非锚固式地上钢储液罐地震易损性强化分析

H. Phan, F. Paolacci, S. Alessandri, P. Hoang
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引用次数: 0

摘要

近几十年来的地震破坏表明,储罐是石化和石油加工厂中最脆弱的部件之一。储罐的损坏通常与安全壳的损失有关,从而导致附近地区的全面破坏。许多现有的钢储罐采用了过时的分析方法,并且低估了地震荷载;因此,在强烈的地面震动中可能发生各种类型的破坏。本文旨在提出一种适当的方法来评估工艺工厂中现有储罐的部件易损性,这将支持根据地面运动强度测量确定容器的损失,并最终对工厂及其附近地区进行定量风险分析。在这方面,一个位于西西里岛(意大利)的无锚储油罐被选为案例研究。首先利用ABAQUS软件对储罐进行了非线性静态推覆分析,并进行了筛选研究,探讨了储罐建模参数的意义。本研究旨在加深对哪些建模参数会显著影响储罐地震反应的理解,并减少易损性评估中的分析次数。然后根据静态推覆分析结果校准的集总质量模型开发易损性曲线。与先前确定的重要参数相关的不确定性来源在脆弱性分析中被考虑,使用抽样程序生成模型的统计显著样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhanced Seismic Fragility Analysis of Unanchored Above-Ground Steel Liquid Storage Tanks
Earthquake damage in recent decades has revealed that storage tanks are one of the most vulnerable components in petrochemical and oil processing plants. Damage to tanks commonly associated with losses of containment, and thus results in the overall damage to nearby areas. Many of existing steel storage tanks were designed with outdated analysis methods and with underestimated seismic loads; therefore, various types of failure may occur during a strong ground shaking. This paper aims to present an appropriate methodology for the component fragility evaluation of existing storage tanks in a process plant, which will support for the determination of the loss of containment in terms of the ground motion intensity measure and finally the quantitative risk analysis of the plant and its nearby areas. In this respect, an unanchored oil storage tank, which is ideally located in Sicily (Italy), is selected as a case study. The significance of modeling parameters of the tank is first investigated with a screening study, which is based on nonlinear static pushover analyses of the tank using the ABAQUS software. The study aims to enhance the understanding of which modeling parameters significantly affect the seismic response of the tank and to reduce the number of analyses in the fragility evaluation. The fragility curves are then developed based on a lumped-mass model that is calibrated from the static pushover analysis results. Sources of uncertainty, related to significant parameters previously identified, are considered in the fragility analysis using a sampling procedure to generate statistically significant samples of the model.
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