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Geomechanics and Engineering
  Volume 37, Number 3, May10 2024 , pages 263-277
DOI: https://doi.org/10.12989/gae.2024.37.3.263
 


Optimizing artificial neural network architectures for enhanced soil type classification
Yaren Aydin, Gebrail Bekdaş, Ümit Işikdağ, Sinan Melih Nigdeli and Zong Woo Geem

 
Abstract
    Artificial Neural Networks (ANNs) are artificial learning algorithms that provide successful results in solving many machine learning problems such as classification, prediction, object detection, object segmentation, image and video classification. There is an increasing number of studies that use ANNs as a prediction tool in soil classification. The aim of this research was to understand the role of hyperparameter optimization in enhancing the accuracy of ANNs for soil type classification. The research results has shown that the hyperparameter optimization and hyperparamter optimized ANNs can be utilized as an efficient mechanism for increasing the estimation accuracy for this problem. It is observed that the developed hyperparameter tool (HyperNetExplorer) that is utilizing the Covariance Matrix Adaptation Evolution Strategy (CMAES), Genetic Algorithm (GA) and Jaya Algorithm (JA) optimization techniques can be successfully used for the discovery of hyperparameter optimized ANNs, which can accomplish soil classification with 100% accuracy.
 
Key Words
    artificial neural networks; bio-inspired methods; hyperparameter optimization; soil classification
 
Address
Yaren Aydin, Gebrail Bekdaş and Sinan Melih Nigdeli: Department of Civil Engineering, Istanbul University-Cerrahpaşa, 34320 Istanbul, Turkey
Ümit Işikdağ: Department of Informatics, Mimar Sinan Fine Arts University, Istanbul 34427, Turkey
Zong Woo Geem: College of IT Convergence, Gachon University, Seongnam 13120, Korea
 

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