Predicting the compressive strength of unreinforced brick masonry using machine learning techniques validated on a case study of a museum through nondestructive testing
Author:
Publisher
Springer Science and Business Media LLC
Subject
Safety, Risk, Reliability and Quality,Civil and Structural Engineering
Link
http://link.springer.com/content/pdf/10.1007/s13349-020-00391-7.pdf
Reference96 articles.
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4. Betti M, Borghini A, Boschi S, Ciavattone A, Vignoli A (2018) Comparative seismic risk assessment of basilica-type churches. J Earthq Eng 22(sup1):62–95
5. Clementi F, Gazzani V, Poiani M, Lenci S (2016) Assessment of seismic behaviour of heritage masonry buildings using numerical modelling. J Build Eng 8:29–47 (ISSN 2352-7102)
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