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首页> 外文期刊>Journal of Earthquake Engineering >STATISTICAL SEISMIC VULNERABILITY ASSESSMENT OF EXISTING REINFORCED CONCRETE BUILDINGS IN TURKEY ON A REGIONAL SCALE
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STATISTICAL SEISMIC VULNERABILITY ASSESSMENT OF EXISTING REINFORCED CONCRETE BUILDINGS IN TURKEY ON A REGIONAL SCALE

机译：区域规模上土耳其既有钢筋混凝土建筑的统计地震易损性评估

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摘要

A statistical procedure, called discriminant analysis, is used to develop a model for the preliminary assessment of the seismic vulnerability of low- to medium-rise (2-7 storey) reinforced concrete buildings. The earthquake damage data compiled in Duezce province after the 12 November 1999 Duezce earthquake formed the damage database. Number of storeys, minimum normalised lateral stiffness index, minimum normalised lateral strength index, normalised redundancy score, soft storey index and overhang ratio are selected as the basic damage inducing variables. Two discriminant functions are derived in terms of these variables considering immediate occupancy and life safety performance levels. In the proposed preliminary seismic vulnerability assessment model, the discriminant scores obtained from these two discriminant functions are combined in an optimal way and are used to classify existing buildings as "safe", "unsafe" and "requires further evaluation". The optimality criterion imposed into the model is the minimisation of the misclassification rate of damage states causing collapse. The validity of the proposed model is checked by using the seismic damage data associated with recent earthquakes that occurred in Turkey. The consistency between the observed damage distribution and the predictions of the proposed model supports the effectiveness of the proposed model.
机译：使用一种称为判别分析的统计程序来开发一个模型，用于对中低层（2-7层）钢筋混凝土建筑的地震脆弱性进行初步评估。 1999年11月12日杜兹采地震后在杜兹采省收集的地震破坏数据构成了破坏数据库。选择层数，最小归一化横向刚度指数，最小归一化横向强度指数，归一化冗余度分数，软层指数和悬挑率作为基本破坏诱发变量。考虑到即时占用率和生命安全绩效水平，根据这些变量得出了两个判别函数。在提出的初步地震脆弱性评估模型中，将从这两个判别函数获得的判别分数以最佳方式进行组合，并用于将现有建筑物分类为“安全”，“不安全”和“需要进一步评估”。施加到模型中的最优标准是最小化导致崩溃的破坏状态的错误分类率。通过使用与土耳其最近发生的地震有关的地震破坏数据来检验所提出模型的有效性。观察到的损害分布与所提出的模型的预测之间的一致性支持所提出的模型的有效性。

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