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Alberto Barontini

Researcher at University of Minho

Publications -  21
Citations -  104

Alberto Barontini is an academic researcher from University of Minho. The author has contributed to research in topics: Masonry & Geology. The author has an hindex of 4, co-authored 12 publications receiving 50 citations.

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An overview on nature-inspired optimization algorithms for Structural Health Monitoring of historical buildings

TL;DR: Possible improvements in built heritage conservation are shown and discussed suggesting a general framework for safety assessment and damage identification of existing structures in Structural Health Monitoring.
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Dynamic characterization of progressively damaged segmental masonry arches with one settled support: experimental and numerical analyses

TL;DR: In this paper, the authors explored the dynamic behavior of a segmental masonry arch subjected to increasing horizontal displacements of one support and used output-only dynamic identification techniques to track the evolution of the dynamic features of the system under progressive damage scenarios.
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Development and Demonstration of an HBIM Framework for the Preventive Conservation of Cultural Heritage

TL;DR: Building Information Modelling (BIM) methodology is becoming widespread with many potential uses, such as facility and asset management for new buildings as discussed by the authors, and it has also been applied for the...
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Seismic Safety Assessment of Mixed Timber-Masonry Historical Building: An Example in Lima, Peru

TL;DR: The Hotel El Comercio as mentioned in this paper is a typical colonial-era house in Peru that exhibits specific architectural details and construction techniques, including adobe walls and mixed timber-ear...
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Deterministically generated negative selection algorithm for damage detection in civil engineering systems

TL;DR: A negative-selection algorithm with a non-random strategy for detector generation is developed and tested and proves that the algorithm is suitable for the purpose of damage detection and provides a sound analysis of the method multiclass classification skills, aiming at the quantification of the damage.