Modelling of lateral effective stress using the particle swarm optimization with machine learning models

UNCUOĞLU E., LATİFOĞLU L., Ozer A. T.

ARABIAN JOURNAL OF GEOSCIENCES, cilt.14, sa.22, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 22
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s12517-021-08686-9
  • Dergi Adı: ARABIAN JOURNAL OF GEOSCIENCES
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aquatic Science & Fisheries Abstracts (ASFA), Geobase, INSPEC
  • Anahtar Kelimeler: Sand, Lateral effective stress, PSO-ANN, PSO-SVR, PSO-RF, Internal friction angle, REST EARTH PRESSURE, COEFFICIENT, PREDICTION, PILES, SOIL, K-0
  • Erciyes Üniversitesi Adresli: Evet

Özet

Predicting the lateral effective stress and the coefficient of lateral earth pressure at rest has a major importance in the design and analysis of many geotechnical problems. The purpose of this study is to predict the lateral effective stress without needing any experimental study or in-situ testing effort, by using the physical properties of sand which can be easily quantified in the laboratory. Therefore, the lateral effective stress values, sigma GREEK TONOSh, were estimated by using particle swarm optimization-artificial neural network (PSO-ANN), particle swarm optimization-support vector regression (PSO-SVR) and particle swarm optimization-random forest (PSO-RF) approaches. The internal friction angles were back-calculated using the Jaky's formula utilizing the output of the PSO-ANN model were compared to that of measured experimentally in the laboratory. Thus, both the reliability of the model and the potential of Jaky's formula in predicting the K-0 coefficient were evaluated. The PSO-ANN model found out to be an effective tool to estimate accurately sigma GREEK TONOSh in cohesionless soils. It is clearly seen that the predictive performance of the PSO-ANN model was better than that of the both PSO-SVR and PSO-RF models.