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DR. SIMON PETROVČIČ Assist. Professor, University of Ljubljana

Lecture 1:  “Inelastic Modelling of Historic Masonry Structures for Seismic Analyses”

The lecture will give a general overview of the special considerations that need to be considered when modelling historic masonry structures for seismic performance assessment. A simple and computationally less demanding technique for the modelling and analysis of regular unreinforced masonry structures will be presented. This technique is based on the equivalent frame approach, and incorporates linear beam elements and the plastic hinge concept. The effect of the vertical loading acting on piers will also be discussed, as well as the formation of typical failure mechanisms throughout the structure. The presented methodology can have many benefits in various practical and research aspects. For example, it can be used to determine possible damage patterns in piers before conducting the actual analysis. 

Exercise 1 (software excel):

The students will be able to try out the inelastic modelling technique that I will present in the lecture on case-study examples. Students will work in pairs. Each pair will be given a picture / schematic representation of a typical European historic masonry structure, together with its general dimensions and material properties. The goal the exercise is to determine the predicted failure modes and inelastic parameters of the given masonry wall assemblage. 

Exercise 2 (part of project No. 3):

This is a continuation of Exercise 1 in which the students create an equivalent frame model of the structure (2D wall assemblage) in SAP2000 and model the inelastic characteristics of the masonry. A pushover analysis is conducted and the seismic performance is assessed, based on criteria from EC8-3. 


Prof. I. PEREIRA (Aveiro, Portugal)

Exploring problems in Regression Analysis
Linear least-squares regression analysis makes very strong assumptions about the structure of
data--and, when these assumptions fail to characterize accurately the data the results of a
regression analysis can be seriously misleading. So it is necessary to give an accessible explanation
of the techniques needed for exploring problems that comprise a regression analysis and for
determining whether certain assumptions appear reasonable. Starting with a review of leastsquares
linear regression, this lecturer covers such topics as the problem of collinearity in multiple
regression, dealing with outlying and influential data, non-normality of errors and non-constant
error variance and considering robust regression. R code is used in order to make some
illustrations.