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A framework for the generation of bridge-specific fragility curves utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and expensive resimulation. The methodology does not place any assumptions on the demand model of various components and helps to identify the relative importance of each uncertain variable in their seismic demand model. The methodology is demonstrated through the case study of a multispan concrete bridge class in California. Geometric, material, and structural uncertainties are accounted for in the generation of bridge numerical models and their fragility curves. It is also noted that the traditional lognormality assumption on the demand model leads to unrealistic fragility estimates. Fragility results obtained by the proposed methodology can be deployed in a risk assessment platform such as HAZUS for regional loss estimation. 相似文献
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Many older unreinforced masonry (URM) buildings feature timber floors and solid brick masonry. Simple equivalent frame models can help predicting the expected failure mechanism and estimating the strength of a URM wall. When modelling a URM wall with an equivalent frame model rather than, for example, a more detailed simplified micro-model, the strengths of the piers and spandrels need to be estimated from mechanical or empirical models. Such models are readily available for URM piers, which have been tested in many different configurations. On the contrary, only few models for spandrel strength have been developed. This paper reviews these models, discusses their merits, faults and compares the predicted strength values to the results of recent experimental tests on masonry spandrels. Based on this assessment, the paper outlines recommendations for a new set of strength equations for masonry spandrels. 相似文献
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Acta Geotechnica - Buried pipelines are one of the critical lifeline structures, and recently, efforts have been directed toward their probabilistic risk assessment. This paper explores the... 相似文献
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Critical uncertainty parameters influencing seismic performance of bridges using Lasso regression 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Recent efforts of regional risk assessment of structures often pose a challenge in dealing with the potentially variable uncertain input parameters. The source of uncertainties can be either epistemic or aleatoric. This article identifies uncertain variables exhibiting strongest influences on the seismic demand of bridge components through various regression techniques such as linear, stepwise, Ridge, Lasso, and elastic net regressions. The statistical results indicate that Lasso regression is the most effective one in predicting the demand model as it has the lowest mean square error and absolute error. As the sensitivity study identifies more than 1 significant variable, a multiparameter fragility model using Lasso regression is suggested in this paper. The proposed fragility methodology is able to identify the relative impact of each uncertain input variable and level of treatment needed for these variables in the estimation of seismic demand models and fragility curves. Thus, the proposed approach helps bridge owners to spend their resources judiciously (e.g., data collection, field investigations, and censoring) in the generation of a more reliable database for regional risk assessment. This proposed approach can be applicable to other structures. 相似文献
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Performance‐based grouping methods of bridge classes for regional seismic risk assessment: Application of ANOVA,ANCOVA, and non‐parametric approaches 下载免费PDF全文
下载免费PDF全文 Sujith Mangalathu Jong‐Su Jeon Jamie E. Padgett Reginald DesRoches 《地震工程与结构动力学》2017,46(14):2587-2602
One of the key tasks to enable a regional risk assessment is to group structures with similar seismic performances and generate fragility curves representative of the grouped structures. The grouping has been traditionally performed based primarily on engineering judgment and prior experience. This paper (i) presents an overview of various statistical techniques such as analysis of variance, analysis of covariance, and Kruskal–Wallis test for grouping the bridges of similar performance; (ii) compares the groupings that emerge from the various grouping techniques; and (iii) identifies the method that has more statistical power in creating bridge sub‐classes of distinct structural performance. The grouping is achieved by comparing the structural responses of bridge classes obtained from the non‐linear time history analysis of bridges. The relative merits of these grouping techniques are discussed with the case study of box‐girder bridges in California. Copyright © 2017 John Wiley & Sons, Ltd. 相似文献
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