2019
DOI: 10.1080/13632469.2019.1597784
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Improved Urban Seismic Vulnerability Assessment Using Typological Curves and Accurate Displacement Demand Prediction

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Cited by 11 publications
(10 citation statements)
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“…After creating the habitability scales and identifying which urban modiner levéis are most strongly correlated with structural and no structural damage, we then combined levéis for each type of structure (masonry and concrete) that had a 70% or higher probabñity of experiencing damage in each kind of son. The resulting building typologies allowed us to rate the habitability of the housing stock and check the findings of other studies including the real building stock (Diana et al 2019). These typologies were generated for both class B and class C son and used to map habitability in the study áreas.…”
Section: Analysis and Results Building Habitability Assessmentmentioning
confidence: 95%
“…After creating the habitability scales and identifying which urban modiner levéis are most strongly correlated with structural and no structural damage, we then combined levéis for each type of structure (masonry and concrete) that had a 70% or higher probabñity of experiencing damage in each kind of son. The resulting building typologies allowed us to rate the habitability of the housing stock and check the findings of other studies including the real building stock (Diana et al 2019). These typologies were generated for both class B and class C son and used to map habitability in the study áreas.…”
Section: Analysis and Results Building Habitability Assessmentmentioning
confidence: 95%
“…The specific typology is based on a detailed survey involving the construction drawings collected in the city archives for a limited number of buildings. Five new building types were defined (Diana et al, 2021): A1, A2, B2, C and D2 (Figure 4). Type A1 corresponds to unreinforced masonry (URM) buildings with a reinforced concrete (RC) basement floor.…”
Section: Building Typologymentioning
confidence: 99%
“…Milutinovic and Trendafiloski, 2003;Risk-UE, 2003;Mouroux and Le Brun, 2006) was applied to both cities, considering the particularities of the Swiss territory in terms of seismicity and the specific features of the Swiss building stock (Lestuzzi et al, 2016;Lestuzzi et al, 2017). The reliability of the results was afterwards further improved by considering typological curves and accurate displacement demand prediction (Rota et al, 2008;Diana et al, 2018;Diana et al, 2021). The investigations were performed using building stock databases from 2015 surveys.…”
Section: Introductionmentioning
confidence: 99%
“…Different types of methods, widely developed in the last 50 years, are particularly suitable to this aim; they are generally classified as three different approaches [7,8]: (i) empirical procedures that identify building categories, called "vulnerability class", on the basis of recurrent typological and structural features to define corresponding vulnerability functions calibrated using damage observed after past earthquakes or combine the evaluation of a few parameters (geographical position, general characteristics of the structure, and possible damage), to obtain a final seismic vulnerability index to establish a relation between the seismic action and response of the buildings [9][10][11][12][13][14][15][16][17][18][19][20]; (ii) mechanical/analytical procedures that define vulnerability functions as a relation between structural capacity and seismic demand by means of structural analysis of one or more numerical models representative of samples of buildings. However, these types of methodologies are in many cases burdensome for large-scale applications, requiring very detailed knowledge about the building features and time-consuming structural calculations [21][22][23][24]; and (iii) hybrid procedures that calibrate the results of the mechanical/analytical assessment with postearthquake observational damages, useful in the cases of partial absence of damage data or difficulty with the calibration or validation of the results of analytical models [25,26].…”
Section: Introductionmentioning
confidence: 99%