Title:
Artificial Neural Network Model for Concrete Strength Predictions Based on Ultrasonic Pulse Velocity Measurement
Author(s):
Fayez Moutassem and Mohamad Kharseh
Publication:
Materials Journal
Volume:
121
Issue:
4
Appears on pages(s):
61-68
Keywords:
artificial neural network (ANN); compressive strength model; machine learning; modeling; ultrasonic pulse velocity (UPV)
DOI:
10.14359/51740776
Date:
8/1/2024
Abstract:
Accurately predicting the compressive strength of concrete is
crucial in various fields, including construction and engineering.
This research paper proposes two mathematical models based
on nonlinear regression and artificial neural networks (ANNs) to
predict the compressive strength of concrete accurately based on
ultrasonic pulse velocity (UPV) measurements. This paper outlines
the proposed models’ formulation, calibration, evaluation, and
validation. An experimental program was designed to calibrate
and evaluate the models, and the analysis of the results reveals
the robust fit of the proposed models to the experimental data.
Both models exhibit exceptional accuracy, effectively predicting
compressive strength values. The ANN and nonlinear regression
models attained high coefficients of determination of 0.993 and
0.992, respectively, demonstrating their reliability. Additionally,
the standard errors of the ANN and nonlinear regression models
are 2.41 and 2.52 MPa, respectively. Practical applications of
these models extend to concrete characterization, enabling efficient
quality control and structural integrity assessment.
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