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Earthquake‐induced damage detection and localization in masonry structures using smart bricks and Kriging strain reconstruction: A numerical study
Author(s) -
GarcíaMacías Enrique,
Ubertini Filippo
Publication year - 2018
Publication title -
earthquake engineering and structural dynamics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.218
H-Index - 127
eISSN - 1096-9845
pISSN - 0098-8847
DOI - 10.1002/eqe.3148
Subject(s) - masonry , structural health monitoring , structural engineering , retrofitting , piezoresistive effect , unreinforced masonry building , smart material , engineering , software deployment , computer science , civil engineering , geotechnical engineering , materials science , electrical engineering , software engineering , nanotechnology
Summary The intrinsic vulnerability of masonry structures to seismic events makes structural health monitoring of the utmost importance for the conservation of the built heritage. The development of piezoresistive bricks, also termed smart bricks, is an innovative technology recently proposed by the authors for the monitoring of such structures. Smart bricks exhibit measurable variations in their electrical properties when subjected to external loads or, alternatively, strain self‐sensing capabilities. Therefore, the deployment of a network of smart bricks into a masonry structure confers self‐diagnostic properties to the host structure. In this light, this paper presents a theoretical investigation on the application of smart bricks to full‐scale masonry structures for seismic assessment. This includes the study of the convenience of providing electrical isolation conditions to the sensors, as well as the effectiveness of smart bricks when installed into either new constructions or in pre‐existing structures. Secondly, numerical results are presented on the seismic analysis of a three‐dimensional masonry building equipped with a network of smart bricks. Finally, in order to map the strain field throughout the structure exploiting the outputs of a limited number of sensors, an interpolation‐based strain reconstruction approach is proposed.