Open this publication in new window or tab >>National Laboratory for Civil Engineering, Portugal.
National Laboratory for Civil Engineering, Portugal.
EUCENTRE European Centre for Training and Research in Earthquake Engineering, Italy.
EUCENTRE European Centre for Training and Research in Earthquake Engineering, Italy; University of Pavia, Italy.
McGill University, Canada.
Basler & Hofmann AG, Switzerland.
Holmes Consulting LP, New Zealand.
BAM Advies & Engineering, Netherlands.
BAM Advies & Engineering, Netherlands.
Holmes Consulting LP, New Zealand.
University of Minho, Portugal.
University of Minho, Portugal.
University of Genoa, Italy.
University of Genoa, Italy.
University of Auckland, New Zealand.
University of Auckland, New Zealand.
University of Auckland, New Zealand.
University of Naples “Federico II”, Italy.
University of Naples “Federico II”, Italy.
University of Naples “Federico II”, Italy.
Sapienza University of Rome, Italy.
Sapienza University of Rome, Italy.
Sapienza University of Rome, Italy.
University of Naples “Federico II”, Italy.
RISE Research Institutes of Sweden, Materials and Production, Applied Mechanics.
University of Naples “Federico II”, Italy.
University of Naples “Federico II”, Italy.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Minho, Portugal.
University of Belgrade, Serbia.
University of Belgrade, Serbia.
École Polytechnique Fédérale de Lausanne, Switzerland.
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2024 (English)In: Bulletin of Earthquake Engineering, ISSN 1570-761X, E-ISSN 1573-1456, Vol. 22, no 12, p. 5993-Article in journal (Refereed) Published
Abstract [en]
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.
National Category
Geophysics
Identifiers
urn:nbn:se:ri:diva-64277 (URN)10.1007/s10518-022-01582-x (DOI)2-s2.0-85150470193 (Scopus ID)
Note
The project leading to this paper has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730900.
2023-03-272023-03-272025-02-21Bibliographically approved