Scala, Santa Anna (2022) Seismic vulnerability of masonry buildings damaged after L'Aquila 2009 earthquake accounting for the effect of construction age. [Tesi di dottorato]

[img]
Preview
Text
scala_santaanna_34.pdf

Download (29MB) | Preview
[error in script] [error in script]
Item Type: Tesi di dottorato
Resource language: English
Title: Seismic vulnerability of masonry buildings damaged after L'Aquila 2009 earthquake accounting for the effect of construction age
Creators:
CreatorsEmail
Scala, Santa Annasantaanna.scala@unina.it
Date: 7 March 2022
Number of Pages: 333
Institution: Università degli Studi di Napoli Federico II
Department: Strutture per l'Ingegneria e l'Architettura
Dottorato: Ingegneria strutturale, geotecnica e rischio sismico
Ciclo di dottorato: 34
Coordinatore del Corso di dottorato:
nomeemail
Iervolino, Iunioiunio.iervolino@unina.it
Tutor:
nomeemail
Verderame, Gerardo MarioUNSPECIFIED
Del Gaudio, CarloUNSPECIFIED
Date: 7 March 2022
Number of Pages: 333
Keywords: Fragility curves; residential masonry buildings; AeDES form; post-earthquake damage data; construction age
Settori scientifico-disciplinari del MIUR: Area 08 - Ingegneria civile e Architettura > ICAR/09 - Tecnica delle costruzioni
Date Deposited: 16 Mar 2022 14:13
Last Modified: 28 Feb 2024 14:13
URI: http://www.fedoa.unina.it/id/eprint/14569

Collection description

The aim of this study is the analysis of seismic vulnerability of residential masonry buildings, with particular emphasis to the evolution of seismic behaviour over the years. To this purpose, first an in-dept analysis of the Italian building’s codes enacted over the years have been done, focusing on the evolution of seismic classification and normative contents related to masonry buildings. Then, an empirical analysis has been performed, based on data collected shortly afterwards the L’Aquila 2009 earthquake and recently released by the Italian Department of Civil Protection (DPC) through the Da.D.O. (Database di Danno Osservato, Database of Observed Damage) platform (Dolce et al., 2019). The building taxonomy has been defined reflecting the need to consider all the parameters available from post-earthquake inspections and the obtainment of reliable and homogeneous sample. A time-consuming data processing has been performed to obtain a generalized version of the original database, which has been integrated with census data to avoid bias in vulnerability and fragility analysis. Then, damage analysis has been done considering 5+1 damage grades defined for the whole building based on the conversion of damage for vertical structures in sight of the classification of European Macroseismic Scale. The analysis of mean damage values reveals the general trends as a function of the main influential parameters, i.e. construction age, structural types, and presence of retrofit intervention. Such vulnerability trends have been further investigated, introducing an intensity measure for the ground motion characterization. Thus, vulnerability curves have been derived assuming a lognormal statistical model and peak ground acceleration as intensity measure, through a minimization procedure of the distance between predicted and observed mean damage. So-obtained curves provide for each building class belonging to the defined taxonomy the relation between seismic intensity and mean damage, leading to the definition of a hierarchy in terms of damage attitude between classes. Moreover, two regression models (nonlinear weighted least squared estimation and maximum likelihood technique) have been adopted to determine the parameters of lognormal fragility curves, measuring their goodness of fit with the observed damage probability matrices (DPMs). Starting from the unconditioned model, further regression constraints (i.e., the respect of the hierarchy of median PGA with the building class and a common value for logarithmic standard deviation) have been introduced, thus leading to the definition of the conditioned model. The benefits in the introduction of further regression constraints are counterposed to the effectiveness of conditioned curves to model observational data through the comparison of the goodness of fit between the unconditioned and conditioned models.

Downloads

Downloads per month over past year

Actions (login required)

View Item View Item