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Seismic vulnerability macrozonation map of SMRFs located in Tehran via reliability framework

  • Amini, Ali (Department of Civil and Environmental Engineering, Amirkabir University of Technology) ;
  • Kia, Mehdi (Department of Civil and Environmental Engineering, University of Science and Technology of Mazandaran) ;
  • Bayat, Mahmoud (Department of Civil and Environmental Engineering, University of South Carolina)
  • Received : 2020.05.31
  • Accepted : 2021.03.26
  • Published : 2021.05.10

Abstract

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.

Keywords

Acknowledgement

The authors would like to appreciate Dr. Mojtaba Mahsuli from Sharif University of Technology for providing the seismic source data and Mr. Mostapha Salehi from Persian Gulf University for assisting in map visualization. The first author thanks Messrs. Hossein Nasrazadani and Mohammad Aghababaei from ETH Zurich and Texas A&M University, respectively for their productive help in Rtx software. The authors would also express their sincere gratitude to the anonymous reviewers for their fruitful and constructive comments that greatly contributed to improving the final version of the paper.

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