Hybrid ECBO–ANN Algorithm for Shear Strength of Partially Grouted Masonry Walls

• Published in: Periodica polytechnica. Civil engineering. Bauingenieurwesen Vol. 67; no. 4; p. 1176
• Main Authors: Kaveh, Ali, Khavaninzadeh, Neda
• Format: Journal Article
• Language: English
• Published: Budapest Periodica Polytechnica, Budapest University of Technology and Economics 01.10.2023
• Subjects: Algorithms; Artificial neural networks; Correlation coefficient; Correlation coefficients; Heuristic methods; Machine learning; Masonry; Masonry materials; Mathematical models; Mean square errors; Mortars (material); Neural networks; Optimization; Parameters; Problem solving; Reinforcing steels; Shear strength; Shear walls
• ISSN: 0553-6626; 1587-3773; 1587-3773
• DOI: 10.3311/PPci.22653

In recent years, artificial neural network (ANN) has become one of the popular and effective machine learning models, having a unique ability to handle very complex problems and the potential to predict accurate results without a defined algorithmic solution. However, the ANN structure and parameters are usually chosen by experience.The behavior of Partially Grouted (PG) masonry shear walls is complex due to the inherent anisotropic properties of the masonry materials and the nonlinear interactions between mortar, blocks, grouted cells, non-grouted cells, and reinforcing steel.In this study, the aim is to develop an artificial neural network model by combining the ECBO meta-heuristic algorithm with the artificial neural network structure to optimize the feed forward propagation network parameters for analyzing the shear strength of PG walls.A total of 255 test data on PG collected from the available literature were used to generate training and test data sets. Various validation criteria such as mean square error, root mean square error and correlation coefficient (R) are used to validate the models.In this study, the optimal number of neurons used in the hidden layer and also the optimal number of CBs required in the ECBO algorithm were obtained. The mathematical formulation of the optimized neural network model with the combination of meta-heuristic algorithm is also presented.