Neural Network-Based Prediction of Dynamic Properties

Dae-Hong Min; YoungSeok Kim; Sewon Kim; Hyun-Jun Choi; Hyung-Koo Yoon

doi:10.7843/kgs.2023.39.12.37

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2023 Vol.39, Issue 12 Preview Page Next Page

Neural Network-Based Prediction of Dynamic Properties 인공신경망을 활용한 동적 물성치 산정 연구

31 December 2023. pp. 37-46

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Abstract

Dynamic soil properties are essential factors for predicting the detailed behavior of the ground. However, there are limitations to gathering soil samples and performing additional experiments. In this study, we used an artificial neural network (ANN) to predict dynamic soil properties based on static soil properties. The selected static soil properties were soil cohesion, internal friction angle, porosity, specific gravity, and uniaxial compressive strength, whereas the compressional and shear wave velocities were determined for the dynamic soil properties. The Levenberg–Marquardt and Bayesian regularization methods were used to enhance the reliability of the ANN results, and the reliability associated with each optimization method was compared. The accuracy of the ANN model was represented by the coefficient of determination, which was greater than 0.9 in the training and testing phases, indicating that the proposed ANN model exhibits high reliability. Further, the reliability of the output values was verified with new input data, and the results showed high accuracy.

Keywords

Artificial neural network

Dynamic soil properties

Hyperparameter

Optimization algorithms

Static soil properties

동적 물성치는 지반의 상세한 거동을 예측하기 위한 필수인자이나, 샘플 채취와 추가적인 실험이 동반되는 한계가 있다. 본 연구의 목적은 정적 지반 물성치를 기반으로 동적 지반 물성치를 예측하는 것으로 인공신경망을 활용하고자 하였다. 정적 물성치는 점착력, 내부마찰각, 함수비, 비중 그리고 일축압축강도로 선정하였으며 출력 값인 동적물성치는 압축파 속도와 전단파 속도로 결정하였다. 인공신경망 적용시 결과값의 신뢰성을 높이기 위해 Levenberg-Marquardt와 Bayesian regularization 방법을 적용하였으며, 각 최적화 방법에 따른 신뢰성을 비교하였다. 인공신경망 모델의 정확도는 결정계수로 나타냈으며, train과 test 과정 모두 0.9 이상의 값을 보여 해당 연구에서 구축한 인공신경망의 신뢰성이 높은 것으로 나타났다. 또한, 구축된 인공신경망 모델의 검증을 위해 새로운 입력 데이터에 대해서도 출력 값의 신뢰성을 검증하였으며, 그 결과 높은 정확도를 보였다.

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Information

Publisher :The Korean Geotechnical Society
Publisher(Ko) :한국지반공학회
Journal Title :Journal of the Korean Geotechnical Society
Journal Title(Ko) :한국지반공학회 논문집
Volume : 39
No :12
Pages :37-46
Received Date : 2023-10-13
Revised Date : 2023-10-25
Accepted Date : 2023-10-26
DOI :https://doi.org/10.7843/kgs.2023.39.12.37

Journal of the Korean Geotechnical Society ISSN:1229-2427(Print) 2288-646X(Online) 한국지반공학회 논문집

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