Compressive Strength Prediction of Self-Compacting Concrete-A Bat Optimization Algorithm Based ANNs

This article examines the feasibility of using bat-trained artificial neural networks (ANNs) to predict the compressive strength of self-compacting concrete (SCC). The nonlinear behavior of SCC challenges traditional modeling techniques. Therefore, this work takes advantage of the superior predictiv...

Full description

Saved in:
Bibliographic Details
Published in:Advances in materials science and engineering 2022, Vol.2022, p.1-12
Main Authors: Andalib, Amir, Aminnejad, Babak, Lork, Alireza
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Compressive strength
Decision trees
Machine learning
Mechanical properties
Neural networks
Neurons
Optimization algorithms
Particle swarm optimization
Pattern recognition
Performance prediction
Regression analysis
Reinforced concrete
Rheology
Self-compacting concrete
Sensitivity analysis
Shear strength
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article examines the feasibility of using bat-trained artificial neural networks (ANNs) to predict the compressive strength of self-compacting concrete (SCC). The nonlinear behavior of SCC challenges traditional modeling techniques. Therefore, this work takes advantage of the superior predictive performance of ANNs coupled with the bat algorithm. A database of 205 SCC samples collected from the literature is used to develop the ANN model. The correctness of the bat-based neural network model is then substantiated by contrasting its performance with that of the particle swarm optimization and teaching-learning-based optimization algorithms employed to train a neural network model. The statistical indices indicate the superior performance of the bat-based ANN model. In addition, a sensitivity analysis was carried out to determine the effects of various input parameters on the compressive strength of SCC.
ISSN:1687-8434
1687-8442
DOI:10.1155/2022/8404774