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Morteza Ghorbanzadeh Ahangari

Morteza Ghorbanzadeh Ahangari

Academic rank: Associate Professor
ORCID:
Education: PhD.
ScopusId:
Faculty: Faculty of Technology and Engineering
Address:
Phone: 35305107

Research

Title
A material-independent deep learning model to predict the tensile strength of polymer concrete
Type
JournalPaper
Keywords
Polymer concrete Deep learning Tensile strength Partial dependence plot (PDP)
Year
2022
Journal COMPOSITES SCIENCE AND TECHNOLOGY
DOI
Researchers Mostafa Hassani Niaki ، Morteza Ghorbanzadeh Ahangari ، Matin Pashaian

Abstract

This study intends to predict the tensile strength of polymer concrete (PC) composites using one of the deep learning-based methods called deep neural network (DNN). A database including 9 variables and the corresponding tensile strength, which consists of 281 experimental data from 22 previous works is prepared. These variables are the weight percentage of materials and also dimensions of fillers, which are used as input variables of the DNN system, and the corresponding tensile strength is the output. The performance of the developed DNN model is evaluated using statistical criteria R, R2, MSE, RMSE, and MAE. Afterward, the sensitivity of the tensile strength of the PC to each input variable is investigated using a partial dependence plot (PDP) analysis within the obtained DNN model. The positive and negative influence of each input variable on the tensile strength can be implemented in the optimized design of the composition of the PC.