2020
DOI: 10.1080/15583058.2020.1723735
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Model Updating of Historical Belfries Based on Oma Identification Techniques

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Cited by 76 publications
(32 citation statements)
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“…Limit analysis has been shown a valuable method to estimate the capacity of masonry structures up to collapse as long as the cracks are effectively described [37][38][39][40][41][42][43][44]. Sophisticated techniques involving dynamic monitoring can reveal the actual state of a traditional masonry structures and points with possible weaknesses [45][46][47][48][49][50][51][52][53][54][55]. Furthermore, building information modelling can be helpful in the investigation of the architectural properties of monumental structures [56].…”
Section: Introductionmentioning
confidence: 99%
“…Limit analysis has been shown a valuable method to estimate the capacity of masonry structures up to collapse as long as the cracks are effectively described [37][38][39][40][41][42][43][44]. Sophisticated techniques involving dynamic monitoring can reveal the actual state of a traditional masonry structures and points with possible weaknesses [45][46][47][48][49][50][51][52][53][54][55]. Furthermore, building information modelling can be helpful in the investigation of the architectural properties of monumental structures [56].…”
Section: Introductionmentioning
confidence: 99%
“…In the paper by Standoli et al (2020), the seismic vulnerability of masonry bell-towers, conditioned by the degrading state and high slenderness, is pointed out. Four different case studies, represented by isolated towers in the province of Ferrara (Italy), were investigated.…”
Section: Structural Health Monitoring Of Architectural Heritage: From the Past To The Future Advancesmentioning
confidence: 99%
“…Several methods have been validated in the literature for earthquake-induced damage identification, aimed at the preventive conservation of historic masonry buildings. Among them, vibration-based structural health monitoring (SHM) methods resorting to modal parameters as damage-sensitive features and based on unsupervised learning techniques are eminently efficient for early-stage damage detection (Bennati et al 2005;Ivorra and Pallarés 2006;Alvandi and Cremona 2006;Brencich and Sabia 2008;Ramos et al 2010;Spina et al 2011;Ditommaso et al 2012;Brando et al 2015;Gentile et al 2016;Dolce et al 2017;Masciotta et al 2017;Clementi et al 2017;Saisi et al 2018;Kita et al 2019;Formisano et al 2020;García-Macías et al 2020a;Giordano et al 2020a, b;Bianconi et al 2020;Standoli et al 2021). Unfortunately, their main drawback is that they are limited to detection only, even though (Saisi et al 2015;Ubertini et al 2018;Venanzi et al 2020) providing a first clue on damage quantification, e.g.…”
Section: Introductionmentioning
confidence: 99%