Geomechanics and Engineering

  • 제37권3호
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  • Pages.263-277
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  • 2024
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  • 2005-307X(pISSN)
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  • 2092-6219(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Optimizing artificial neural network architectures for enhanced soil type classification

  • Yaren Aydin (Department of Civil Engineering, Istanbul University-Cerrahpasa) ;
  • Gebrail Bekdas (Department of Civil Engineering, Istanbul University-Cerrahpasa) ;
  • Umit Isikdag (Department of Informatics, Mimar Sinan Fine Arts University) ;
  • Sinan Melih Nigdeli (Department of Civil Engineering, Istanbul University-Cerrahpasa) ;
  • Zong Woo Geem (College of IT Convergence, Gachon University)
  • 투고 : 2024.01.21
  • 심사 : 2024.04.22
  • 발행 : 2024.05.10
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

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