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Applied Sciences
Volume 10
Issue 22
10.3390/app10228255
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Article

A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks

by
Ala Hijazi
1,*,
Sameer Al-Dahidi
1 and
Safwan Altarazi
2
1
Department of Mechanical Engineering, German Jordanian University, Amman 11180, Jordan
2
Department of Industrial Engineering, German Jordanian University, Amman 11180, Jordan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(22), 8255; https://doi.org/10.3390/app10228255
Received: 5 October 2020 / Revised: 13 November 2020 / Accepted: 19 November 2020 / Published: 20 November 2020
(This article belongs to the Section Mechanical Engineering)
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Abstract

An artificial neural network (ANN) extracts knowledge from a training dataset and uses this acquired knowledge to forecast outputs for any new set of inputs. When the input/output relations are complex and highly non-linear, the ANN needs a relatively large training dataset (hundreds of data points) to capture these relations adequately. This paper introduces a novel assisted-ANN modeling approach that enables the development of ANNs using small datasets, while maintaining high prediction accuracy. This approach uses parameters that are obtained using the known input/output relations (partial or full relations). These so called assistance parameters are included as ANN inputs in addition to the traditional direct independent inputs. The proposed assisted approach is applied for predicting the residual strength of panels with multiple site damage (MSD) cracks. Different assistance levels (four levels) and different training dataset sizes (from 75 down to 22 data points) are investigated, and the results are compared to the traditional approach. The results show that the assisted approach helps in achieving high predictions’ accuracy (<3% average error). The relative accuracy improvement is higher (up to 46%) for ANN learning algorithms that give lower prediction accuracy. Also, the relative accuracy improvement becomes more significant (up to 38%) for smaller dataset sizes. View Full-Text
Keywords: artificial neural networks; small datasets; assisted-ANN; hybrid-ANN; ANN inputs; fracture mechanics; residual strength; multiple site damage cracks artificial neural networks; small datasets; assisted-ANN; hybrid-ANN; ANN inputs; fracture mechanics; residual strength; multiple site damage cracks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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MDPI and ACS Style

Hijazi, A.; Al-Dahidi, S.; Altarazi, S. A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks. Appl. Sci. 2020, 10, 8255. https://doi.org/10.3390/app10228255

AMA Style

Hijazi A, Al-Dahidi S, Altarazi S. A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks. Applied Sciences. 2020; 10(22):8255. https://doi.org/10.3390/app10228255

Chicago/Turabian Style

Hijazi, Ala, Sameer Al-Dahidi, and Safwan Altarazi. 2020. "A Novel Assisted Artificial Neural Network Modeling Approach for Improved Accuracy Using Small Datasets: Application in Residual Strength Evaluation of Panels with Multiple Site Damage Cracks" Applied Sciences 10, no. 22: 8255. https://doi.org/10.3390/app10228255

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