2019
DOI: 10.31462/jseam.2019.01194198
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Prediction and verification of compressive strength of historical masonry structures with artificial neural network

Abstract: Compressive strength and modulus of elasticity are the key material properties of historical structures to adapt finite element model to obtain correct structural assessment. Generally, modulus of elasticity is adapted by multiplying compressive strength of the material. In this paper, it is aimed to predict compressive strength of historical masonry buildings by using material characteristics and physical characteristics. For this purpose, 21 historical masonry mosques, churches and cathedrals were selected. … Show more

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Cited by 2 publications
(1 citation statement)
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“…It is also important to check the obtained natural frequency according to some empirical formulations related to the masonry type bridges. The study performed by Onat [24] represents different empirical formulations including some parameters such as unit weight, elasticity modulus of masonry materials, length, height, width and main span length of the masonry bridge. It can be understood that the empirical formulation consisting of both mechanical and physical quantities gives a more reliable result when compared to those with the natural frequencies obtained experimentally.…”
Section: Experimental Modal Identification Of Sarpdere Bridgementioning
confidence: 99%
“…It is also important to check the obtained natural frequency according to some empirical formulations related to the masonry type bridges. The study performed by Onat [24] represents different empirical formulations including some parameters such as unit weight, elasticity modulus of masonry materials, length, height, width and main span length of the masonry bridge. It can be understood that the empirical formulation consisting of both mechanical and physical quantities gives a more reliable result when compared to those with the natural frequencies obtained experimentally.…”
Section: Experimental Modal Identification Of Sarpdere Bridgementioning
confidence: 99%