Abstract
This review chapter outlines the outcomes of a combined experimental-numerical investigation on the retrofitting of masonry structures by means of polymeric textile reinforcement. Masonry systems comprise a significant portion of cultural heritage structures, particularly within European borders. Several of these systems are faced with progressive ageing effects and are exposed to extreme events, as for instance intense seismicity levels for structures in the center of Italy. As a result, the attention of the engineering community and infrastructure operators has turned to the development, testing, and eventual implementation of effective strengthening and protection solutions. This work overviews such a candidate, identified as a full-coverage reinforcement in the form of a polymeric multi-axial textile. This investigation is motivated by the EU-funded projects Polytect and Polymast, in the context of which this protection solution was developed. This chapter is primarily concerned with the adequate simulation and verification of the retrofitted system, in ways that are computationally affordable yet robust in terms of simulation accuracy. To this end, finite element-based mesoscopic and multiscale representations are overviewed and discussed within the context of characterization, identification and performance assessment.
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Acknowledgements
Dr. Fuggini would like to gratefully acknowledge the support of European Community’s Seventh Framework Programme [FP7/2007-2013] for access to Eucentre under grant agreement N 227887. Prof. Chatzi and Prof. Triantafyllou would like to gratefully acknowledge the support of the Swiss National Science Foundation under Research Grants \(\#200021\_146996\), \(\#200021\_153379\).
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Chatzi, E.N., Triantafyllou, S.P., Fuggini, C. (2018). Numerical and Experimental Investigations of Reinforced Masonry Structures Across Multiple Scales. In: Ottaviano, E., Pelliccio, A., Gattulli, V. (eds) Mechatronics for Cultural Heritage and Civil Engineering. Intelligent Systems, Control and Automation: Science and Engineering, vol 92. Springer, Cham. https://doi.org/10.1007/978-3-319-68646-2_15
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