Shake‑table testing of a stone masonry building aggregate: overview of blind prediction study

City centres of Europe are often composed of unreinforced masonry structural aggregates, whose seismic response is challenging to predict. To advance the state of the art on the seismic response of these aggregates, the Adjacent Interacting Masonry Structures (AIMS) subproject from Horizon 2020 project Seismology and Earthquake Engineering Research Infrastructure Alliance for Europe (SERA) provides shake-table test data of a two-unit, double-leaf stone masonry aggregate subjected to two horizontal components of dynamic excitation. A blind prediction was organized with participants from academia and industry to test modelling approaches and assumptions and to learn about the extent of uncertainty in modelling for such masonry aggregates. The participants were provided with the full set of material and geometrical data, construction details and original seismic input and asked to predict prior to the test the expected seismic response in terms of damage mechanisms, base-shear forces, and roof displacements. The modelling approaches used differ significantly in the level of detail and the modelling assumptions. This paper provides an overview of the adopted modelling approaches and their subsequent predictions. It further discusses the range of assumptions made when modelling masonry walls, floors and connections, and aims at discovering how the common solutions regarding modelling masonry in general, and masonry aggregates in particular, affect the results. The results are evaluated both in terms of damage mechanisms, base shear forces, displacements and interface openings in both directions, and then compared with the experimental results. The modelling approaches featuring Discrete Element Method (DEM) led to the best predictions in terms of displacements, while a submission using rigid block limit analysis led to the best prediction in terms of damage mechanisms. Large coefficients of variation of predicted displacements and general underestimation of displacements in comparison with experimental results, except for DEM models, highlight the need for further consensus building on suitable modelling assumptions for such masonry aggregates

Seismic testing of adjacent interacting masonry structures – shake table test and blind prediction competition

Across historical centres in Europe, stone masonry buildings form building aggregates that developed as the layout of the city or village was densified. In these aggregates, adjacent buildings can share structural walls with an older and a newer unit connected either by interlocking stones or by a layer of mortar. Observations after for example the recent Central Italy earthquakes showed that joints between the buildings were often the first elements to be damaged, leading to a complex interaction between the units. The analysis of such building aggregates is difficult due to the lack of guidelines, as the advances were impeded by the scarce experimental data. Therefore, the objective of the project AIMS (Seismic Testing of Adjacent Interacting Masonry Structures), included in the H2020 project SERA, was to provide such data by testing an aggregate of two double-leaf stone masonry buildings under two horizontal components of dynamic excitation. The test units were constructed at half-scale, with a two-storey building and a one-storey building. The buildings shared one common wall, while only a layer of mortar connected the façade walls. The floors were at different heights and had different beam orientations. Prior to the test, a blind prediction competition was organized with twelve participants from academia and industry that were provided with all the geometrical and material data, construction details, and the seismic input. The participants were asked to report results in terms of damage mechanisms, recorded displacements and base shear values. Results of the shake-table campaign are reported, together with a comparison with the blind predictions. Large scatter in terms of reported predictions highlights the impact of modelling uncertainties and the need for further tests