2015
DOI: 10.1016/j.compstruc.2015.02.008
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An inverse analysis procedure for material parameter identification of mortar joints in unreinforced masonry

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Cited by 32 publications
(17 citation statements)
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“…One of the merits of the proposed mesoscale description is that an accurate calibration of material parameters can be achieved performing relatively simple tests at component level and low invasive in-situ experiments in the case of existing structures. In particular, the experimental-numerical calibration procedure proposed in [35,36] can be adopted, where simple tests with flat jacks are coupled with inverse analysis techniques for mesoscale material parameters calibration.…”
Section: D Mesoscale Descriptionmentioning
confidence: 99%
“…One of the merits of the proposed mesoscale description is that an accurate calibration of material parameters can be achieved performing relatively simple tests at component level and low invasive in-situ experiments in the case of existing structures. In particular, the experimental-numerical calibration procedure proposed in [35,36] can be adopted, where simple tests with flat jacks are coupled with inverse analysis techniques for mesoscale material parameters calibration.…”
Section: D Mesoscale Descriptionmentioning
confidence: 99%
“…Compared to more common gradient-based algorithms, they are able to escape local optima and find a solution when the analytical form of the objective function is not known. In this work the software TOSCA, developed at the University of Trieste and previously utilised in identification problems [38,39], was employed. A population of potential solutions (individuals) is created and the objective function is evaluated for each individual.…”
Section: Optimal Tuning Of Tmds By Means Of Gasmentioning
confidence: 99%
“…1). A Genetic Algorithm with the following properties was used: Each one of the listed parameters was set based on previous research in optimization problems (Chisari et al, 2015a;2015b). The Genetic Algorithm was implemented in the software TOSCA (Chisari, 2015).…”
Section: Optimization Analysesmentioning
confidence: 99%