The seismic assessment of unreinforced masonry (URM) buildings with cavity walls is a relevant issue in many countries, such as in Central and Northern Europe, Australia, New Zealand, China and several other countries. A cavity wall consists of two separate parallel masonry walls (called leaves) connected by metal ties: an inner loadbearing wall and an outer veneer having mostly aesthetic and insulating functions. Cavity walls are particularly vulnerable structural elements. If the two leaves of the cavity wall are not properly connected, their out-of-plane strength may be significantly smaller than that of an equivalent solid wall with the same thickness.The research presented in this paper focuses on a mechanical model developed to predict the failure mode and the strength capacity of metal tie connections in masonry cavity walls. The model considers six possible failures, namely tie failure, cone break-out failure, pull-out failure, buckling failure, piercing failure and punching failure. Tie failure is a predictable quantity when the possible failure modes can be captured. The mechanical model for the ties has been validated against the outcomes of an experimental campaign conducted earlier by the authors. The mechanical model is able to capture the mean peak force and the failure mode obtained from the tests. The mechanical model can be easily adopted by practising engineers who aim to model the wall ties accurately in order to assess the strength and behaviour of the structures against earthquakes. Furthermore, the proposed mechanical model is used to extrapolate the experimental results to untested configurations, by performing parametric analyses on key parameters including a higher strength mortar of the calcium silicate brick masonry, a different cavity depth, a different tie embedment depth, and solid versus perforated clay bricks.
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The seismic assessment of unreinforced masonry (URM) buildings with cavity walls is of high relevance in regions such as in Central and Northern Europe, Australia, New Zealand and China because of the characteristics of the masonry building stock. A cavity wall consists of two separate parallel walls usually connected by metal ties. Cavity walls are particularly vulnerable to earthquakes, as the out-of-plane capacity of each individual leaf is significantly smaller than the one of an equivalent solid wall. This paper presents the results of an experimental campaign conducted by the authors on metal wall tie connections and proposes a mechanical model to predict the cyclic behaviour of these connections. The model has been calibrated by us- ing the experimental results in terms of observed failure modes and force-displacement responses. Results are also presented in statistical format.
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In recent years, the number of human-induced earthquakes in Groningen, a large gas field in the north of the Netherlands, has increased. The majority of the buildings are built by using unreinforced masonry (URM), most of which consists of cavity (i.e. two-leaf) walls, and were not designed to withstand earthquakes. Efforts to define, test and standardize the metal ties, which do play an important role, are valuable also from the wider construction industry point of view. The presented study exhibits findings on the behavior of the metal tie connections between the masonry leaves often used in Dutch construction practice, but also elsewhere around the world. An experimental campaign has been carried out at Delft University of Technology to provide a complete characterization of the axial behavior of traditional connections in cavity walls. A large number of variations was considered in this research: two embedment lengths, four pre-compression levels, two different tie geometries, and five different testing protocols, including monotonic and cyclic loading. The experimental results showed that the capacity of the connection was strongly influenced by the embedment length and the geometry of the tie, whereas the applied pre-compression and the loading rate did not have a significant influence.
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Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection. The validated product of the experiment will be tested on the monument of Fraeylemaborg.
Post-earthquake structural damage shows that wall collapse is one of the most common failure mechanisms in unreinforced masonry buildings. It is expected to be a critical issue also in Groningen, located in the northern part of the Netherlands, where human-induced seismicity has become an uprising problem in recent years. The majority of the existing buildings in that area are composed of unreinforced masonry; they were not designed to withstand earthquakes since the area has never been affected by tectonic earthquakes. They are characterised by vulnerable structural elements such as slender walls, large openings and cavity walls. Hence, the assessment of unreinforced masonry buildings in the Groningen province has become of high relevance. The abovementioned issue motivates engineering companies in the region to research seismic assessments of the existing structures. One of the biggest challenges is to be able to monitor structures during events in order to provide a quick post-earthquake assessment hence to obtain progressive damage on structures. The research published in the literature shows that crack detection can be a very powerful tool as an assessment technique. In order to ensure an adequate measurement, state-of-art technologies can be used for crack detection, such as special sensors or deep learning techniques for pixel-level crack segmentation on masonry surfaces. In this project, a new experiment will be run on an in-plane test setup to systematically propagate cracks to be able to detect cracks by new crack detection tools, namely digital crack sensor and vision-based crack detection.