Masonry compressive strength prediction using artificial neural networks

Liborio Cavaleri, Hugo Rodrigues, Humberto Varum, Job Thomas, Ioannis Argyropoulos, Paulo B. Lourenço, Panagiotis G. Asteris

Risultato della ricerca: Conference contribution

9 Citazioni (Scopus)

Abstract

The masonry is not only included among the oldest building materials, but it is also the most widely used material due to its simple construction and low cost compared to the other modern building materials. Nevertheless, there is not yet a robust quantitative method, available in the literature, which can reliably predict its strength, based on the geometrical and mechanical characteristics of its components. This limitation is due to the highly nonlinear relation between the compressive strength of masonry and the geometrical and mechanical properties of the components of the masonry. In this paper, the application of artificial neural networks for predicting the compressive strength of masonry has been investigated. Specifically, back-propagation neural network models have been used for predicting the compressive strength of masonry prism based on experimental data available in the literature. The comparison of the derived results with the experimental findings demonstrates the ability of artificial neural networks to approximate the compressive strength of masonry walls in a reliable and robust manner.
Lingua originaleEnglish
Titolo della pubblicazione ospite1st International Conference on Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage, TMM_CH 2018; Athens; Greece; 10 October 2018 through 13 October 2018
Pagine200-224
Numero di pagine25
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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    Cavaleri, L., Rodrigues, H., Varum, H., Thomas, J., Argyropoulos, I., Lourenço, P. B., & Asteris, P. G. (2019). Masonry compressive strength prediction using artificial neural networks. In 1st International Conference on Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage, TMM_CH 2018; Athens; Greece; 10 October 2018 through 13 October 2018 (pagg. 200-224)