Title:
Prediction of Tensile Properties of Ultra-High-Performance Concrete Using Artificial Neural Network
Author(s):
Amjad Y. Diab and Anca C. Ferche
Publication:
Structural Journal
Volume:
121
Issue:
2
Appears on pages(s):
57-69
Keywords:
artificial neural network (ANN); cracking stress; machine learning; multilayer perceptron (MLP); tensile strength; ultra-high-performance concrete (UHPC)
DOI:
10.14359/51740245
Date:
3/1/2024
Abstract:
A multilayer perceptron artificial neural network (MLP-ANN)
was developed to calculate the cracking stress, tensile strength,
and strain at tensile strength of ultra-high-performance concrete
(UHPC), using the mixture design parameters and strain rate
during testing as inputs. This tool is envisioned to provide reference
values for direct tension test results performed on UHPC specimens,
or to be employed as a framework to determine the tension
response characteristics of UHPC in the absence of experimental
testing, with minimal computational effort to determine the tensile
characteristics. A database of 470 data points was compiled from
19 different experimental programs with the direct tensile strength,
cracking stress, and strain at tensile strength corresponding to
different UHPC mixtures. The model was trained, and its accuracy
was tested using this database. A reasonably good performance
was achieved with the coefficients of determination, R2, of 0.91,
0.81, and 0.92 for the tensile strength, cracking stress, and strain at
tensile strength, respectively. The results showed an increase in the
cracking tensile stress and tensile strength for higher strain rates,
whereas the strain at tensile strength was unaffected by the strain
rate.
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