RESEARCH ARTICLE


Non-destructive Method of the Assessment of Stone Masonry by Artificial Neural Networks



Rachel Martini1, *, Jorge Carvalho2, António Arêde3, Humberto Varum3
1 Department of Civil Engineering and Environment, Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Campus Curvelo, Minas Gerais, Brazil
2 Department of Mining Engineering, CERENA - Faculty of Engineering (FEUP), University of Porto, Lisbon, Portugal
3 Department of Civil Engineering, CONSTRUCT-LESE - Faculty of Engineering (FEUP), University of Porto, Lisbon, Portugal


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Creative Commons License
© 2020 Martini et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Civil Engineering and environment, Centro Federal de Educação Tecnológica de Minas Gerais (CEFET-MG), Campus Curvelo, Brazil; Tel: +55 (38) 3729-3900; E-mail: racheljmartini@gmail.com


Abstract

Background:

In this study , a methodology based on non-destructive tests was used to characterize historical masonry and later to obtain information regarding the mechanical parameters of these elements. Due to the historical and cultural value that these buildings represent, the maintenance and rehabilitation work are important to maintain the appreciation of history. The preservation of buildings classified as historical-cultural heritage is of social interest, since they are important to the history of society. Considering the research object as a historical building, it is not recommended to use destructive investigative techniques.

Objective:

This work contributes to the technical-scientific knowledge regarding the characterization of granite masonry based on geophysical, mechanical and neural networks techniques.

Methods:

The database was built using the GPR (Ground Penetrating Radar) method, sonic and dynamic tests, for the characterization of eight stone masonry walls constructed in a controlled environment. The mechanical characterization was performed with conventional tests of resistance to uniaxial compression, and the elastic modulus was the parameter used as output data of ANNs.

Results:

For the construction and selection of network architecture, some possible combinations of input data were defined, with variations in the number of hidden layer neurons (5, 10, 15, 20, 25 and 30 nodes), with 122 trained networks.

Conclusion:

A mechanical characterization tool was developed applying the Artificial Neural Networks (ANN), which may be used in historic granite walls. From all the trained ANNs, based on the errors attributed to the estimated elastic modulus, networks with acceptable errors were selected.

Keywords: Sonic test, Dynamic test, GPR, Compression test and elastic modulus, Mechanical parameters, Non-destructive testing techniques.