able to represent specific failure configurations that can be linked to functionality levels or repair durations.Non UBCUnreviewedThis collection contains the proceedings of ICASP12, the 12th International Conference on Applications of Statistics and Probability in Civil Engineering held in Vancouver, Canada on July 12-15, 2015. Abstracts were peer-reviewed and authors of accepted abstracts were invited to submit full papers. Also full papers were peer reviewed. The editor for this collection is Professor Terje Haukaas, Department of Civil Engineering, UBC Vancouver.FacultyResearche

Vector intensity measures for a more accurate reliability assessment of NPP sub-systems

International audienceWithin the European-funded H2020 NARSIS project (New Approach to Reactor Safety ImprovementS, http://narsis.brgm.fr), one of the main challenges pertains to the development of innovative methods for the numerical derivation of fragility function for systems, structures or components (SSCs) within nuclear power plants (NPPs). In this context, the present study investigates the effect of integrating secondary seismic intensity measures when assessing the reliability of SSCs, with the objective of better identifying or even reducing the uncertainties due to record-to-record variability. While such an approach has already been applied to common reinforced-concrete or masonry buildings in previous studies, it is proposed here to consider an industrial structure containing a set of components at various locations of the building. We propose to treat the problem of station blackout following an earthquake (an accident event which is of primary importance as exemplified by the Fukushima Dai-Ichi event). Here the internal equipment is supposed to perform a given function (e.g., generation of emergency on-site power) and it may therefore be considered as a subsystem , for which a functionality assessment may be required within a probabilistic safety analysis. The studied subsystem comprises various types of components, whose failure modes are susceptible to either floor acceleration spectra or inter-story drift (e.g., failure of anchor bolts, excessive stress in pipeline segments, etc.). Such engineering demand parameters at various floor locations are not evenly correlated with the same intensity measures (e.g., spectral accelerations at different periods), thus highlighting the need for vector intensity measures that are able to capture a wide range of potential failure modes. Finally, the vector-based fragility functions derived at the component level are assembled in order to quantify the probability of the subsystem losing its function, for various levels of seismic loading. The statistical dependence between component failure events is taken into account thanks to the matrix-based system reliability method. A parametric study is then performed in order to evaluate the sensitivity of the sub-system's reliability with respect to various assumptions (e.g., correlation between failure events, correlation between intensity measures, etc.)