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Masonry Compressive Strength Prediction Using Artificial Neural Networks

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Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage (TMM_CH 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 962))

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.

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Authors and Affiliations

  1. Computational Mechanics Laboratory, School of Pedagogical and Technological Education, Heraklion, Athens, Greece

    Panagiotis G. Asteris & Ioannis Argyropoulos

  2. Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM), University of Palermo, Palermo, Italy

    Liborio Cavaleri

  3. RISCO, Department of Civil Engineering, Polytechnic Institute of Leiria, Leiria, Portugal

    Hugo Rodrigues

  4. Civil Engineering Department, Faculty of Engineering of the University of Porto, Porto, Portugal

    Humberto Varum

  5. Department of Civil Engineering, Cochin University of Science and Technology, Cochin, Kerala, India

    Job Thomas

  6. Department of Civil Engineering, ISISE, University of Minho, Guimarães, Portugal

    Paulo B. Lourenço

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  1. Panagiotis G. Asteris
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  2. Ioannis Argyropoulos
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  3. Liborio Cavaleri
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  7. Paulo B. Lourenço
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Correspondence to Panagiotis G. Asteris .

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Editors and Affiliations

  1. National Technical University of Athens, Athens, Greece

    Antonia Moropoulou

  2. National Technical University of Athens, Athens, Greece

    Manolis Korres

  3. National Technical University of Athens, Athens, Greece

    Andreas Georgopoulos

  4. National Technical University of Athens, Athens, Greece

    Constantine Spyrakos

  5. National Technical University of Athens, Athens, Greece

    Charalambos Mouzakis

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Asteris, P.G. et al. (2019). Masonry Compressive Strength Prediction Using Artificial Neural Networks. In: Moropoulou, A., Korres, M., Georgopoulos, A., Spyrakos, C., Mouzakis, C. (eds) Transdisciplinary Multispectral Modeling and Cooperation for the Preservation of Cultural Heritage. TMM_CH 2018. Communications in Computer and Information Science, vol 962. Springer, Cham. https://doi.org/10.1007/978-3-030-12960-6_14

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  • DOI: https://doi.org/10.1007/978-3-030-12960-6_14

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