Introduction

In many historical centres in Europe, stone masonry buildings are part of building aggregates, which developed when the layout of the city or village was densified. In these aggregates, adjacent buildings share structural walls to support floors and roofs. Meanwhile, the masonry walls of the façades of adjacent buildings are often connected by dry joints since adjacent buildings were constructed at different times. Observations after for the 2016 earthquakes in Central Italy showed that the dry joints between the building units were often the first elements to be damaged. As a result, the joints opened up leading to pounding between the building units and a complicated interaction at floor and roof beam supports.

The analysis of such building aggregates is very challenging and modelling guidelines missing. Advances in the development of analysis methods have been impeded by the lack of experimental data on the seismic response of such aggregates. The objective of this project is to provide such experimental data by testing an aggregate of two buildings under two components of horizontal excitation. One building is a two storey building, the other a one storey building. The buildings share one common wall while the façade walls are connected by dry joints. The floors are at different heights leading to a complex dynamic response of this smallest possible building aggregate.
To compare and advance the modelling approaches a blind prediction competition is organised. This document provides all required information. The test is planned for end of November 2019 and will be carried out at the LNEC facility in Lisbon, Portugal, under the lead of Prof. António Correia, Prof. Paulo Candeias and Prof. Alfredo Campos Costa. If interested in taking part in the blind prediction competition or in case of any questions, please contact any of the following:

The experimental test is funded through the SERA project (SERA H2020-INFRAIA-2016-2017, Transnational access to research infrastructure). This opportunity is gratefully acknowledged.

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