Masonry Compressive Strength Prediction Using Artificial Neural Networks

  • Panagiotis G. AsterisEmail author
  • Ioannis Argyropoulos
  • Liborio Cavaleri
  • Hugo Rodrigues
  • Humberto Varum
  • Job Thomas
  • Paulo B. Lourenço
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 962)


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.


Artificial Neural Networks (ANNs) Back-Propagation Neural Networks (BPNNs) Building materials Compressive strength Masonry Masonry unit Mortar Soft-computing techniques 


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Authors and Affiliations

  • Panagiotis G. Asteris
    • 1
    Email author
  • Ioannis Argyropoulos
    • 1
  • Liborio Cavaleri
    • 2
  • Hugo Rodrigues
    • 3
  • Humberto Varum
    • 4
  • Job Thomas
    • 5
  • Paulo B. Lourenço
    • 6
  1. 1.Computational Mechanics LaboratorySchool of Pedagogical and Technological EducationHeraklion, AthensGreece
  2. 2.Department of Civil, Environmental, Aerospace and Materials Engineering (DICAM)University of PalermoPalermoItaly
  3. 3.RISCO, Department of Civil EngineeringPolytechnic Institute of LeiriaLeiriaPortugal
  4. 4.Civil Engineering DepartmentFaculty of Engineering of the University of PortoPortoPortugal
  5. 5.Department of Civil EngineeringCochin University of Science and TechnologyCochinIndia
  6. 6.Department of Civil Engineering, ISISEUniversity of MinhoGuimarãesPortugal

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