Добірка наукової літератури з теми "State-dependent fragility curves"

This paper seeks to determine the effect deterioration has on the seismic vulnerability of a 3 span integral concrete bridge. Traditionally it has been common to neglect the effects of deterioration when assessing the seismic vulnerability of bridges. However, since a lot of the bridges currently being assessed for retrofit are approaching the end of their design life, deterioration is often significant. Furthermore, since deterioration affects the main force resisting components of a bridge, it is reasonable to assume that it might affect its performance during an earthquake. For this paper, chloride induced corrosion of the reinforcing steel in the columns and in the deck has been considered. Corrosion is represented by a loss of steel cross section and strength. A 3 dimensional non-linear finite element model is created using the finite element platform Opensees. A full probabilistic analysis is conducted to develop time-dependent fragility curves. These fragility curves give the probability of reaching or exceeding a defined damage limit state, for a given ground motion intensity measure taken as Peak Ground Acceleration (PGA). This analysis accounts for variation in ground motion, material and corrosion parameters when assessing its overall seismic performance as well as the performance of its most critical components. The results of the study show that all components experience an increase in fragility with age, but that the columns are the most sensitive component to aging and dominate the system fragility for this bridge type.

Bridge horizontal deck curvature and the prevalence of in-span hinges in multi-frame RC box-girder bridges have reinforced this class of bridge to response with unique dynamic behavior during seismic excitations. This paper assesses the impacts of 10 different retrofit strategies on the vulnerability of curved multi-frame RC box-girder bridges with multi-column bents based on nonlinear time history analyses in OpenSEES. Consistent with HAZUS-MH definitions, fragility curves corresponding to four damage states at the component and system levels are developed for various bridge deck radii. The results indicate that combinations of retrofit strategies should be used to enhance the desirable level of bridge performance. Moreover, the most effective retrofit strategy in reducing probable damage for a given intensity is dependent on the bridge deck radius and is a function of the damage state of interest.

The seismic capacity of a structure is a function of the characteristics of the system as well as of its state, which is mainly affected by previous damage and deterioration. The cumulative damage from repeated shocks (for example during a seismic sequence or due to multiple events affecting an unrepaired building stock) affects the vulnerability of masonry buildings for subsequent events. This paper proposes an analytical methodology for the derivation of state-dependent fragility curves, taking into account cumulated seismic damage, whilst neglecting possible ageing effects. The methodology is based on nonlinear dynamic analyses of an equivalent single degree of freedom system, properly calibrated to reproduce the static and dynamic behaviour of the structure. An application of the proposed methodology to an unreinforced masonry case study building is also presented. The effect of cumulated damage on the seismic response of this prototype masonry building is further studied by means of nonlinear dynamic analyses with the accelerograms recorded during a real earthquake sequence that occurred in Canterbury (New Zealand) between 2010 and 2012.

In seismic risk estimation, among the different types of fragility curves used (judgement-based, mechanical, empirical/observational, hybrid), the mechanical ones have the twofold advantage of allowing a better control over the basic parameters and of representing a validation test of the consistency of empirical/observational ones. In this study, fragility curves of RC frames with column-driven failures are obtained from a simplified analytical pushover method implemented in a simple spreadsheet, thus allowing the user to perform a large number of analyses. More importantly, the proposed method introduces the concept that Limit States at the structural level are obtained consequent to the attainment of the same Limit States at the local level, in the columns’ sections. This avoids using additional criteria, such as interstorey drift thresholds. This simple analytical model allows for rapid development of fragility curves, for any Limit State, of different building typologies identified by a set of global quantities (number of storeys, story heights, number of spans and span lengths) and by a set of local quantities (element sizes, reinforcement, and material properties). It also allows for a straightforward treatment of the influence of the soil class on the fragility curves parameters, which is another critical issue addressed in this work that helps when interpreting some literature results using empirical/observational methods.

Background:There are several validated tools to quantitate lupus disease activity, end-organ damage and overall fragility. An algorithm to predict the hospitalization risk in lupus patients was proposed by Li et al1. That algorithm was able to effectively screen patients at increased risk of hospitalization using EHR information only. Recently, the new 2019 Lupus classification criteria score has been noted to accurately predict 10 year mortality2.Objectives:To test the above 2 algorithms with potential to predict lupus related hospital admissions.First, we attempted to validate the existing algorithm from the index study of Li et al to predict lupus hospitalization.Second, we tested the 2019 lupus clinical classification score for its ability to predict hospitalizations.Methods:A retrospective chart review was performed using EHR data collected from 2013 to 2018 at University of Kentucky (UK) Medical Center. Inclusion criteria were 18 years or older at first outpatient rheumatology appointment at UK, at least 3 outpatient rheumatology visits at UK, and ICD 9/10 code for Lupus. A total of 217 patients met inclusion criteria. Variables similar to the index study were extracted from patients’ first outpatient rheumatology visit at UK. Additionally, 2019 Lupus Classification Criteria score was calculated. Patients who were subsequently hospitalized, manual chart review was done to determine if the hospitalization was attributable to lupus or not.Results:Table 1 shows differences between the variables predicting hospitalization in patients in this study (UK) and the Ohio State University (OSU) cohort from whom the admission predicting algorithm was derived1. All the risk factors that were found to predict lupus hospitalization in the index study, failed to achieve a statistical significance in our validation study.Table 1.Differences in the variables predicting hospitalization between Index and Validation CohortVariables predicting Lupus HospitalizationIndex Study (Ohio State),% of patients(n=226)Validation Study(University of Kentucky)% of patients; (n=217)African American33%18%Creatinine > 1.217%7%Hemoglobin < 11g/dl79%18%Platelets < 180 / uL75%22%High Risk immunosuppression35%9. 2%Missed appointment27%25%There was more success predicting lupus hospitalization using the 2019 lupus classification criteria score (CCS) (Figure 1). A CCS >=19 predicted higher risk of lupus related hospitalization vs CCS < 19 over the ensuing 2 years (p-0.05).Figure 1.Kaplan- Meier Survival Analysis comparing the risk of hospitalization between the groups with 2019 Lupus classification criteria score (CCS) of less than 19 (red) and more/equal to 19 (blue). A time-dependent effect, with the admission free survival curves crossing at two years (Figure 1), indicated a 1 out of 3 chance of lupus related admission during the first 6 months for a high CCS score > =19.Conclusion:We failed to validate the EHR algorithm identifying patients at high risk for lupus hospitalization in our less severely affected cohort with fewer admission events to analyze. Nonetheless, “criteria counting” using the weightings of the 2019 lupus classification criteria was granular enough to make these case finding criteria themselves prognostic for future hospitalization risk. It is likely that existing EHRs, using protocols based upon classification criteria scores, are now capable of predicting survival, costs, and admissions automatically.References:[1]D Li et al. Determining risk factors that increase hospitalizations in patients with systemic lupus erythematosus, Lupus (2018) 27, 1321–1328[2]Carneiro et al. A comparison of three classification criteria sets for Systemic Lupus Erythematosus – a study looking at links to outcome and mortality; Arthritis Care Res (Hoboken). 2019 Sep 10. doi: 10.1002/acr.24061Disclosure of Interests:Saurav Suman: None declared, Mervat Eissa: None declared, Heidi Rogers: None declared, Aleksander Lenert: None declared, Arnold Stromberg: None declared, william roberts Shareholder of: Own Stocks of Pfizer and Novartis