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Prioritization of a Building Portfolio for Seismic Retrofit Based on Risk Considering Structural Irregularities

Asadi, Mohammad | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56142 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Mahsouli, Mojtaba
  7. Abstract:
  8. This study proposes a probabilistic approach to prioritize a collection of buildings in a region for seismic retrofit based on regional risk analysis. Risk in this context is the exceedance probability of total economic and social losses, which include both direct and indirect consequences resulting from building damage. The prioritization criterion is determined by the benefit-to-cost ratio of retrofit, where the benefit is measured by the reduction of seismic risk achieved through retrofitting. Hence, the buildings are analyzed both in their current state and after retrofit to compute the risk measure, i.e., the mean total loss. Buildings that yield the highest reduction of the risk measure per unit retrofit cost are given priority for seismic retrofit. The prioritization also takes into account the impact of structural irregularities and defects on the performance and thus, the prioritization of buildings. Furthermore, this study proposes a methodology to extend the regional risk analysis from the building level to the block level in order to alleviate the computational demand and make the analysis feasible. Each block is a collection of sub-blocks, and each sub-block is formed by aggregating the buildings of that block with the same vulnerability parameters at the center of the block. Risk analysis is conducted using Monte Carlo sampling, in which multiple probabilistic models simulate various phenomena. These models include occurrence, magnitude, rupture area, and location of earthquakes, the resulting ground motion intensity, structural response, damage, cost of repairing structural and non-structural components and contents, business disruption, downtime, injuries and fatalities and their ensuing social loss including the degradation of life quality. As a case study, the proposed framework is implemented for a neighborhood in the City of Tehran, Iran using Rtx, which is a computer program for reliability, risk, and resilience analyses with a comprehensive library of probabilistic models. According to the findings, the mean and standard deviation of the maximum 50-year total loss in the studied area are 5,841 and 7,766 billion Tomans, respectively, prior to retrofit, and 1,718 and 3,415 billion Tomans afterwards, reducing the seismic risk by 71%. Three retrofit prioritization measures are proposed, the choice of which depends on the preferences of decision-makers. The most complete measure is one that considers all types of the considered economic and social losses. It is shown that prioritizing buildings merely in accordance to their seismic risk leads to inefficient allocation of resources compared to the proposed prioritization measure. In the case study, retrofitting the top-10 buildings with the highest seismic risk would cost 625 billion Tomans, and would result in a reduction of 566 billion Tomans in the mean maximum 50-year total loss for the entire building portfolio. However, spending a similar amount on the buildings that are ranked the highest according to the proposed measure leads to retrofitting 405 buildings, which reduces the mean maximum 50-year total loss by nearly 2,500 billion Tomans, a fourfold increase in savings. Moreover, the results of a risk analysis at the sub-block level indicate that aggregating the buildings into sub-blocks can reduce the computational demand by 64% for the small error of 1.3% in computing the risk measure. Thus, using the proposed approach makes it possible to conduct risk analysis of large building portfolios and megacities
  9. Keywords:
  10. Prioritization ; Retrofit ; Probabilistic Seismic Structures Vulnerability ; Building ; Irregularity Criterion ; Monte Carlo Sampling ; Probabilistic Modeling ; Socioeconomic Loss

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