Evaluating the Behaviour of Centrally Perforated Unreinforced Masonry Walls: Applications of Numerical Analysis, Machine Learning, and Stochastic Methods

Authors

Mohsen Khaleghi, Islamic Azad University, Kermanshah Branch
Javid Salimi, University of Tehran
Visar Farhangi, University of Nevada, Las Vegas
Mohammad Javad Moradi, Carleton University
Moses Karakouzian, University of Nevada, Las VegasFollow

Document Type

Article

Publication Date

5-1-2022

Publication Title

Ain Shams Engineering Journal

Volume

13

Issue

3

First page number:

1

Last page number:

20

Abstract

The presence of openings greatly affects the response of unreinforced masonry (URM) walls. This topic greatly attracts the attention of many researchers. Perforated unreinforced masonry (PURM) walls under in-plane loads through the truss discretization method (TDM) along with several machine learning approaches such as Multilayer perceptron (MLP), Group of Method Data Handling (GMDH), and Radial basis function (RBF) are described in this paper. A new method named Multi-pier (MP) that is fast and accurate, is used to determine the behavior of PURM walls. The results of the MP method are expressed as a ratio of lateral load-bearing capacity and initial stiffness of PURM walls to the solid wall in order to generalize the obtained results to other PURM walls. The outcomes of the MP method are employed to predict the behavior of PURM walls using various machine learning approaches. Using the validated network with suitable accuracy, empirical functions and curves are presented in an effort to provide a simplified and practical approach to assess the reduction in the load-bearing capacity and initial stiffness of PURM walls. Results indicate that the adjacent piers of opening have a remarkable impact on the overall response of the PURM wall. Finally, the ability of the MP method to conduct stochastic analysis is evaluated. Moreover, the effect of randomness in the mechanical characteristics and their spatial variation within the PURM wall is presented.

Keywords

Machine learning; Multi-Pier method; Opening; Perforated masonry wall; Random field

Disciplines

Numerical Analysis and Computation

File Format

pdf

File Size

7807 KB

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Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Repository Citation

Khaleghi, M., Salimi, J., Farhangi, V., Moradi, M., Karakouzian, M. (2022). Evaluating the Behaviour of Centrally Perforated Unreinforced Masonry Walls: Applications of Numerical Analysis, Machine Learning, and Stochastic Methods. Ain Shams Engineering Journal, 13(3), 1-20.
http://dx.doi.org/10.1016/j.asej.2021.10.026